Paper-Reading

Paper reading list in natural language processing (NLP), with special emphasis on dialogue systems and natural language generation. This repo will keep updating 🤗 ...

• Deep Learning in NLP
• Pre-trained Language Models
• Knowledge Representation Learning
• Dialogue System
  • Survey
  • LLMs for Dialogue
  • Personalized Dialogue
  • Target-oriented Dialogue
  • Recommendation Dialogue and CRS
  • Knowledge-grounded Dialogue
  • Emotion-aware Dialogue
  • Task-oriented Dialogue
  • Open-domain Dialogue
  • Dialogue Evaluation
• Natural Language Generation
  • Survey of NLG
  • Text Planning
  • Controllable Generation
  • Diffusion Models for Generation
  • Generation (Theories and Techniques)
  • Decoding Algorithm

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Deep Learning in NLP

• Data Augmentation: "A Survey of Data Augmentation Approaches for NLP". ACL-Findings(2021) [PDF]
• Survey of Transformers: "A Survey of Transformers". arXiv(2021) [PDF]
• Graphormer: "Do Transformers Really Perform Bad for Graph Representation?". NeurIPS(2021) [PDF] [code]
• GAT: "Graph Attention Networks". ICLR(2018) [PDF] [code-tf] [code-py]
• Transformer-XL: "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context". ACL(2019) [PDF] [code]
• Transformer: "Attention is All you Need". NeurIPS(2017) [PDF] [code-official] [code-tf] [code-py]
• VAE: "An Introduction to Variational Autoencoders". arXiv(2019) [PDF]
• ConvS2S: "Convolutional Sequence to Sequence Learning". ICML(2017) [PDF]
• Survey of Attention: "An Introductory Survey on Attention Mechanisms in NLP Problems". arXiv(2018) [PDF] ⭐⭐⭐⭐⭐
• Additive Attention: "Neural Machine Translation by Jointly Learning to Align and Translate". ICLR(2015) [PDF]
• Multiplicative Attention: "Effective Approaches to Attention-based Neural Machine Translation". EMNLP(2015) [PDF]
• Memory Net: "End-To-End Memory Networks". NeurIPS(2015) [PDF]
• Copy Mechanism (PGN): "Get To The Point: Summarization with Pointer-Generator Networks". ACL(2017) [PDF] [code] ⭐⭐⭐⭐⭐
• Copy Mechanism: "Incorporating Copying Mechanism in Sequence-to-Sequence Learning". ACL(2016) [PDF]
• Coverage Mechanism: "Modeling Coverage for Neural Machine Translation". ACL(2016) [PDF]
• ELMo: "Deep contextualized word representations". NAACL(2018) [PDF] [code]
• Glove: "GloVe: Global Vectors for Word Representation". EMNLP(2014) [PDF] [code]
• word2vec: "word2vec Parameter Learning Explained". arXiv(2016) [PDF] ⭐⭐⭐⭐⭐
• SeqGAN: "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient". AAAI(2017) [PDF] [code]
• GAN: "Generative Adversarial Nets". NeurIPS(2014) [PDF]
• Multi-task Learning: "An Overview of Multi-Task Learning in Deep Neural Networks". arXiv(2017) [PDF]
• Gradient Descent: "An Overview of Gradient Descent Optimization Algorithms". arXiv(2016) [PDF] ⭐⭐⭐⭐⭐

Pre-trained Language Models

• Survey of PLMs: "Pre-Trained Models: Past, Present and Future". arXiv(2021) [PDF]
• Survey of PLMs: "Pre-trained Models for Natural Language Processing: A Survey". arXiv(2020) [PDF]
• CPT: "CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation". arXiv(2021) [PDF] [code] ⭐⭐⭐
• GLM: "All NLP Tasks Are Generation Tasks: A General Pretraining Framework". arXiv(2021) [PDF] [code]
• GPT-3: "Language Models are Few-Shot Learners". arXiv(2020) [PDF] ⭐⭐⭐⭐
• BART: "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension". ACL(2020) [PDF] [code] ⭐⭐⭐
• T5: "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer". JMLR(2020) [PDF] [code-tf] [code-py] ⭐⭐⭐
• MASS: "MASS: Masked Sequence to Sequence Pre-training for Language Generation". ICML(2019) [PDF] [code]
• ALBERT: "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations". ICLR(2020) [PDF]
• TinyBERT: "TinyBERT: Distilling BERT for Natural Language Understanding". arXiv(2019) [PDF] [code]
• Chinese BERT: "Pre-Training with Whole Word Masking for Chinese BERT". arXiv(2019) [PDF] [code]
• SpanBERT: "SpanBERT: Improving Pre-training by Representing and Predicting Spans". TACL(2020) [PDF] [code]
• RoBERTa: "RoBERTa: A Robustly Optimized BERT Pretraining Approach". arXiv(2019) [PDF] [code]
• UniLM: "Unified Language Model Pre-training for Natural Language Understanding and Generation". NeurIPS(2019) [PDF] [code] ⭐⭐⭐⭐
• XLNet: "XLNet: Generalized Autoregressive Pretraining for Language Understanding". NeurIPS(2019) [PDF] [code]
• XLM: "Cross-lingual Language Model Pretraining". NeurIPS(2019) [PDF] [code]
• GPT-2: "Language Models are Unsupervised Multitask Learners". OpenAI(2019) [PDF] [code]
• BERT: "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding". NAACL(2019) [PDF] [code] ⭐⭐⭐⭐⭐
• GPT: "Improving Language Understanding by Generative Pre-Training". OpenAI(2018) [PDF] ⭐⭐⭐⭐⭐

