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WebSRC: A Dataset for Web-Based Structural Reading Comprehension ...
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Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
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ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations ...
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CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization ...
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PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them ...
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Abstract:
Open-domain Question Answering models which directly leverage question-answer (QA) pairs, such as closed-book QA (CBQA) models and QA-pair retrievers, show promise in terms of speed and memory compared to conventional models which retrieve and read from text corpora. QA-pair retrievers also offer interpretable answers, a high degree of control, and are trivial to update at test time with new knowledge. However, these models lack the accuracy of retrieve-and-read systems, as substantially less knowledge is covered by the available QA-pairs relative to text corpora like Wikipedia. To facilitate improved QA-pair models, we introduce Probably Asked Questions (PAQ), a very large resource of 65M automatically-generated QA-pairs. We introduce a new QA-pair retriever, RePAQ, to complement PAQ. We find that PAQ preempts and caches test questions, enabling RePAQ to match the accuracy of recent retrieve-and-read models, whilst being significantly faster. Using PAQ, we train CBQA models which outperform comparable ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Question-Answering Systems
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URL: https://underline.io/lecture/38209-paq-65-million-probably-asked-questions-and-what-you-can-do-with-them https://dx.doi.org/10.48448/s0hq-cn08
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Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations ...
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Mapping probability word problems to executable representations ...
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Contrastive Domain Adaptation for Question Answering using Limited Text Corpora ...
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Smoothing Dialogue States for Open Conversational Machine Reading ...
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How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering ...
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FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation ...
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Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation ...
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Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval ...
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Zero-Shot Dialogue State Tracking via Cross-Task Transfer ...
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Case-based Reasoning for Natural Language Queries over Knowledge Bases ...
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Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answering ...
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Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation ...
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