Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net?

Seyed Hossein Alavi, Anton Leuski, David Traum


Abstract
We compare two models for corpus-based selection of dialogue responses: one based on cross-language relevance with a cross-language LSTM model. Each model is tested on multiple corpora, collected from two different types of dialogue source material. Results show that while the LSTM model performs adequately on a very large corpus (millions of utterances), its performance is dominated by the cross-language relevance model for a more moderate-sized corpus (ten thousands of utterances).
Anthology ID:
2020.lrec-1.92
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
735–742
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.92
DOI:
Bibkey:
Cite (ACL):
Seyed Hossein Alavi, Anton Leuski, and David Traum. 2020. Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net?. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 735–742, Marseille, France. European Language Resources Association.
Cite (Informal):
Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net? (Alavi et al., LREC 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2020.lrec-1.92.pdf