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        <title>SimVecs: Similarity-Based Vectors for Utterance Representation in Conversational AI Systems</title>
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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            <namePart type="given">Mohit</namePart>
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    <abstract>Conversational AI systems are gaining a lot of attention recently in both industrial and scientific domains, providing a natural way of interaction between customers and adaptive intelligent systems. A key requirement in these systems is the ability to understand the user’s intent and provide adequate responses to them. One of the greatest challenges of language understanding (LU) services is efficient utterance (sentence) representation in vector space, which is an essential step for most ML tasks. In this paper, we propose a novel approach for generating vector space representations of utterances using pair-wise similarity metrics. The proposed approach uses only a few corpora to tune the weights of the similarity metric without relying on external general purpose ontologies. Our experiments confirm that the generated vectors can improve the performance of LU services in unsupervised, semi-supervised and supervised learning tasks.</abstract>
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%0 Conference Proceedings
%T SimVecs: Similarity-Based Vectors for Utterance Representation in Conversational AI Systems
%A Mahgoub, Ashraf
%A Shahin, Youssef
%A Mansour, Riham
%A Bagchi, Saurabh
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F mahgoub-etal-2019-simvecs
%X Conversational AI systems are gaining a lot of attention recently in both industrial and scientific domains, providing a natural way of interaction between customers and adaptive intelligent systems. A key requirement in these systems is the ability to understand the user’s intent and provide adequate responses to them. One of the greatest challenges of language understanding (LU) services is efficient utterance (sentence) representation in vector space, which is an essential step for most ML tasks. In this paper, we propose a novel approach for generating vector space representations of utterances using pair-wise similarity metrics. The proposed approach uses only a few corpora to tune the weights of the similarity metric without relying on external general purpose ontologies. Our experiments confirm that the generated vectors can improve the performance of LU services in unsupervised, semi-supervised and supervised learning tasks.
%R 10.18653/v1/K19-1066
%U https://aclanthology.org/K19-1066
%U https://doi.org/10.18653/v1/K19-1066
%P 708-717

Markdown (Informal)

[SimVecs: Similarity-Based Vectors for Utterance Representation in Conversational AI Systems](https://aclanthology.org/K19-1066) (Mahgoub et al., CoNLL 2019)

• SimVecs: Similarity-Based Vectors for Utterance Representation in Conversational AI Systems (Mahgoub et al., CoNLL 2019)

ACL

• Ashraf Mahgoub, Youssef Shahin, Riham Mansour, and Saurabh Bagchi. 2019. SimVecs: Similarity-Based Vectors for Utterance Representation in Conversational AI Systems. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 708–717, Hong Kong, China. Association for Computational Linguistics.