Knowledge Representation Learning

• DRAGON: "Deep Bidirectional Language-Knowledge Graph Pretraining". NeurIPS(2022) [PDF] [code]
• FKGE: "Differentially Private Federated Knowledge Graphs Embedding". CIKM(2021) [PDF] [code]
• FILM: "Adaptable and Interpretable Neural Memory Over Symbolic Knowledge". NAACL(2021) [PDF]
• ERICA: "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning". ACL(2021) [PDF] [code] ⭐⭐⭐
• K-Adapter: "K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters". ACL-Findings(2021) [PDF] [code]
• CoLAKE: "CoLAKE: Contextualized Language and Knowledge Embedding". COLING(2020) [PDF] [code]
• KEPLER: "KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation". TACL(2020) [PDF] [code]
• LUKE: "LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention". EMNLP(2020) [PDF] [code] ⭐⭐⭐
• GLM: "Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning". EMNLP(2020) [PDF] [code]
• GRF: "Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph". EMNLP(2020) [PDF] [code]
• K-BERT: "K-BERT: Enabling Language Representation with Knowledge Graph". AAAI(2020) [PDF] [code] ⭐⭐⭐
• LM-as-KG: "Language Models are Open Knowledge Graphs". arXiv(2020) [PDF]
• LAMA: "Language Models as Knowledge Bases?". EMNLP(2019) [PDF] [code] ⭐⭐⭐
• COMET: "COMET: Commonsense Transformers for Automatic Knowledge Graph Construction". ACL(2019) [PDF] [code] ⭐⭐⭐
• ATOMIC: "ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning". AAAI(2019) [PDF] [data]
• ERNIE(Tsinghua): "ERNIE: Enhanced Language Representation with Informative Entities". ACL(2019) [PDF] [code]
• ERNIE(Baidu): "ERNIE: Enhanced Representation through Knowledge Integration". arXiv(2019) [PDF] [code]

Dialogue System

Survey

• Survey of Dialogue: "Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey". arXiv(2021) [PDF] ⭐⭐⭐⭐
• Survey of Open-domain Dialogue: "Challenges in Building Intelligent Open-domain Dialog Systems". TOIS(2020) [PDF] ⭐⭐⭐⭐
• Survey of Dialogue: "A Survey on Dialogue Systems: Recent Advances and New Frontiers". SIGKDD Explorations(2017) [PDF]
• Survey of Corpora: "A Survey of Available Corpora For Building Data-Driven Dialogue Systems". arXiv(2017) [PDF] [data]

LLMs for Dialogue

• ChatGPT: "ChatGPT: Optimizing Language Models for Dialogue". OpenAI(2022) [Blog] ⭐⭐⭐⭐⭐
• Sparrow: "Improving alignment of dialogue agents via targeted human judgements". arXiv(2022) [PDF] [data]
• BlenderBot3: "BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage". arXiv(2022) [PDF]
• LaMDA: "LaMDA: Language Models for Dialog Applications". arXiv(2022) [PDF]
• GODEL: "GODEL: Large-Scale Pre-Training for Goal-Directed Dialog". arXiv(2022) [PDF] [code]
• Anthropic Assistant: "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback". arXiv(2022) [PDF]
• Anthropic Assistant: "A General Language Assistant as a Laboratory for Alignment". arXiv(2021) [PDF]

Personalized Dialogue

Character-based Dialogue

• KNUDGE: "Ontologically Faithful Generation of Non-Player Character Dialogues". arXic(2022) [PDF]
• HPD: "What would Harry say? Building Dialogue Agents for Characters in a Story". arXiv(2022) [PDF]
• DialStory: "A Benchmark for Understanding and Generating Dialogue between Characters in Stories". arXiv(2022) [PDF]
• RSODD: "Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models". NAACL(2022) [PDF] [data]
• PDP: "Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances". NAACL(2022) [PDF] [code]
• CharacterChat: "CharacterChat: Supporting the Creation of Fictional Characters through Conversation and Progressive Manifestation with a Chatbot". ACM C&C(2021）[PDF]
• ALOHA: "ALOHA: Artificial Learning of Human Attributes for Dialogue Agents". AAAI(2020) [PDF] [code] ⭐⭐⭐

Personality-aware Dialogue

• ChatGPT-MBTI: "Can ChatGPT Assess Human Personalities? A General Evaluation Framework". arXiv(2023) [PDF] [code]
• Prompted Personality: "Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning". IWSDS(2023) [PDF]
• CPED: "CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI". arXiv(2022) [PDF] [data] ⭐⭐⭐
• PELD: "Automatically Select Emotion for Response via Personality-affected Emotion Transition". ACL-Findings(2021) [PDF] [data]
• NegotiationToM: "Improving Dialog Systems for Negotiation with Personality Modeling". ACL(2021) [PDF] [code]
• FriendsPersona: "Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings". AAAI-Student Abstract(2020) [PDF] [data]
• APR: "Identifying Personality Traits Using Overlap Dynamics in Multiparty Dialogue". INTERSPEECH(2019) [PDF]
• PersonalDilaog: "Personalized Dialogue Generation with Diversified Traits". arXiv(2019) [PDF] [data]
• PersonageNLG: "Controlling Personality-Based Stylistic Variation with Neural Natural Language Generators". SIGDIAL(2018) [PDF] [data]

Persona-based Dialogue

• LMEDR: "Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues". AAAI(2023) [PDF] [code]
• Retrieval-to-Prediction: "Improving Personality Consistency in Conversation by Persona Extending". CIKM(2022) [PDF] [code]
• Implicit-Persona: "A Personalized Dialogue Generator with Implicit User Persona Detection". COLING(2022) [PDF]
• CareCallMemory: "Keep Me Updated! Memory Management in Long-term Conversations". EMNLP-Findings(2022) [PDF] [data]
• PersonaDefense: "You Don't Know My Favorite Color: Preventing Dialogue Representations from Revealing Speakers' Private Personas". NAACL(2022) [PDF] [code]
• Prompt-Tuning: "Building a Personalized Dialogue System with Prompt-Tuning". NAACL-SRW(2022) [PDF]
• DuLeMon: "Long Time No See! Open-Domain Conversation with Long-Term Persona Memory". ACL-Findings(2022) [PDF] [data] ⭐⭐⭐
• INFO: "You Truly Understand What I Need: Intellectual and Friendly Dialogue Agents grounding Knowledge and Persona". EMNLP-Findings(2022) [PDF] [code]
• FoCus: "Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge". AAAI(2022) [PDF] [code] ⭐⭐⭐
• MSP: "Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation". NAACL(2022) [PDF]
• GME: "Transferable Persona-Grounded Dialogues via Grounded Minimal Edits". EMNLP(2021) [PDF] [code]
• BoB: "BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data". ACL(2021) [PDF] [code]
• PABST: "Unsupervised Enrichment of Persona-grounded Dialog with Background Stories". ACL(2021) [PDF] [code]
• DHAP: "One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles". SIGIR(2021) [PDF]
• Pchatbot: "Pchatbot: A Large-Scale Dataset for Personalized Chatbot". SIGIR(2021) [PDF] [data] ⭐⭐⭐
• COMPAC: "Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions". EMNLP(2020) [PDF] [code]
• pragmatic-consistency: "Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness". EMNLP(2020) [PDF] [code] ⭐⭐⭐⭐
• XPersona: "XPersona: Evaluating Multilingual Personalized Chatbot". arXiv(2020) [PDF] [data]
• KvPI: "Profile Consistency Identification for Open-domain Dialogue Agents". EMNLP(2020) [PDF] [code]
• GDR: "Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation". ACL(2020) [PDF]
• P^2Bot: "You Impress Me: Dialogue Generation via Mutual Persona Perception". ACL(2020) [PDF] [code]
• RCDG: "Generating Persona Consistent Dialogues by Exploiting Natural Language Inference". AAAI(2020) [PDF] [code]
• Persona-sparse: "A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data". AAAI(2020) [PDF]
• PersonaWAE: "Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders". EMNLP(2019) [PDF]
• PAML: "Personalizing Dialogue Agents via Meta-Learning". ACL(2019) [PDF] [code]
• PersonaChat: "Personalizing Dialogue Agents: I have a dog, do you have pets too?" ACL(2018) [PDF] [data] ⭐⭐⭐
• PCCM: "Assigning Personality/Profile to a Chatting Machine for Coherent Conversation Generation". IJCAI(2018) [PDF]

Target-oriented Dialogue

• TopKG: "TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph". COLING(2022) [PDF] [code]
• TGCP: "Target-Guided Open-Domain Conversation Planning". COLING(2022) [PDF] [code]
• FOP: "Long-term Control for Dialogue Generation: Methods and Evaluation". NAACL(2022) [PDF] [code]
• CODA: "Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation". NAACL-Findings(2022) [PDF] [code]
• OTTers: "OTTers: One-turn Topic Transitions for Open-Domain Dialogue". ACL(2021) [PDF] [data]
• CG-nAR: "Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems". EMNLP(2021) [PDF] [code] ⭐⭐⭐
• DiSCoL: "DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation". NAACL(2021) [PDF] [code]
• DialoGraph: "DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues". ICLR(2021) [PDF] [code] ⭐⭐⭐
• FeHED: "Augmenting Non-Collaborative Dialog Systems with Explicit Semantic and Strategic Dialog History". ICLR(2020) [PDF] [code]
• TG-ReDial: "Towards Topic-Guided Conversational Recommender System". COLING(2020) [PDF] [code]
• CG-Policy: "Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation". ACL(2020) [PDF]
• CTX-PSA: "Learning to Plan and Realize Separately for Open-Ended Dialogue Systems". EMNLP-Findings(2020) [PDF]
• PersuasionForGood: "Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good". ACL(2019) [PDF] [data]
• DuConv: "Proactive Human-Machine Conversation with Explicit Conversation Goals". ACL(2019) [PDF] [code]
• CKC: "Keyword-Guided Neural Conversational Model". AAAI(2021) [PDF] [code]
• KnowHRL: "Knowledge Graph Grounded Goal Planning for Open-Domain Conversation Generation". AAAI(2020) [PDF]
• DKRN: "Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation". AAAI(2020) [PDF] [code]
• TGConv: "Target-Guided Open-Domain Conversation". ACL(2019) [PDF] [code]

Recommendation Dialogue and CRS

• KERS: "KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals". EMNLP-Findings(2021) [PDF] [code]
• DuRecDial2.0: "DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation". EMNLP(2021) [PDF] [code]
• DuRecDial: "Towards Conversational Recommendation over Multi-Type Dialogs". ACL(2020) [PDF] [code] ⭐⭐⭐⭐
• INSPIRED: "INSPIRED: Toward Sociable Recommendation Dialog Systems". EMNLP(2020) [PDF] [data]
• GoRecDial: "Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue". EMNLP(2019) [PDF] [code]
• CRS-Survey: "A Survey on Conversational Recommender Systems". ACM Computing Surveys(2021) [PDF]
• CRS-Survey: "Advances and Challenges in Conversational Recommender Systems: A Survey ". arXiv(2021) [PDF]
• CRSLab: "CRSLab: An Open-Source Toolkit for Building Conversational Recommender System". arXiv(2021) [PDF] [code] ⭐⭐⭐
• MESE: "Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information". NAACL(2022) [PDF] [code]
• C2-CRS: "C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System". WSDM(2022) [PDF] [code]
• BotPlay: "Self-Supervised Bot Play for Conversational Recommendation with Justifications". arXiv(2021) [PDF]
• RID: "Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph". arXiv(2021) [PDF] [code]
• CRFR: "CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs". EMNLP(2021) [PDF]
• NTRD: "Learning Neural Templates for Recommender Dialogue System". EMNLP(2021) [PDF] [code]
• CR-Walker: "CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation". EMNLP(2021) [PDF] [code] ⭐⭐⭐⭐
• RevCore: "RevCore: Review-augmented Conversational Recommendation". ACL-Findings(2021) [PDF] [code]
• KECRS: "KECRS: Towards Knowledge-Enriched Conversational Recommendation System". arXiv(2021) [PDF]
• FPAN: "Adapting User Preference to Online Feedback in Multi-round Conversational Recommendation". WSDM(2021) [PDF] [code]
• UNICORN: "Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning". SIGIR(2021) [PDF] [code]
• KGSF: "Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion". KDD(2020) [PDF] [code]
• CPR: "Interactive Path Reasoning on Graph for Conversational Recommendation". KDD(2020) [PDF] [code]
• EAR: "Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems". WSDM(2020) [PDF] [code]
• KBRD: "Towards Knowledge-Based Recommender Dialog System". EMNLP(2019) [PDF] [code]
• ReDial: "Towards Deep Conversational Recommendations". NeurIPS(2018) [PDF] [data]

