Unified Open-Domain Question Answering with Structured and Unstructured Knowledge [https://arxiv.org/pdf/2012.14610.pdf]
Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals [https://arxiv.org/pdf/2101.03737.pdf]
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval [https://arxiv.org/pdf/2101.00436.pdf]
WebSRC: A Dataset for Web-Based Structural Reading Comprehension [https://arxiv.org/pdf/2101.09465.pdf]
WEAKLY SUPERVISED NEURO-SYMBOLIC MODULE NETWORKS FOR NUMERICAL REASONING [https://arxiv.org/pdf/2101.02235.pdf]
Can Small and Synthetic Benchmarks Drive Modeling Innovation? A Retrospective Study of Question Answering Modeling Approaches [https://arxiv.org/pdf/2102.01065.pdf]
MULTIMODALQA: COMPLEX QUESTION ANSWERING OVER TEXT, TABLES AND IMAGES [https://openreview.net/pdf?id=ee6W5UgQLa]
Complementary Evidence Identification in Open-Domain Question Answering [https://arxiv.org/pdf/2103.11643.pdf]
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering [https://arxiv.org/pdf/2104.06378.pdf]
Ask what’s missing and what’s useful: Improving Clarification Question Generation using Global Knowledge [https://arxiv.org/pdf/2104.06828.pdf]
Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval [https://arxiv.org/pdf/2104.08801.pdf]
Capturing Row and Column Semantics in Transformer Based Question Answering over Tables [https://arxiv.org/pdf/2104.08303.pdf]
Optimizing Deeper Transformers on Small Datasets: An Application on Text-to-SQL Semantic Parsing [https://arxiv.org/pdf/2012.15355.pdf]
Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering [https://arxiv.org/pdf/2007.00266.pdf]
Web Question Answering with Neurosymbolic Program Synthesis [https://arxiv.org/pdf/2104.07162.pdf]
Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations [https://arxiv.org/pdf/2104.07478.pdf]
Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog [https://arxiv.org/pdf/2104.04923.pdf]
Constrained Language Models Yield Few-Shot Semantic Parsers [https://arxiv.org/pdf/2104.08768.pdf]
End-to-End Training of Neural Retrievers for Open-Domain Question Answering [https://arxiv.org/pdf/2101.00408.pdf]
ERNIE-DOC: The Retrospective Long-Document Modeling Transformer [https://arxiv.org/pdf/2012.15688.pdf]
LIME: LEARNING INDUCTIVE BIAS FOR PRIMITIVES OF MATHEMATICAL REASONING [https://arxiv.org/pdf/2101.06223.pdf]
Making Pre-trained Language Models Better Few-shot Learners [https://arxiv.org/pdf/2012.15723.pdf]
On-the-Fly Attention Modularization for Neural Generation [https://arxiv.org/pdf/2101.00371.pdf]
Prefix-Tuning: Optimizing Continuous Prompts for Generation [https://arxiv.org/pdf/2101.00190.pdf]
Studying Strategically: Learning to Mask for Closed-book QA [https://arxiv.org/pdf/2012.15856.pdf]
Representations for Question Answering from Documents with Tables and Text [https://arxiv.org/pdf/2101.10573.pdf]
Muppet: Massive Multi-task Representations with Pre-Finetuning [https://arxiv.org/pdf/2101.11038.pdf]
How Many Data Points is a Prompt Worth? [https://arxiv.org/pdf/2103.08493.pdf]
Rethinking Relational Encoding in Language Model Pre-Training for General Sequences [https://arxiv.org/pdf/2103.10334.pdf]
Controllable Generation from Pre-trained Language Models via Inverse Prompting [https://arxiv.org/pdf/2103.10685.pdf]
Attribute Alignment: Controlling Text Generation from Pre-trained Language Models [https://arxiv.org/pdf/2103.11070.pdf]
Unified Pre-training for Program Understanding and Generation [https://arxiv.org/pdf/2103.06333.pdf]
