@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.",
}

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.
Zhang, Xiao Huang, Heyan Chi, Zewen Mao, Xian-Ling
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension