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        <title>E3: Entailment-driven Extracting and Editing for Conversational Machine Reading</title>
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        <namePart type="given">Victor</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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    <abstract>Conversational machine reading systems help users answer high-level questions (e.g. determine if they qualify for particular government benefits) when they do not know the exact rules by which the determination is made (e.g. whether they need certain income levels or veteran status). The key challenge is that these rules are only provided in the form of a procedural text (e.g. guidelines from government website) which the system must read to figure out what to ask the user. We present a new conversational machine reading model that jointly extracts a set of decision rules from the procedural text while reasoning about which are entailed by the conversational history and which still need to be edited to create questions for the user. On the recently introduced ShARC conversational machine reading dataset, our Entailment-driven Extract and Edit network (E3) achieves a new state-of-the-art, outperforming existing systems as well as a new BERT-based baseline. In addition, by explicitly highlighting which information still needs to be gathered, E3 provides a more explainable alternative to prior work. We release source code for our models and experiments at https://github.com/vzhong/e3.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1223</identifier>
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%0 Conference Proceedings
%T E3: Entailment-driven Extracting and Editing for Conversational Machine Reading
%A Zhong, Victor
%A Zettlemoyer, Luke
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhong-zettlemoyer-2019-e3
%X Conversational machine reading systems help users answer high-level questions (e.g. determine if they qualify for particular government benefits) when they do not know the exact rules by which the determination is made (e.g. whether they need certain income levels or veteran status). The key challenge is that these rules are only provided in the form of a procedural text (e.g. guidelines from government website) which the system must read to figure out what to ask the user. We present a new conversational machine reading model that jointly extracts a set of decision rules from the procedural text while reasoning about which are entailed by the conversational history and which still need to be edited to create questions for the user. On the recently introduced ShARC conversational machine reading dataset, our Entailment-driven Extract and Edit network (E3) achieves a new state-of-the-art, outperforming existing systems as well as a new BERT-based baseline. In addition, by explicitly highlighting which information still needs to be gathered, E3 provides a more explainable alternative to prior work. We release source code for our models and experiments at https://github.com/vzhong/e3.
%R 10.18653/v1/P19-1223
%U https://aclanthology.org/P19-1223
%U https://doi.org/10.18653/v1/P19-1223
%P 2310-2320

Markdown (Informal)

[E3: Entailment-driven Extracting and Editing for Conversational Machine Reading](https://aclanthology.org/P19-1223) (Zhong & Zettlemoyer, ACL 2019)

• E3: Entailment-driven Extracting and Editing for Conversational Machine Reading (Zhong & Zettlemoyer, ACL 2019)

ACL

• Victor Zhong and Luke Zettlemoyer. 2019. E3: Entailment-driven Extracting and Editing for Conversational Machine Reading. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2310–2320, Florence, Italy. Association for Computational Linguistics.