BASE Layers: Simplifying Training of Large, Sparse Models [https://arxiv.org/pdf/2103.16716.pdf]
Factual Probing Is [MASK]: Learning vs. Learning to Recall https://arxiv.org/pdf/2104.05240.pdf
Relational world knowledge representation in contextual language models: A review [https://arxiv.org/pdf/2104.05837.pdf]
Learning How to Ask: Querying LMs with Mixtures of Soft Prompts [https://arxiv.org/pdf/2104.06599.pdf]
On the Influence of Masking Policies in Intermediate Pre-training [https://arxiv.org/pdf/2104.08840.pdf]
TRANSFORMER-XH: MULTI-EVIDENCE REASONING WITH EXTRA HOP ATTENTION [https://openreview.net/pdf?id=r1eIiCNYwS]
Promoting Graph Awareness in Linearized Graph-to-Text Generation [https://arxiv.org/pdf/2012.15793.pdf]
Inducing Meaningful Units from Character Sequences with Slot Attention [https://arxiv.org/pdf/2102.01223.pdf]
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Technical Report on Data Integration and Preparation [https://arxiv.org/pdf/2103.01986.pdf]
Neural Database Operator Model [https://research.fb.com/wp-content/uploads/2021/02/Neural-Database-Operator-Model.pdf]
Relational Pretrained Transformers towards Democratizing Data Preparation [https://arxiv.org/pdf/2012.02469.pdf]
Querying Heterogeneous Information Sources Using Source Descriptions [http://ilpubs.stanford.edu:8090/191/1/1996-61.pdf]
Answering queries using views: A survey [http://www.diag.uniroma1.it//~degiacom/didattica/semingsoft/SIS05-06/materiale/2-integrazione/riferimenti/Halevy-survey-vldbj-01.pdf]
Natural Language Inference over Tables: Enabling Explainable Data Exploration on Data Lakes [https://openreview.net/pdf/c6b759e2420cdaf517b3b98243b7f9e1da6d6ae6.pdf]
Logic Embeddings for Complex Query Answering [https://arxiv.org/pdf/2103.00418.pdf]
From Natural Language Processing to Neural Databases [http://www.vldb.org/pvldb/vol14/p1033-thorne.pdf]
Noah: Creating Data Integration Pipelines over Continuously Extracted Web Data [http://ceur-ws.org/Vol-2841/PIE+Q_3.pdf]
Annotating Columns with Pre-trained Language Models [https://arxiv.org/pdf/2104.01785.pdf]
Survey on semantic data integration approaches: Issues and directions [http://www.semantic-web-journal.net/system/files/swj2756.pdf]
Learning to Reason for Text Generation from Scientific Tables [https://openreview.net/pdf?id=_QCy6HJJ4wd]
Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network [https://www.aclweb.org/anthology/2021.eacl-main.102.pdf]
Synthesizing Context-free Grammars from Recurrent Neural Networks [https://arxiv.org/pdf/2101.08200.pdf]
Certified Robustness to Adversarial Word Substitutions [https://arxiv.org/pdf/1909.00986.pdf]
Neural Data Augmentation via Example Extrapolation [https://arxiv.org/pdf/2102.01335.pdf]
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation [https://arxiv.org/pdf/2102.04664.pdf]
Semi-supervised Event Argument Extraction via Dual Question Answering [https://www.aaai.org/AAAI21Papers/AAAI-2635.ZhouY.pdf]
Representing Numbers in NLP: a Survey and a Vision [https://arxiv.org/pdf/2103.13136.pdf]
DODRIO: Exploring Transformer Models with Interactive Visualization [https://arxiv.org/pdf/2103.14625.pdf]
Cycle Self-Training for Domain Adaptation [https://arxiv.org/pdf/2103.03571.pdf]
On Unifying Misinformation Detection [https://arxiv.org/pdf/2104.05243.pdf]
Automatic Webpage Briefing [https://www.ruizhang.info/publications/ICDE2021_Automatic%20Webpage%20Briefing.pdf]
Learning to Decompose and Organize Complex Tasks [http://ryenwhite.com/papers/ZhangNAACL2021.pdf]