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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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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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The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Das, Rajarshi; Godbole, Ameya; Lee, Jay-Yoon; McCallum, Andrew; Perez, Ethan; Polymenakos, Lazaros; Tan, Lizhen; Thai, June; Zaheer, Manzil. - : Underline Science Inc., 2021
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.755/ Abstract: It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions --- a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach (\alg) for question answering over large knowledge bases. CBR-KBQA consists of a nonparametric memory that stores cases (question and logical forms) and a parametric model that can generate a logical form for a new question by retrieving cases that are relevant to it. On several KBQA datasets that contain complex questions, CBR-KBQA achieves competitive performance. For example, on the CWQ dataset, CBR-KBQA outperforms the current state of the art by 11\% on accuracy. Furthermore, we show that CBR-KBQA is capable of using new cases \emph{without} any further training: by incorporating a few human-labeled examples in the case memory, CBR-KBQA is able to successfully generate logical forms containing ...
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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://dx.doi.org/10.48448/ds4h-2779 https://underline.io/lecture/37456-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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