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Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Harsh
;
Balasubramanian, Niranjan
;
Inoue, Naoya
;
Inui, Kentaro
;
Sinha, Steven
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.490/ Abstract: How can we generate concise explanations for multi-hop Reading Comprehension (RC)? The current strategies of identifying supporting sentences can be seen as an extractive question-focused summarization of the input text. However, these extractive explanations are not necessarily concise i.e. not minimally sufficient for answering a question. Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system. Given a limited amount of human-annotated abstractive explanations, we train the abstractive explainer in a semi-supervised manner, where we start from the supervised model and then train it further through trial and error maximizing a conciseness-promoted reward function. Our experiments demonstrate that the proposed abstractive explainer can generate more compact explanations than an extractive explainer with limited ...
Keyword:
Computational Linguistics
;
Machine Learning
;
Machine Learning and Data Mining
;
Natural Language Processing
;
Question-Answering Systems
URL:
https://underline.io/lecture/37463-summarize-then-answer-generating-concise-explanations-for-multi-hop-reading-comprehension
https://dx.doi.org/10.48448/g81n-2n36
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