@inproceedings{gao-etal-2020-explicit,
title = "Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading",
author = "Gao, Yifan and
Wu, Chien-Sheng and
Joty, Shafiq and
Xiong, Caiming and
Socher, Richard and
King, Irwin and
Lyu, Michael and
Hoi, Steven C.H.",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.88",
doi = "10.18653/v1/2020.acl-main.88",
pages = "935--945",
abstract = "The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions. Existing approaches are limited in their decision making due to struggles in extracting question-related rules and reasoning about them. In this paper, we present a new framework of conversational machine reading that comprises a novel Explicit Memory Tracker (EMT) to track whether conditions listed in the rule text have already been satisfied to make a decision. Moreover, our framework generates clarification questions by adopting a coarse-to-fine reasoning strategy, utilizing sentence-level entailment scores to weight token-level distributions. On the ShARC benchmark (blind, held-out) testset, EMT achieves new state-of-the-art results of 74.6{\%} micro-averaged decision accuracy and 49.5 BLEU4. We also show that EMT is more interpretable by visualizing the entailment-oriented reasoning process as the conversation flows. Code and models are released at \url{https://github.com/Yifan-Gao/explicit_memory_tracker}.",
}
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<abstract>The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions. Existing approaches are limited in their decision making due to struggles in extracting question-related rules and reasoning about them. In this paper, we present a new framework of conversational machine reading that comprises a novel Explicit Memory Tracker (EMT) to track whether conditions listed in the rule text have already been satisfied to make a decision. Moreover, our framework generates clarification questions by adopting a coarse-to-fine reasoning strategy, utilizing sentence-level entailment scores to weight token-level distributions. On the ShARC benchmark (blind, held-out) testset, EMT achieves new state-of-the-art results of 74.6% micro-averaged decision accuracy and 49.5 BLEU4. We also show that EMT is more interpretable by visualizing the entailment-oriented reasoning process as the conversation flows. Code and models are released at https://github.com/Yifan-Gao/explicit_memory_tracker.</abstract>
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%0 Conference Proceedings
%T Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
%A Gao, Yifan
%A Wu, Chien-Sheng
%A Joty, Shafiq
%A Xiong, Caiming
%A Socher, Richard
%A King, Irwin
%A Lyu, Michael
%A Hoi, Steven C.H.
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F gao-etal-2020-explicit
%X The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions. Existing approaches are limited in their decision making due to struggles in extracting question-related rules and reasoning about them. In this paper, we present a new framework of conversational machine reading that comprises a novel Explicit Memory Tracker (EMT) to track whether conditions listed in the rule text have already been satisfied to make a decision. Moreover, our framework generates clarification questions by adopting a coarse-to-fine reasoning strategy, utilizing sentence-level entailment scores to weight token-level distributions. On the ShARC benchmark (blind, held-out) testset, EMT achieves new state-of-the-art results of 74.6% micro-averaged decision accuracy and 49.5 BLEU4. We also show that EMT is more interpretable by visualizing the entailment-oriented reasoning process as the conversation flows. Code and models are released at https://github.com/Yifan-Gao/explicit_memory_tracker.
%R 10.18653/v1/2020.acl-main.88
%U https://aclanthology.org/2020.acl-main.88
%U https://doi.org/10.18653/v1/2020.acl-main.88
%P 935-945
Markdown (Informal)
[Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading](https://aclanthology.org/2020.acl-main.88) (Gao et al., ACL 2020)
ACL