@inproceedings{razumovskaia-etal-2022-natural,
title = "Natural Language Processing for Multilingual Task-Oriented Dialogue",
author = "Razumovskaia, Evgeniia and
Glava{\v{s}}, Goran and
Majewska, Olga and
Ponti, Edoardo and
Vuli{\'c}, Ivan",
editor = "Benotti, Luciana and
Okazaki, Naoaki and
Scherrer, Yves and
Zampieri, Marcos",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-tutorials.8",
doi = "10.18653/v1/2022.acl-tutorials.8",
pages = "44--50",
abstract = "Recent advances in deep learning have also enabled fast progress in the research of task-oriented dialogue (ToD) systems. However, the majority of ToD systems are developed for English and merely a handful of other widely spoken languages, e.g., Chinese and German. This hugely limits the global reach and, consequently, transformative socioeconomic potential of such systems. In this tutorial, we will thus discuss and demonstrate the importance of (building) multilingual ToD systems, and then provide a systematic overview of current research gaps, challenges and initiatives related to multilingual ToD systems, with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP. The tutorial will aim to provide answers or shed new light to the following questions: a) Why are multilingual dialogue systems so hard to build: what makes multilinguality for dialogue more challenging than for other NLP applications and tasks? b) What are the best existing methods and datasets for multilingual and cross-lingual (task-oriented) dialog systems? How are (multilingual) ToD systems usually evaluated? c) What are the promising future directions for multilingual ToD research: where can one draw inspiration from related NLP areas and tasks?",
}
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<abstract>Recent advances in deep learning have also enabled fast progress in the research of task-oriented dialogue (ToD) systems. However, the majority of ToD systems are developed for English and merely a handful of other widely spoken languages, e.g., Chinese and German. This hugely limits the global reach and, consequently, transformative socioeconomic potential of such systems. In this tutorial, we will thus discuss and demonstrate the importance of (building) multilingual ToD systems, and then provide a systematic overview of current research gaps, challenges and initiatives related to multilingual ToD systems, with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP. The tutorial will aim to provide answers or shed new light to the following questions: a) Why are multilingual dialogue systems so hard to build: what makes multilinguality for dialogue more challenging than for other NLP applications and tasks? b) What are the best existing methods and datasets for multilingual and cross-lingual (task-oriented) dialog systems? How are (multilingual) ToD systems usually evaluated? c) What are the promising future directions for multilingual ToD research: where can one draw inspiration from related NLP areas and tasks?</abstract>
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%0 Conference Proceedings
%T Natural Language Processing for Multilingual Task-Oriented Dialogue
%A Razumovskaia, Evgeniia
%A Glavaš, Goran
%A Majewska, Olga
%A Ponti, Edoardo
%A Vulić, Ivan
%Y Benotti, Luciana
%Y Okazaki, Naoaki
%Y Scherrer, Yves
%Y Zampieri, Marcos
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F razumovskaia-etal-2022-natural
%X Recent advances in deep learning have also enabled fast progress in the research of task-oriented dialogue (ToD) systems. However, the majority of ToD systems are developed for English and merely a handful of other widely spoken languages, e.g., Chinese and German. This hugely limits the global reach and, consequently, transformative socioeconomic potential of such systems. In this tutorial, we will thus discuss and demonstrate the importance of (building) multilingual ToD systems, and then provide a systematic overview of current research gaps, challenges and initiatives related to multilingual ToD systems, with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP. The tutorial will aim to provide answers or shed new light to the following questions: a) Why are multilingual dialogue systems so hard to build: what makes multilinguality for dialogue more challenging than for other NLP applications and tasks? b) What are the best existing methods and datasets for multilingual and cross-lingual (task-oriented) dialog systems? How are (multilingual) ToD systems usually evaluated? c) What are the promising future directions for multilingual ToD research: where can one draw inspiration from related NLP areas and tasks?
%R 10.18653/v1/2022.acl-tutorials.8
%U https://aclanthology.org/2022.acl-tutorials.8
%U https://doi.org/10.18653/v1/2022.acl-tutorials.8
%P 44-50
Markdown (Informal)
[Natural Language Processing for Multilingual Task-Oriented Dialogue](https://aclanthology.org/2022.acl-tutorials.8) (Razumovskaia et al., ACL 2022)
ACL
- Evgeniia Razumovskaia, Goran Glavaš, Olga Majewska, Edoardo Ponti, and Ivan Vulić. 2022. Natural Language Processing for Multilingual Task-Oriented Dialogue. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts, pages 44–50, Dublin, Ireland. Association for Computational Linguistics.