Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining
for Task-Oriented Dialog
- URL: http://arxiv.org/abs/2205.10400v1
- Date: Fri, 20 May 2022 18:35:38 GMT
- Title: Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining
for Task-Oriented Dialog
- Authors: Chia-Chien Hung, Anne Lauscher, Ivan Vuli\'c, Simone Paolo Ponzetto,
Goran Glava\v{s}
- Abstract summary: Multi2WOZ dataset spans four typologically diverse languages: Chinese, German, Arabic, and Russian.
We introduce a new framework for multilingual conversational specialization of pretrained language models (PrLMs) that aims to facilitate cross-lingual transfer for arbitrary downstream TOD tasks.
Our experiments show that, in most setups, the best performance entails the combination of (I) conversational specialization in the target language and (ii) few-shot transfer for the concrete TOD task.
- Score: 67.20796950016735
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Research on (multi-domain) task-oriented dialog (TOD) has predominantly
focused on the English language, primarily due to the shortage of robust TOD
datasets in other languages, preventing the systematic investigation of
cross-lingual transfer for this crucial NLP application area. In this work, we
introduce Multi2WOZ, a new multilingual multi-domain TOD dataset, derived from
the well-established English dataset MultiWOZ, that spans four typologically
diverse languages: Chinese, German, Arabic, and Russian. In contrast to
concurrent efforts, Multi2WOZ contains gold-standard dialogs in target
languages that are directly comparable with development and test portions of
the English dataset, enabling reliable and comparative estimates of
cross-lingual transfer performance for TOD. We then introduce a new framework
for multilingual conversational specialization of pretrained language models
(PrLMs) that aims to facilitate cross-lingual transfer for arbitrary downstream
TOD tasks. Using such conversational PrLMs specialized for concrete target
languages, we systematically benchmark a number of zero-shot and few-shot
cross-lingual transfer approaches on two standard TOD tasks: Dialog State
Tracking and Response Retrieval. Our experiments show that, in most setups, the
best performance entails the combination of (I) conversational specialization
in the target language and (ii) few-shot transfer for the concrete TOD task.
Most importantly, we show that our conversational specialization in the target
language allows for an exceptionally sample-efficient few-shot transfer for
downstream TOD tasks.
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