Here is a list of recent publications about Embedding models of entities and relationships for temporal knowledge base completion.
(Update on Nov 26th, 2020)
-- We will continue to add and update related papers and codes on this page.
-- 
indicates available code and 
indicates high citation in recent years.
An overview of embedding models of entities and relationships for temporal knowledge base completion. Yongpan Sheng* (CQU), Lirong He* (CQU), Jiasheng Zhang* (UESTC), Zaitang Li (CUHK), Zhiting Hu (UCSD), Qingyun Wang (UIUC), Heng Ji (UIUC), Meng Jiang (ND), and Jie Shao (UESTC). arXiv. 2010.04389 (* represents equal contribution)
To the best of our knowledge, our survey is the first work that presents a comprehensive review of embedding models for temporal knowledge base completion. It aims to provide NLG researchers a synthesis and pointer to related researches. Our survey also includes a detailed discussion about how NLG can benefit from recent progress in deep learning and artificial intelligence, including technologies such as graph neural network, reinforcement learning, neural topic modeling and so on.
(For new learners, some important papers for general NLG/KENLG.)
[Seq2Seq] Sequence to Sequence Learning with Neural Networks
- Ilya Sutskever (Google) et al, In NeurIPS 2014.
- [SeqAttn] Neural Machine Translation by Jointly Learning to Align and Translate
- Dzmitry Bahdanau (Jacobs University) et al, In ICLR 2015.
- [CopyNet] Incorporating Copying Mechanism in Sequence-to-Sequence Learning
[GPT-2] Language Models are Unsupervised Multitask Learners
- Alec Radford (OpenAI) et al, On OpenAI blog 2019. [official code](tensorflow) [huggingface](pytorch)
- [UniLM] Unified Language Model Pre-training for Natural Language Understanding and Generation
- Li Dong (Microsoft) et al, In NeurIPS 2019. [official code](pytorch)
- [Posterior Regularization] Deep Generative Models with Learnable Knowledge Constraints
- Zhiting Hu (Carnegie Mellon University) et al, In NeurIPS 2018.
- [Plug and Play] Plug and Play Language Models: A Simple Approach to Controlled Text Generation
- [Dialogue System] Topic Aware Neural Response Generation
- Chen Xing (Nankai University) et al, In AAAI 2017.
- [Dialogue System] A Neural TopicalExpansion Framework for Unstructured Persona-oriented Dialogue Generation
- Minghong Xu (Shandong University) et al, In ECAI 2020. [code](tensorflow)
- [Summarization] Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
- Shashi Narayan (University of Edinburgh) et al, In EMNLP 2018. [code](pytorch)
- [Dialogue System] Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
- Lili Mou (Peking University) et al, In COLING 2016. [code](tensorflow)
- [Dialogue System] Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
- Hao Zhou (Tsinghua University) et al, In AAAI 2018. [code](tensorflow)
- [Dialogue System] Generating Responses with a Specific Emotion in Dialog
- Zhenqiao Song (Fudan University) et al, In ACL 2019.
- [Summarization] Guiding Generation for Abstractive Text Summarization based on Key Information Guide Network
- [Question Answering] Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning
- Shizhu He (Chinese Academy of Sciences) et al, In ACL 2017.
- [Question Answering] Natural answer generation with heterogeneous memory
- Yao Fu (Peking University) et al, In NAACL 2018.
- [Dialogue System] Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems
- Andrea Madotto (Hong Kong University of Science and Technology) et al, In ACL 2019. [code](pytorch)
- [Dialogue System] Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
- Chien-Sheng Wu (Hong Kong University of Science and Technology) et al, In ICLR 2019. [code](pytorch)
- [Dialogue System] Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering
- Jian Wang (South China University of Technology) et al, In AAAI 2020. [code](pytorch)
- [Dialogue System] Learning to Select Knowledge for Response Generation in Dialog Systems
- Rongzhong Lian (Baidu) et al, In IJCAI 2019.
- [Dialogue System] Commonsense Knowledge Aware Conversation Generation with Graph Attention
- [Dialogue System] Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs
- Zhibin Liu, (Baidu) et al, In EMNLP 2019.
- [Dialogue System] DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs
- Yi-Lin Tuan (National Taiwan University) et al, In EMNLP 2019. [code](tensorflow)
- [Dialogue System] Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs
- Houyu Zhang (Brown University) et al, In ACL 2020. [code](pytorch)
- [Question Answering] Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs
- Angela Fan (Facebook AI Research) et al, In EMNLP 2019.
- [Summarization] Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward
- [Dialogue System] A Knowledge-Grounded Neural Conversation Model
- Marjan Ghazvininejad (University of Southern California) et al, In AAAI 2018. [data]
- [Dialogue System] Wizard of Wikipedia: Knowledge-Powered Conversational agents
- Emily Dinan (Facebook AI Research) et al, In ICLR 2019. [code](pytorch)
- [KG + LM] A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation
- Jian Guan (Tsinghua University) et al, In TACL 2020. [code](tensorflow)
@article{yu2020survey,
title={A Survey of Knowledge-Enhanced Text Generation},
author={Sheng, Yongpan and He, Lirong and Zhang, Jiasheng and Hu, Zhiting and Wang, Qingyun and Ji, Heng and Jiang, Meng and Shao, Jie},
journal={arXiv preprint arXiv:2010.04389},
year={2020}
}
This page is contributed by Yongpan Sheng(shengyp2011@163.com) and Jiasheng Zhang(zjss12358@163.com).