@inproceedings{lin-etal-2021-xpersona,
title = "{XP}ersona: Evaluating Multilingual Personalized Chatbot",
author = "Lin, Zhaojiang and
Liu, Zihan and
Winata, Genta Indra and
Cahyawijaya, Samuel and
Madotto, Andrea and
Bang, Yejin and
Ishii, Etsuko and
Fung, Pascale",
editor = "Papangelis, Alexandros and
Budzianowski, Pawe{\l} and
Liu, Bing and
Nouri, Elnaz and
Rastogi, Abhinav and
Chen, Yun-Nung",
booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4convai-1.10",
doi = "10.18653/v1/2021.nlp4convai-1.10",
pages = "102--112",
abstract = "Personalized dialogue systems are an essential step toward better human-machine interaction. Existing personalized dialogue agents rely on properly designed conversational datasets, which are mostly monolingual (e.g., English), which greatly limits the usage of conversational agents in other languages. In this paper, we propose a multi-lingual extension of Persona-Chat, namely XPersona. Our dataset includes persona conversations in six different languages other than English for evaluating multilingual personalized agents. We experiment with both multilingual and cross-lingual trained baselines and evaluate them against monolingual and translation-pipeline models using both automatic and human evaluation. Experimental results show that the multilingual trained models outperform the translation pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages. On the other hand, the state-of-the-art cross-lingual trained models achieve inferior performance to the other models, showing that cross-lingual conversation modeling is a challenging task. We hope that our dataset and baselines will accelerate research in multilingual dialogue systems.",
}
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<abstract>Personalized dialogue systems are an essential step toward better human-machine interaction. Existing personalized dialogue agents rely on properly designed conversational datasets, which are mostly monolingual (e.g., English), which greatly limits the usage of conversational agents in other languages. In this paper, we propose a multi-lingual extension of Persona-Chat, namely XPersona. Our dataset includes persona conversations in six different languages other than English for evaluating multilingual personalized agents. We experiment with both multilingual and cross-lingual trained baselines and evaluate them against monolingual and translation-pipeline models using both automatic and human evaluation. Experimental results show that the multilingual trained models outperform the translation pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages. On the other hand, the state-of-the-art cross-lingual trained models achieve inferior performance to the other models, showing that cross-lingual conversation modeling is a challenging task. We hope that our dataset and baselines will accelerate research in multilingual dialogue systems.</abstract>
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%0 Conference Proceedings
%T XPersona: Evaluating Multilingual Personalized Chatbot
%A Lin, Zhaojiang
%A Liu, Zihan
%A Winata, Genta Indra
%A Cahyawijaya, Samuel
%A Madotto, Andrea
%A Bang, Yejin
%A Ishii, Etsuko
%A Fung, Pascale
%Y Papangelis, Alexandros
%Y Budzianowski, Paweł
%Y Liu, Bing
%Y Nouri, Elnaz
%Y Rastogi, Abhinav
%Y Chen, Yun-Nung
%S Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F lin-etal-2021-xpersona
%X Personalized dialogue systems are an essential step toward better human-machine interaction. Existing personalized dialogue agents rely on properly designed conversational datasets, which are mostly monolingual (e.g., English), which greatly limits the usage of conversational agents in other languages. In this paper, we propose a multi-lingual extension of Persona-Chat, namely XPersona. Our dataset includes persona conversations in six different languages other than English for evaluating multilingual personalized agents. We experiment with both multilingual and cross-lingual trained baselines and evaluate them against monolingual and translation-pipeline models using both automatic and human evaluation. Experimental results show that the multilingual trained models outperform the translation pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages. On the other hand, the state-of-the-art cross-lingual trained models achieve inferior performance to the other models, showing that cross-lingual conversation modeling is a challenging task. We hope that our dataset and baselines will accelerate research in multilingual dialogue systems.
%R 10.18653/v1/2021.nlp4convai-1.10
%U https://aclanthology.org/2021.nlp4convai-1.10
%U https://doi.org/10.18653/v1/2021.nlp4convai-1.10
%P 102-112
Markdown (Informal)
[XPersona: Evaluating Multilingual Personalized Chatbot](https://aclanthology.org/2021.nlp4convai-1.10) (Lin et al., NLP4ConvAI 2021)
ACL
- Zhaojiang Lin, Zihan Liu, Genta Indra Winata, Samuel Cahyawijaya, Andrea Madotto, Yejin Bang, Etsuko Ishii, and Pascale Fung. 2021. XPersona: Evaluating Multilingual Personalized Chatbot. In Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI, pages 102–112, Online. Association for Computational Linguistics.