Transforming Question Answering Datasets Into Natural Language Inference Datasets

Abstract

Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.

arXiv comment11 pages, 6 figures

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• KERMIT: Generative Insertion-Based Modeling for Sequences
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• FarsTail: A Persian Natural Language Inference Dataset
• Ask to Know More: Generating Counterfactual Explanations for Fake Claims
• Dialogue Natural Language Inference
• Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models
• Asking Crowdworkers to Write Entailment Examples: The Best of Bad Options
• Zero-shot Fact Verification by Claim Generation
• Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering
• Open Information Extraction from Question-Answer Pairs
• Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning
• Reading Comprehension as Natural Language Inference: A Semantic Analysis
• Can NLI Models Verify QA Systems' Predictions?
• Case-Based Abductive Natural Language Inference
• Looking Beyond Sentence-Level Natural Language Inference for Downstream Tasks
• Incorporating Question Answering-Based Signals into Abstractive Summarization via Salient Span Selection
• Careful Selection of Knowledge to solve Open Book Question Answering
• DocNLI: A Large-scale Dataset for Document-level Natural Language Inference
• Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuning
• Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little
• Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
• Learning Open Information Extraction of Implicit Relations from Reading Comprehension Datasets
• Understanding Roles and Entities: Datasets and Models for Natural Language Inference
• Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment
• Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models