Natural Answer Generation: From Factoid Answer to Full-length Answer using Grammar Correction

• URL: http://arxiv.org/abs/2112.03849v1
• Date: Tue, 7 Dec 2021 17:39:21 GMT
• Title: Natural Answer Generation: From Factoid Answer to Full-length Answer using Grammar Correction
• Authors: Manas Jain, Sriparna Saha, Pushpak Bhattacharyya, Gladvin Chinnadurai, Manish Kumar Vatsa
• Abstract summary: This paper proposes a system that outputs a full-length answer given a question and the extracted factoid answer as the input. A transformer-based Grammar Error Correction model GECToR ( 2020), is used as a post-processing step for better fluency. We compare our system with (i) Modified Pointer Generator (SOTA) and (ii) Fine-tuned DialoGPT for factoid questions.
• Score: 39.40116590327074
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Question Answering systems these days typically use template-based language generation. Though adequate for a domain-specific task, these systems are too restrictive and predefined for domain-independent systems. This paper proposes a system that outputs a full-length answer given a question and the extracted factoid answer (short spans such as named entities) as the input. Our system uses constituency and dependency parse trees of questions. A transformer-based Grammar Error Correction model GECToR (2020), is used as a post-processing step for better fluency. We compare our system with (i) Modified Pointer Generator (SOTA) and (ii) Fine-tuned DialoGPT for factoid questions. We also test our approach on existential (yes-no) questions with better results. Our model generates accurate and fluent answers than the state-of-the-art (SOTA) approaches. The evaluation is done on NewsQA and SqUAD datasets with an increment of 0.4 and 0.9 percentage points in ROUGE-1 score respectively. Also the inference time is reduced by 85\% as compared to the SOTA. The improved datasets used for our evaluation will be released as part of the research contribution.

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