Knowledge-grounded Dialogue

• GLM-Dialog: "GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation". arXiv(2023) [PDF] [code]
• MultiRefKGC: "There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning". EMNLP(2022) [PDF] [code] ⭐⭐⭐
• CorefDiffs: "CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations". COLING(2022) [PDF] [code]
• DTR: "Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting". NAACL(2022) [PDF] [code]
• XDAI: "XDAI: A Tuning-free Framework for Exploiting Pre-trained Language Models in Knowledge Grounded Dialogue Generation". KDD(2022) [PDF] [code]
• PersonaKGC: "There Are a Thousand Hamlets in a Thousand People's Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory". ACL(2022) [PDF] [code]
• KI: "Lexical Knowledge Internalization for Neural Dialog Generation". ACL(2022) [PDF] [code]
• DiffKG: "Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems". ACL-Findings(2022) [PDF] [code] ⭐⭐⭐
• KSAM: "KSAM: Infusing Multi-Source Knowledge into Dialogue Generation via Knowledge Source Aware Multi-Head Decoding". ACL-Findings(2022) [PDF]
• MDSP: "Multi-Stage Prompting for Knowledgeable Dialogue Generation". ACL-Findings(2022) [PDF] [code]
• FSB: "Few-Shot Bot: Prompt-Based Learning for Dialogue Systems". arXiv(2021) [PDF] [code] ⭐⭐⭐
• P-GDG: "Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation". arXiv(2021) [PDF]
• KAT-TSLF: "A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation". EMNLP(2021) [PDF] [code]
• DIALKI: "DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization". EMNLP(2021) [PDF] [code]
• CoLV: "CoLV: A Collaborative Latent Variable Model for Knowledge-Grounded Dialogue Generation". EMNLP(2021) [PDF]
• SKT-KG: "Augmenting Knowledge-grounded Conversations with Sequential Knowledge Transition". NAACL(2021) [PDF]
• MSKE: "More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge". EMNLP(2021) [PDF] [code]
• EARL: "EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning". EMNLP(2021) [PDF] [code]
• SECE: "Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer". ACL(2021) [PDF] [code] ⭐⭐⭐
• MIKe: "Initiative-Aware Self-Supervised Learning for Knowledge-Grounded Conversations". SIGIR(2021) [PDF] [code]
• GOKC: "Learning to Copy Coherent Knowledge for Response Generation". AAAI(2021) [PDF] [code]
• KnowledGPT: "Knowledge-Grounded Dialogue Generation with Pre-trained Language Models". EMNLP(2020) [PDF] [code]
• DiffKS: "Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation". EMNLP-Findings(2020) [PDF] [code]
• DukeNet: "DukeNet: A Dual Knowledge Interaction Network for Knowledge-Grounded Conversation". SIGIR(2020) [PDF] [code]
• CCN: "Cross Copy Network for Dialogue Generation". EMNLP(2020) [PDF] [code]
• PIPM: "Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation". EMNLP(2020) [PDF]
• ConceptFlow: "Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs". ACL(2020) [PDF] [code] ⭐⭐⭐⭐
• ConKADI: "Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness". ACL(2020) [PDF] [code] ⭐⭐⭐
• KIC: "Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy". ACL(2020) [PDF]
• SKT: "Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue". ICLR(2020) [PDF] [code] ⭐⭐⭐
• KdConv: "KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation". ACL(2020) [PDF] [data]
• TransDG: "Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering". AAAI(2020) [PDF] [code]
• RefNet: "RefNet: A Reference-aware Network for Background Based Conversation". AAAI(2020) [PDF] [code]
• GLKS: "Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation". AAAI(2020) [PDF] [code]
• AKGCM: "Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs". EMNLP(2019) [PDF] [code]
• DyKgChat: "DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs". EMNLP(2019) [PDF] [code]
• OpenDialKG: "OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs". ACL(2019) [PDF] [data]
• WoW: "Wizard of Wikipedia: Knowledge-Powered Conversational agents". ICLR(2019) [PDF]
• PostKS: "Learning to Select Knowledge for Response Generation in Dialog Systems". IJCAI(2019) [PDF] [code-1] [code-2] ⭐⭐⭐
• NKD: "Knowledge Diffusion for Neural Dialogue Generation". ACL(2018) [PDF] [data]
• Dual Fusion: "Smarter Response with Proactive Suggestion: A New Generative Neural Conversation Paradigm". IJCAI(2018) [PDF]
• CCM: "Commonsense Knowledge Aware Conversation Generation with Graph Attention". IJCAI(2018) [PDF] [code-tf] [code-py] ⭐⭐⭐⭐⭐
• MTask: "A Knowledge-Grounded Neural Conversation Model". AAAI(2018) [PDF]
• GenDS: "Flexible End-to-End Dialogue System for Knowledge Grounded Conversation". arXiv(2017) [PDF]

