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    <abstract>Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets. Available datasets suffer from several shortcomings: a) they contain few languages b) they contain small amounts of labeled examples per language c) they are based on the simple intent and slot detection paradigm for non-compositional queries. In this paper, we present a new multilingual dataset, called MTOP, comprising of 100k annotated utterances in 6 languages across 11 domains. We use this dataset and other publicly available datasets to conduct a comprehensive benchmarking study on using various state-of-the-art multilingual pre-trained models for task-oriented semantic parsing. We achieve an average improvement of +6.3 points on Slot F1 for the two existing multilingual datasets, over best results reported in their experiments. Furthermore, we demonstrate strong zero-shot performance using pre-trained models combined with automatic translation and alignment, and a proposed distant supervision method to reduce the noise in slot label projection.</abstract>
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%0 Conference Proceedings
%T MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark
%A Li, Haoran
%A Arora, Abhinav
%A Chen, Shuohui
%A Gupta, Anchit
%A Gupta, Sonal
%A Mehdad, Yashar
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F li-etal-2021-mtop
%X Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets. Available datasets suffer from several shortcomings: a) they contain few languages b) they contain small amounts of labeled examples per language c) they are based on the simple intent and slot detection paradigm for non-compositional queries. In this paper, we present a new multilingual dataset, called MTOP, comprising of 100k annotated utterances in 6 languages across 11 domains. We use this dataset and other publicly available datasets to conduct a comprehensive benchmarking study on using various state-of-the-art multilingual pre-trained models for task-oriented semantic parsing. We achieve an average improvement of +6.3 points on Slot F1 for the two existing multilingual datasets, over best results reported in their experiments. Furthermore, we demonstrate strong zero-shot performance using pre-trained models combined with automatic translation and alignment, and a proposed distant supervision method to reduce the noise in slot label projection.
%R 10.18653/v1/2021.eacl-main.257
%U https://aclanthology.org/2021.eacl-main.257
%U https://doi.org/10.18653/v1/2021.eacl-main.257
%P 2950-2962

Markdown (Informal)

[MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark](https://aclanthology.org/2021.eacl-main.257) (Li et al., EACL 2021)

• MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark (Li et al., EACL 2021)

ACL

• Haoran Li, Abhinav Arora, Shuohui Chen, Anchit Gupta, Sonal Gupta, and Yashar Mehdad. 2021. MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2950–2962, Online. Association for Computational Linguistics.