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            <namePart type="given">Kristiina</namePart>
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    <abstract>Conversational agents offer users a natural-language interface to accomplish tasks, entertain themselves, or access information. Informational dialogue is particularly challenging in that the agent has to hold a conversation on an open topic, and to achieve a reasonable coverage it generally needs to digest and present unstructured information from textual sources. Making responses based on such sources sound natural and fit appropriately into the conversation context is a topic of ongoing research, one of the key issues of which is preventing the agent’s responses from sounding repetitive. Targeting this issue, we propose a new task, known as redundancy localization, which aims to pinpoint semantic overlap between text passages. To help address it systematically, we formalize the task, prepare a public dataset with fine-grained redundancy labels, and propose a model utilizing a weak training signal defined over the results of a passage-retrieval system on web texts. The proposed model demonstrates superior performance compared to a state-of-the-art entailment model and yields encouraging results when applied to a real-world dialogue.</abstract>
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%0 Conference Proceedings
%T Redundancy Localization for the Conversationalization of Unstructured Responses
%A Krause, Sebastian
%A Kozhevnikov, Mikhail
%A Malmi, Eric
%A Pighin, Daniele
%Y Jokinen, Kristiina
%Y Stede, Manfred
%Y DeVault, David
%Y Louis, Annie
%S Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
%D 2017
%8 August
%I Association for Computational Linguistics
%C Saarbrücken, Germany
%F krause-etal-2017-redundancy
%X Conversational agents offer users a natural-language interface to accomplish tasks, entertain themselves, or access information. Informational dialogue is particularly challenging in that the agent has to hold a conversation on an open topic, and to achieve a reasonable coverage it generally needs to digest and present unstructured information from textual sources. Making responses based on such sources sound natural and fit appropriately into the conversation context is a topic of ongoing research, one of the key issues of which is preventing the agent’s responses from sounding repetitive. Targeting this issue, we propose a new task, known as redundancy localization, which aims to pinpoint semantic overlap between text passages. To help address it systematically, we formalize the task, prepare a public dataset with fine-grained redundancy labels, and propose a model utilizing a weak training signal defined over the results of a passage-retrieval system on web texts. The proposed model demonstrates superior performance compared to a state-of-the-art entailment model and yields encouraging results when applied to a real-world dialogue.
%R 10.18653/v1/W17-5515
%U https://aclanthology.org/W17-5515
%U https://doi.org/10.18653/v1/W17-5515
%P 115-126

Markdown (Informal)

[Redundancy Localization for the Conversationalization of Unstructured Responses](https://aclanthology.org/W17-5515) (Krause et al., SIGDIAL 2017)

• Redundancy Localization for the Conversationalization of Unstructured Responses (Krause et al., SIGDIAL 2017)

ACL

• Sebastian Krause, Mikhail Kozhevnikov, Eric Malmi, and Daniele Pighin. 2017. Redundancy Localization for the Conversationalization of Unstructured Responses. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pages 115–126, Saarbrücken, Germany. Association for Computational Linguistics.