Emotion-aware Dialogue

• MultiESC: "Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning". EMNLP(2022) [PDF] [code] ⭐⭐⭐⭐
• CASE: "CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation". arXiv(2022) [PDF]
• PosEmoDial: "Towards Multi-Turn Empathetic Dialogs with Positive Emotion Elicitation". arXiV(2022) [PDF]
• MISC: "MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation". ACL(2022) [PDF] [code]
• C3KG: "C3KG: A Chinese Commonsense Conversation Knowledge Graph". ACL-Findings(2022) [PDF] [data]
• GLHG: "Control Globally, Understand Locally: A Global-to-Local Hierarchical Graph Network for Emotional Support Conversation". IJCAI(2022) [PDF]
• CEM: "CEM: Commonsense-aware Empathetic Response Generation". AAAI(2022) [PDF] [code]
• GEE: "Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes". EMNLP(2021) [PDF] [code]
• RecEC: "Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations". EMNLP-Findings(2021) [PDF] [code]
• CoMAE: "CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation". ACL-Findings(2021) [PDF] [code]
• ESConv: "Towards Emotional Support Dialog Systems". ACL(2021) [PDF] [data] ⭐⭐⭐
• CARE: "CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts". AAAI(2021) [PDF] [code]
• EmpDG: "EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation". COLING(2020) [PDF] [code]
• MIME: "MIME: MIMicking Emotions for Empathetic Response Generation". EMNLP(2020) [PDF] [code]
• PEC: "Towards Persona-Based Empathetic Conversational Models". EMNLP(2020) [PDF] [code]
• MoEL: "MoEL: Mixture of Empathetic Listeners". EMNLP(2019) [PDF] [code]
• EmpatheticDialogues: "Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset". ACL(2019) [PDF] [data] ⭐⭐⭐
• EmoDS: "Generating Responses with a Specific Emotion in Dialog". ACL(2019) [PDF]
• MojiTalk: "MojiTalk: Generating Emotional Responses at Scale". ACL(2018) [PDF]
• ECM: "Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory". AAAI(2018) [PDF] [code]

Task-oriented Dialogue

• Dialogic: "Dialogic: Controllable Dialogue Simulation with In-Context Learning". EMNLP-Findings(2022) [PDF] [code] ⭐⭐⭐
• KB-Adapter: "Injecting Domain Knowledge in Language Models for Task-Oriented Dialogue Systems". EMNLP(2022) [PDF] [code]
• TacoBot: "Bootstrapping a User-Centered Task-Oriented Dialogue System". Proceedings of Alexa Prize TaskBot(2021) [PDF] ⭐⭐⭐
• USDA: "User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems". WWW(2022) [PDF] [code]
• USS: "Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems". SIGIR(2021) [PDF] [data]
• NS-Dial: "An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation". ACL(2022) [PDF] [code]
• GALAXY: "GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection". AAAI(2022) [PDF] [code]
• PPTOD: "Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System". arXiv(2021) [PDF] [code]
• ToDCL: "Continual Learning in Task-Oriented Dialogue Systems". EMNLP(2021) [PDF] [code]
• IR-Net: "Intention Reasoning Network for Multi-Domain End-to-end Task-Oriented Dialogue". EMNLP(2021) [PDF]
• HyKnow: "HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management". ACL-Findings(2021) [PDF] [code]
• DDMN: "Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems". COLING(2020) [PDF] [code]
• ToD-BERT: "ToD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogues". EMNLP(2020) [PDF] [code]
• GraphDialog: "GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems". EMNLP(2020) [PDF] [code]
• MARCO: "Multi-Domain Dialogue Acts and Response Co-Generation". ACL(2020) [PDF] [code]
• DF-Net: "Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog". ACL(2020) [PDF] [code]
• MALA: "MALA: Cross-Domain Dialogue Generation with Action Learning". AAAI(2020) [PDF]
• SGD: "Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset". AAAI(2020) [PDF] [data]
• CrossWOZ: "CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset". TACL(2020) [PDF] [code]
• MultiWOZ: "MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling". EMNLP(2018) [PDF] [code]
• Neural Task-Oriented Dialogue: "Learning to Memorize in Neural Task-Oriented Dialogue Systems". MPhil Thesis(2019) [PDF] ⭐⭐⭐⭐
• GLMP: "Global-to-local Memory Pointer Networks for Task-Oriented Dialogue". ICLR(2019) [PDF] [code] ⭐⭐⭐⭐⭐
• KB Retriever: "Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever". EMNLP(2019) [PDF] [data]
• TRADE: "Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems". ACL(2019) [PDF] [code]
• WMM2Seq: "A Working Memory Model for Task-oriented Dialog Response Generation". ACL(2019) [PDF]
• Pretrain-Fine-tune: "Training Neural Response Selection for Task-Oriented Dialogue Systems". ACL(2019) [PDF] [data]
• Multi-level Mem: "Multi-Level Memory for Task Oriented Dialogs". NAACL(2019) [PDF] [code] ⭐⭐⭐
• BossNet: "Disentangling Language and Knowledge in Task-Oriented Dialogs ". NAACL(2019) [PDF] [code]
• SDN: "Subgoal Discovery for Hierarchical Dialogue Policy Learning". EMNLP(2018) [PDF] ⭐⭐⭐
• D3Q: "Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning". EMNLP(2018) [PDF] [code]
• DDQ: "Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning". ACL(2018) [PDF] [code]
• MAD: "Memory-augmented Dialogue Management for Task-oriented Dialogue Systems". TOIS(2018) [PDF]
• TSCP: "Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures". ACL(2018) [PDF] [code]
• Mem2Seq: "Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems". ACL(2018) [PDF] [code] ⭐⭐⭐⭐
• Topic-Seg-Label: "A Weakly Supervised Method for Topic Segmentation and Labeling in Goal-oriented Dialogues via Reinforcement Learning". IJCAI(2018) [PDF] [code]
• AliMe: "AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine". ACL(2017) [PDF]
• KVR Net: "Key-Value Retrieval Networks for Task-Oriented Dialogue". SIGDIAL(2017) [PDF] [data]

