Who cites «DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs» · page 6

600 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning — our card · 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Single-dataset Experts for Multi-dataset Question Answering — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
SituatedQA: Incorporating Extra-Linguistic Contexts into QA — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance — 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Team Papelo at FEVEROUS: Multi-hop Evidence Pursuit — 2021 · Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)
Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
The Power of Scale for Parameter-Efficient Prompt Tuning — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Time-Stamped Language Model: Teaching Language Models to Understand The Flow of Events — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Truth-Conditional Captions for Time Series Data — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
What Can a Generative Language Model Answer About a Passage? — 2021 · Proceedings of the 3rd Workshop on Machine Reading for Question Answering
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? — 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
What to Pre-Train on? Efficient Intermediate Task Selection — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
What's in Your Head? Emergent Behaviour in Multi-Task Transformer Models — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
What's in a Name? Answer Equivalence For Open-Domain Question Answering — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Why Machine Reading Comprehension Models Learn Shortcuts? — 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering — 2020 · arXiv:2005.05257
A Framework for Evaluation of Machine Reading Comprehension Gold Standards — 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference
A Sequence-to-sequence Approach for Numerical Slot-filling Dialog Systems — 2020 · Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
A Simple and Effective Model for Answering Multi-span Questions — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
A Vietnamese Dataset for Evaluating Machine Reading Comprehension — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
A guide to the dataset explosion in QA, NLI, and commonsense reasoning — our card · 2020 · Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts
An Empirical Investigation of Contextualized Number Prediction — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Are Pretrained Language Models Symbolic Reasoners over Knowledge? — 2020 · Proceedings of the 24th Conference on Computational Natural Language Learning
Ask to Learn: A Study on Curiosity-driven Question Generation — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension — 2020
Benefits of Intermediate Annotations in Reading Comprehension — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models — 2020 · arXiv:2005.14709
Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language Models — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Break It Down: A Question Understanding Benchmark — 2020 · Transactions of the Association for Computational Linguistics
CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management — 2020 · Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architectures — 2020 · arXiv:2007.08970
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Do We Need to Create Big Datasets to Learn a Task? — 2020 · Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing
Dynamic Sampling Strategies for Multi-Task Reading Comprehension — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Evaluating Models' Local Decision Boundaries via Contrast Sets — our card · 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
Exploring and Predicting Transferability across NLP Tasks — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Fine-tune BERT for E-commerce Non-Default Search Ranking — 2020 · arXiv:2008.09689
How Much Knowledge Can You Pack Into the Parameters of a Language Model? — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data — 2020 · arXiv:2004.07347
IIRC: A Dataset of Incomplete Information Reading Comprehension Questions — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Improving Compositional Generalization in Semantic Parsing — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
Improving QA Generalization by Concurrent Modeling of Multiple Biases — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
Injecting Numerical Reasoning Skills into Language Models — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Joint Translation and Unit Conversion for End-to-end Localization — 2020 · Proceedings of the 17th International Conference on Spoken Language Translation
Language (Re)modelling: Towards Embodied Language Understanding — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Language Models are Few-Shot Learners — 2020 · arXiv:2005.14165
Learn to Combine Linguistic and Symbolic Information for Table-based Fact Verification — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
Learning from Task Descriptions — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning — 2020 · arXiv:2007.08124
Low-Resource Generation of Multi-hop Reasoning Questions — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Machine Reasoning: Technology, Dilemma and Future — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
Methods for Numeracy-Preserving Word Embeddings — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
MuTual: A Dataset for Multi-Turn Dialogue Reasoning — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Obtaining Faithful Interpretations from Compositional Neural Networks — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Overestimation of Syntactic Representation in Neural Language Models — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Question Directed Graph Attention Network for Numerical Reasoning over Text — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning — 2020 · arXiv:2002.04326
Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions — our card · 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
TaPas: Weakly Supervised Table Parsing via Pre-training — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Teaching Machine Comprehension with Compositional Explanations — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
To Test Machine Comprehension, Start by Defining Comprehension — 2020 · arXiv:2005.01525
ToTTo: A Controlled Table-To-Text Generation Dataset — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Towards Interpretable Reasoning over Paragraph Effects in Situation — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Training Question Answering Models From Synthetic Data — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
UNIFIEDQA: Crossing Format Boundaries with a Single QA System — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
Understanding Unnatural Questions Improves Reasoning over Text — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
What Question Answering can Learn from Trivia Nerds — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
What-if I ask you to explain: Explaining the effects of perturbations in procedural text — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts — 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference
A Discrete Hard EM Approach for Weakly Supervised Question Answering — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering — 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering
Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
CLER: Cross-task Learning with Expert Representation to Generalize Reading and Understanding — 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering
Can You Unpack That? Learning to Rewrite Questions-in-Context — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Comprehensive Multi-Dataset Evaluation of Reading Comprehension — 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering
Do NLP Models Know Numbers? Probing Numeracy in Embeddings — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Domain-agnostic Question-Answering with Adversarial Training — 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering
EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference — our card · 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Evaluating Question Answering Evaluation — 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Let''s Ask Again: Refine Network for Automatic Question Generation — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Movie Plot Analysis via Turning Point Identification — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
NumNet: Machine Reading Comprehension with Numerical Reasoning — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
On Making Reading Comprehension More Comprehensive — 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering
QASC: A Dataset for Question Answering via Sentence Composition — 2019 · arXiv:1910.11473
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning — our card · 2019 · arXiv:1908.05803
Reasoning Over Paragraph Effects in Situations — 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering
Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches — 2019 · arXiv:1904.01172