Dialogue Act (DA) tagging is crucial for spoken language understanding systems, as it provides a general representation of speakers’ intents, not bound to a particular dialogue system. Unfortunately, publicly available data sets with DA annotation are all based on different annotation schemes and thus incompatible with each other. Moreover, their schemes often do not cover all aspects necessary for open-domain human-machine interaction. In this paper, we propose a methodology to map several publicly available corpora to a subset of the ISO standard, in order to create a large task-independent training corpus for DA classification. We show the feasibility of using this corpus to train a domain-independent DA tagger testing it on out-of-domain conversational data, and argue the importance of training on multiple corpora to achieve robustness across different DA categories.

ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents / Mezza, Stefano; Cervone, Alessandra; Tortoreto, Giuliano; Stepanov, Evgeny A.; Riccardi, Giuseppe. - ELETTRONICO. - (2018), pp. 3539-3551. (Intervento presentato al convegno COLING tenutosi a Santa Fe nel 20th-26th August 2018).

ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents

Alessandra Cervone;Giuliano Tortoreto;Evgeny A. Stepanov;Giuseppe Riccardi
2018-01-01

Abstract

Dialogue Act (DA) tagging is crucial for spoken language understanding systems, as it provides a general representation of speakers’ intents, not bound to a particular dialogue system. Unfortunately, publicly available data sets with DA annotation are all based on different annotation schemes and thus incompatible with each other. Moreover, their schemes often do not cover all aspects necessary for open-domain human-machine interaction. In this paper, we propose a methodology to map several publicly available corpora to a subset of the ISO standard, in order to create a large task-independent training corpus for DA classification. We show the feasibility of using this corpus to train a domain-independent DA tagger testing it on out-of-domain conversational data, and argue the importance of training on multiple corpora to achieve robustness across different DA categories.
2018
Proceedings of the 27th International Conference on Computational Linguistics
Santa Fe
Association for Computational Linguistics
978-1-948087-50-6
Mezza, Stefano; Cervone, Alessandra; Tortoreto, Giuliano; Stepanov, Evgeny A.; Riccardi, Giuseppe
ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents / Mezza, Stefano; Cervone, Alessandra; Tortoreto, Giuliano; Stepanov, Evgeny A.; Riccardi, Giuseppe. - ELETTRONICO. - (2018), pp. 3539-3551. (Intervento presentato al convegno COLING tenutosi a Santa Fe nel 20th-26th August 2018).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/221154
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