Open-domain Dialogue

• Overview: "Open-domain Dialogue Generation: What We Can Do, Cannot Do, And Should Do Next". ACL-NLP4ConvAI(2022) [PDF]
• Chirpy Cardinal: "Neural Generation Meets Real People: Building a Social, Informative Open-Domain Dialogue Agent". SIGDIAL(2022) [PDF] [code] [project] ⭐⭐⭐
• TIL: "Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure". EMNLP(2022) [PDF]
• ProphetChat: "ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation". ACL(2022) [PDF]
• DialoFlow: "Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances". ACL(2021) [PDF] [code] ⭐⭐⭐
• DialogBERT: "DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances". AAAI(2021) [PDF]
• BlenderBot: "Recipes for Building an Open-Domain Chatbot". EACL(2021) [PDF] [code]
• CDial-GPT: "A Large-Scale Chinese Short-Text Conversation Dataset". NLPCC(2020) [PDF] [code]
• DialoGPT: "DialoGPT : Large-Scale Generative Pre-training for Conversational Response Generation". ACL(2020) [PDF] [code] ⭐⭐⭐
• PLATO-XL: "PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation". arXiv(2021) [PDF] [code]
• PLATO-2: "PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning". ACL-Findings(2021) [PDF] [code]
• PLATO: "PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable". ACL(2020) [PDF] [code]
• Guyu: "An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue Generation". arXiv(2020) [PDF] [code]
• CL4Dialogue: "Group-wise Contrastive Learning for Neural Dialogue Generation". EMNLP-Findings(2020) [PDF] [code] ⭐⭐⭐
• Neg-train: "Negative Training for Neural Dialogue Response Generation". ACL(2020) [PDF] [code]
• HDSA: "Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention". ACL(2019) [PDF] [code] ⭐⭐⭐
• CAS: "Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory". NAACL(2019) [PDF] [code]
• Edit-N-Rerank: "Response Generation by Context-aware Prototype Editing". AAAI(2019) [PDF] [code] ⭐⭐⭐
• HVMN: "Hierarchical Variational Memory Network for Dialogue Generation". WWW(2018) [PDF] [code]
• XiaoIce: "The Design and Implementation of XiaoIce, an Empathetic Social Chatbot". arXiv(2018) [PDF] ⭐⭐⭐
• D2A: "Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base". NeurIPS(2018) [PDF] [code]
• DAIM: "Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization". NeurIPS(2018) [PDF]
• REASON: "Dialog Generation Using Multi-turn Reasoning Neural Networks". NAACL(2018) [PDF]
• STD/HTD: "Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders". ACL(2018) [PDF] [code]
• CSF: "Generating Informative Responses with Controlled Sentence Function". ACL(2018) [PDF] [code]
• DAWnet: "Chat More: Deepening and Widening the Chatting Topic via A Deep Model". SIGIR(2018) [PDF] [code]
• ZSDG: "Zero-Shot Dialog Generation with Cross-Domain Latent Actions". SIGDIAL(2018) [PDF] [code]
• DUA: "Modeling Multi-turn Conversation with Deep Utterance Aggregation". COLING(2018) [PDF] [code]
• Data-Aug: "Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding". COLING(2018) [PDF] [code]
• DC-MMI: "Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints". EMNLP(2018) [PDF] [code]
• cVAE-XGate/CGate: "Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity". EMNLP(2018) [PDF] [code]
• Retrieval+multi-seq2seq: "An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems". IJCAI(2018) [PDF]
• DAM: "Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network". ACL(2018) [PDF] [code] ⭐⭐⭐⭐
• SMN: "Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots". ACL(2017) [PDF] [code] ⭐⭐⭐
• CVAE/KgCVAE: "Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders". ACL(2017) [PDF] [code] ⭐⭐⭐
• TA-Seq2Seq: "Topic Aware Neural Response Generation". AAAI(2017) [PDF] [code]
• MA: "Mechanism-Aware Neural Machine for Dialogue Response Generation". AAAI(2017) [PDF]
• VHRED: "A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues". AAAI(2017) [PDF] [code]
• HRED: "Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models". AAAI(2016) [PDF] [code]
• RL-Dialogue: "Deep Reinforcement Learning for Dialogue Generation". EMNLP(2016) [PDF]
• MMI: "A Diversity-Promoting Objective Function for Neural Conversation Models". NAACL(2016) [PDF] [code]

Dialogue Evaluation

• GPTScore: "GPTScore: Evaluate as You Desire". arXiv(2023) [PDF] [code]
• LLMEval: "Understanding the Effectiveness of Very Large Language Models on Dialog Evaluation". IWSDS(2023) [PDF]
• ChatEvalPlatform: "Don't Forget Your ABC's: Evaluating the State-of-the-Art in Chat-Oriented Dialogue Systems". arXiv(2022) [PDF] [code]
• MoralDial: "MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Constructing Moral Discussions". arXiv(2022) [PDF]
• MDD-Eval: "MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation". AAAI(2022) [PDF] [code]
• Self-Eval: "SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation". COLING(2022) [PDF] [code]
• FineD-Eval: "FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation". EMNLP(2022) [PDF] [code]
• FlowEval: "FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows". EMNLP(2022) [PDF]
• IM2: "IM^2: an Interpretable and Multi-category Integrated Metric Framework for Automatic Dialogue Evaluation". EMNLP(2022) [PDF] [code]
• RoMe: "RoMe: A Robust Metric for Evaluating Natural Language Generation". ACL(2022) [PDF] [code]
• EAD: "Rethinking and Refining the Distinct Metric". ACL(2022) [PDF] [code]
• DiscoScore: "DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence". arXiv(2022) [PDF] [code]
• CTC-Score: "Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation". EMNLP(2021) [PDF] [code]
• Q^2: "$Q^{2}$: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering". EMNLP(2021) [PDF] [code]
• QuantiDCE: "Towards Quantifiable Dialogue Coherence Evaluation". ACL(2021) [PDF] [code]
• DynaEval: "DynaEval: Unifying Turn and Dialogue Level Evaluation". ACL(2021) [PDF] [code]
• Review: "How to Evaluate Your Dialogue Models: A Review of Approaches". arXiv(2021) [PDF]
• ConvLabEval: "Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation". SIGDIAL(2020) [PDF]
• FED: "Unsupervised Evaluation of Interactive Dialog with DialoGPT". SIGDIAL(2020) [PDF] [code] [data] ⭐⭐⭐
• Spot-the-Bot: "Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems". EMNLP(2020) [PDF] [code]
• CMADE: "Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation". ACL(2020) [PDF] [code]
• Coherence: "Dialogue Coherence Assessment Without Explicit Dialogue Act Labels". ACL(2020) [PDF] [code]
• MAUDE: "Learning an Unreferenced Metric for Online Dialogue Evaluation". ACL(2020) [PDF] [code]
• GRADE: "GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems". ACL(2020) [PDF] [code]
• BLEURT: "BLEURT: Learning Robust Metrics for Text Generation". ACL(2020) [PDF] [code]
• uBLEU: "uBLEU: Uncertainty-Aware Automatic Evaluation Method for Open-Domain Dialogue Systems". ACL(2020) [PDF] [code]
• USR: "USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation". ACL(2020) [PDF] [code]
• InteractiveEval: "Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems". NeurIPS(2019) [PDF] [code] ⭐⭐⭐
• ChatEval: "ChatEval: A Tool for Chatbot Evaluation". NAACL(2019) [PDF] [project]
• ADVMT: "One Ruler for All Languages: Multi-Lingual Dialogue Evaluation with Adversarial Multi-Task Learning". IJCAI(2018) [PDF]

