@inproceedings{zhang-etal-2023-bridging-gap,
title = "Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension",
author = "Zhang, Xiao and
Huang, Heyan and
Chi, Zewen and
Mao, Xian-Ling",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.857",
doi = "10.18653/v1/2023.acl-long.857",
pages = "15374--15386",
abstract = "Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversational interaction scenes. Machines are required to make a decision of {``}Yes/No/Inquire{''} or generate a follow-up question when the decision is {``}Inquire{''} based on retrieved rule texts, user scenario, user question and dialogue history. Recent studies try to reduce the information gap between decision-making and question generation, in order to improve the performance of generation. However, the information gap still persists because these methods are still limited in pipeline framework, where decision-making and question generation are performed separately, making it hard to share the entailment reasoning used in decision-making across all stages. To tackle the above problem, we propose a novel one-stage end-to-end framework, called Entailment Fused-T5 (EFT), to bridge the information gap between decision-making and question generation in a global understanding manner. The extensive experimental results demonstrate that our proposed framework achieves new state-of-the-art performance on the OR-ShARC benchmark. Our model and code are publicly available at an anonymous link.",
}
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<abstract>Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversational interaction scenes. Machines are required to make a decision of “Yes/No/Inquire” or generate a follow-up question when the decision is “Inquire” based on retrieved rule texts, user scenario, user question and dialogue history. Recent studies try to reduce the information gap between decision-making and question generation, in order to improve the performance of generation. However, the information gap still persists because these methods are still limited in pipeline framework, where decision-making and question generation are performed separately, making it hard to share the entailment reasoning used in decision-making across all stages. To tackle the above problem, we propose a novel one-stage end-to-end framework, called Entailment Fused-T5 (EFT), to bridge the information gap between decision-making and question generation in a global understanding manner. The extensive experimental results demonstrate that our proposed framework achieves new state-of-the-art performance on the OR-ShARC benchmark. Our model and code are publicly available at an anonymous link.</abstract>
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%0 Conference Proceedings
%T Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension
%A Zhang, Xiao
%A Huang, Heyan
%A Chi, Zewen
%A Mao, Xian-Ling
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhang-etal-2023-bridging-gap
%X Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversational interaction scenes. Machines are required to make a decision of “Yes/No/Inquire” or generate a follow-up question when the decision is “Inquire” based on retrieved rule texts, user scenario, user question and dialogue history. Recent studies try to reduce the information gap between decision-making and question generation, in order to improve the performance of generation. However, the information gap still persists because these methods are still limited in pipeline framework, where decision-making and question generation are performed separately, making it hard to share the entailment reasoning used in decision-making across all stages. To tackle the above problem, we propose a novel one-stage end-to-end framework, called Entailment Fused-T5 (EFT), to bridge the information gap between decision-making and question generation in a global understanding manner. The extensive experimental results demonstrate that our proposed framework achieves new state-of-the-art performance on the OR-ShARC benchmark. Our model and code are publicly available at an anonymous link.
%R 10.18653/v1/2023.acl-long.857
%U https://aclanthology.org/2023.acl-long.857
%U https://doi.org/10.18653/v1/2023.acl-long.857
%P 15374-15386
Markdown (Informal)
[Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension](https://aclanthology.org/2023.acl-long.857) (Zhang et al., ACL 2023)
ACL