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Adopting Semantic Similarity for Utterance Candidates Discovery from Human-to-Human Dialogue Corpus

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9577)

Abstract

Having appropriate utterances in response to user input is an essential element to sustain the flow of conversation in dialogue systems, and a basic and fundamental element for maintaining such conversation coherence is an adjacency pair. To find appropriate candidates for adjacency pairs completion, and thus contribute to avoiding conversational disrupt in casual chatbot systems, we suggest an approach that utilizes human-to-human chat logs, and combines standard Information Retrieval methods and semantic similarity measures based on distributed word representations. The experimental results show the approach improves the quality of utterance pairs compared to standard IR-based methods.

Keywords

  • Semantic similarity
  • Utterance candidates
  • Continuous word embeddings
  • Word2vec
  • Human-to-human dialogue corpus

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Notes

  1. 1.

    As described in Sect. 3.

  2. 2.

    CyberAgent, Inc. https://www.cyberagent.co.jp/en/.

  3. 3.

    Apache Lucene is used for the experiments.

  4. 4.

    https://pypi.python.org/pypi/gensim implementation.

  5. 5.

    We don’t consider an extra crowdsourcing step proposed in the paper though.

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Correspondence to Roman Y. Shtykh .

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Shtykh, R.Y., Makita, M. (2016). Adopting Semantic Similarity for Utterance Candidates Discovery from Human-to-Human Dialogue Corpus. In: Quesada, J., Martín Mateos, FJ., Lopez-Soto, T. (eds) Future and Emergent Trends in Language Technology. FETLT 2015. Lecture Notes in Computer Science(), vol 9577. Springer, Cham. https://doi.org/10.1007/978-3-319-33500-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-33500-1_12

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