Natural Language Generation

Survey of NLG

• CTG: "A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models". arXiv(2022) [PDF]
• RTG: "A Survey on Retrieval-Augmented Text Generation". arXiv(2022) [PDF]
• Hallucination: "Survey of Hallucination in Natural Language Generation". arXiv(2022) [PDF]
• Evaluation: "A Survey of Evaluation Metrics Used for NLG Systems". arXiv(2020) [PDF]

Text Planning

• RSTGen: "RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators". NAACL(2022) [PDF]
• Time Control: "Language Modeling via Stochastic Processes". ICLR(2022) [PDF] [code] ⭐⭐⭐⭐⭐
• PLANET: "PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation". ACL(2022) [PDF]
• EventPlan: "Event Transition Planning for Open-ended Text Generation". ACL-Findings(2022) [PDF] [code]
• CETP: "Knowledge-based Review Generation by Coherence Enhanced Text Planning". SIGIR(2021) [PDF] ⭐⭐⭐
• PlanGen: "Plan-then-Generate: Controlled Data-to-Text Generation via Planning". EMNLP-Findings(2021) [PDF] [code]
• DYPLOC: "DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation". ACL(2021) [PDF] [code]
• Tree-PLAN: "Infobox-to-text Generation with Tree-like Planning based Attention Network". IJCAI(2020) [PDF]
• ProphetNet: "ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training". EMNLP-Findings(2020) [PDF] [code] ⭐⭐⭐
• PAIR: "PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation". EMNLP(2020) [PDF] [code]
• SentPlan: "Sentence-Level Content Planning and Style Specification for Neural Text Generation". EMNLP(2019) [PDF] [code]
• PHVM: "Long and Diverse Text Generation with Planning-based Hierarchical Variational Model". EMNLP(2019) [PDF] [code]
• TwinNet: "Twin Networks: Matching the Future for Sequence Generation". ICLR(2018) [PDF] [code]
• PAG: "Plan, Attend, Generate: Planning for Sequence-to-Sequence Models". NIPS(2017) [PDF]

Controllable Generation

• Cognac: "Controllable Text Generation with Language Constraints". arXiv(2022) [PDF] [code]
• CriticControl: "Critic-Guided Decoding for Controlled Text Generation". arXiv(2022) [PDF]
• LatentOps: "Composable Text Controls in Latent Space with ODEs". arXiv(2022) [PDF] [code]
• FAST: "FAST: Improving Controllability for Text Generation with Feedback Aware Self-Training". arXiv(2022) [PDF]
• DisCup: "DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation". EMNLP(2022) [PDF] [code]
• MultiControl: "A Distributional Lens for Multi-Aspect Controllable Text Generation". EMNLP(2022) [PDF] [code]
• NADO: "Controllable Text Generation with Neurally-Decomposed Oracle". NeurIPS(2022) [PDF] [code]
• Mix-Match: "Mix and Match: Learning-free Controllable Text Generation using Energy Language Models". ACL(2022) [PDF] [code]
• ControlPrefix: "Controllable Natural Language Generation with Contrastive Prefixes". ACL-Findings(2022) [PDF]
• MUCOCO: "Controlled Text Generation as Continuous Optimization with Multiple Constraints". NeurIPS(2021) [PDF] [code]
• DExperts: "DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts". ACL(2021) [PDF] [code]
• FUDGE: "FUDGE: Controlled Text Generation With Future Discriminators". NAACL(2021) [PDF] [code]
• GeDi: "GeDi: Generative Discriminator Guided Sequence Generation". EMNLP-Findings(2021) [PDF] [code]
• GDC: "A Distributional Approach to Controlled Text Generation". ICLR(2021) [PDF] [code] ⭐⭐⭐
• CoCon: "CoCon: A Self-Supervised Approach for Controlled Text Generation". ICLR(2021) [PDF] [code]
• PPLM: "Plug and Play Language Models: A Simple Approach to Controlled Text Generation". ICLR(2020) [PDF] [code] ⭐⭐⭐
• CTRL: "CTRL: A Conditional Transformer Language Model for Controllable Generation". arXiv(2019) [PDF] [code]

Diffusion Models for Generation

• discrete-diffusion: "A Reparameterized Discrete Diffusion Model for Text Generation". arXiv(2023) [PDF] [code]
• Difformer: "Difformer: Empowering Diffusion Models on the Embedding Space for Text Generation". arXiv(2023) [PDF] ⭐⭐⭐
• GENIE: "Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise". arXiv(2022) [PDF] [code]
• SED: "Self-conditioned Embedding Diffusion for Text Generation". arXiv(2022) [PDF]
• SSD-LM: "SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control". arXiv(2022) [PDF] [code]
• LD4LG: "Latent Diffusion for Language Generation". arXiv(2022) [PDF] [code]
• DiffusionBERT: "DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models". arXiv(2022) [PDF] [code]
• DiffusER: "DiffusER: Discrete Diffusion via Edit-based Reconstruction". arXiv(2022) [PDF] [code]
• SeqDiffuSeq: "SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers". arXiv(2022) [PDF] [code]
• DiffuSeq: "DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models". ICLR(2023) [PDF] [code]
• Diffusion-LM: "Diffusion-LM Improves Controllable Text Generation". NeurIPS(2022) [PDF] [code] ⭐⭐⭐
• D3PM: "Structured Denoising Diffusion Models in Discrete State-Spaces". NeurIPS(2021) [PDF] [code]

Generation (Theories and Techniques)

• LaMemo: "LaMemo: Language Modeling with Look-Ahead Memory". NAACL(2022) [PDF] [code]
• PTG: "Learning to Transfer Prompts for Text Generation". NAACL(2022) [PDF] [code]
• EISL: "Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation". NAACL(2022) [PDF] [code]
• CT-Loss: "A Simple Contrastive Learning Objective for Alleviating Neural Text Degeneration". arXiv(2022) [PDF] [code]
• SimCTG: "A Contrastive Framework for Neural Text Generation". NeurIPS(2022) [PDF] [code] ⭐⭐⭐
• CoNT: "CoNT: Contrastive Neural Text Generation". NeurIPS(2022) [PDF] [code]
• Two-level-CL: "Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation". ACL(2022) [PDF]
• CLAPS: "Contrastive Learning with Adversarial Perturbations for Conditional Text Generation". ICLR(2021) [PDF] [code] ⭐⭐⭐⭐
• RetGen: "RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling". AAAI(2022) [PDF] [code]
• RAG: "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks". NeurIPS(2020) [PDF] [code] ⭐⭐⭐⭐
• TextGAIL: "TextGAIL: Generative Adversarial Imitation Learning for Text Generation". AAAI(2021) [PDF] [code]
• Latent-GLAT: "latent-GLAT: Glancing at Latent Variables for Parallel Text Generation". ACL(2022) [PDF] [code]
• s2s-ft: "s2s-ft: Fine-Tuning Pretrained Transformer Encoders for Sequence-to-Sequence Learning". arXiv(2021) [PDF] [code]
• EBM: "Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation?". EMNLP(2021) [PDF]
• DiscoDVT: "DiscoDVT: Generating Long Text with Discourse-Aware Discrete Variational Transformer". EMNLP(2021) [PDF] [code]
• DATG: "Data Augmentation for Text Generation Without Any Augmented Data". ACL(2021) [PDF]
• JointGT: "JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs". ACL-Findings(2021) [PDF] [code]
• Embedding-Transfer: "Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation". ACL(2021) [PDF] [code]
• FastSeq: "EL-Attention: Memory Efficient Lossless Attention for Generation". ICML(2021) [PDF] [code] ⭐⭐⭐
• BERTSeq2Seq: "Leveraging Pre-trained Checkpoints for Sequence Generation Tasks". TACL(2020) [PDF] [code-tf] [code-py] ⭐⭐⭐
• ERNIE-GEN: "ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation". IJCAI(2020) [PDF] [code] ⭐⭐⭐
• DITTO: "Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation". NeurIPS(2022) [PDF] [code]
• Repetition-Problem: "A Theoretical Analysis of the Repetition Problem in Text Generation". AAAI(2021) [PDF] [code]
• ENCONTER: "ENCONTER: Entity Constrained Progressive Sequence Generation via Insertion-based Transformer". EACL(2021) [PDF] [code]
• POINTER: "POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training". EMNLP(2020) [PDF] [code]
• Cascaded Generation: "Cascaded Text Generation with Markov Transformers". NeurIPS(2020) [PDF] [code]
• Entmax: "Sparse Sequence-to-Sequence Models". ACL(2019) [PDF] [code]

Decoding Algorithm

• EAD: "The Stable Entropy Hypothesis and Entropy-Aware Decoding: An Analysis and Algorithm for Robust Natural Language Generation". arXiv(2023) [PDF] [code]
• Contrastive Search: "Contrastive Search Is What You Need For Neural Text Generation". TMLR(2023) [PDF] [code] [blog] ⭐⭐⭐
• Momentum Decoding: "Momentum Decoding: Open-ended Text Generation As Graph Exploration". arXiv(2022) [PDF] [code]
• Crowd Sampling: "Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding". arXiv(2022) [PDF] [code]
• RankGen: "RankGen: Improving Text Generation with Large Ranking Models". EMNLP(2022) [PDF] [code]
• Contrastive Decoding: "Contrastive Decoding: Open-ended Text Generation as Optimization". arXiv(2022) [PDF] [code]
• COLD: "COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics". NeurIPS(2022) [PDF] [code] ⭐⭐⭐
• Lattice: "Massive-scale Decoding for Text Generation using Lattices". NAACL(2022) [PDF] [code]
• KID: "Knowledge Infused Decoding". ICLR(2022) [PDF] [code]
• NeuroLogic A*esque: "NeuroLogic A *esque Decoding: Constrained Text Generation with Lookahead Heuristics". NAACL(2022) [PDF] [code]
• NeuroLogic: "NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints". NAACL(2021) [PDF] [code]
• DeLorean: "Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning". EMNLP(2020) [PDF] [code]
• Top-p(Nucleus) Sampling: "The Curious Case of Neural Text Degeneration". ICLR(2020) [PDF] [code]
• Disjunctive Constraints: "Guided Generation of Cause and Effect". IJCAI(2020) [PDF] [code-huggingface]
• CGMH: "CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling". AAAI(2019) [PDF] [code]
• DBS: "Directed Beam Search: Plug-and-Play Lexically Constrained Language Generation". arXiv(2020) [PDF] [code]
• DBA: "Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation". NAACL(2018) [PDF] [code-official] [code-fairseq]
• GBS: "Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search". ACL(2017) [PDF] [code]

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