MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark

• URL: http://arxiv.org/abs/2008.09335v2
• Date: Wed, 27 Jan 2021 03:36:21 GMT
• Title: MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark
• Authors: Haoran Li, Abhinav Arora, Shuohui Chen, Anchit Gupta, Sonal Gupta, Yashar Mehdad
• Abstract summary: We present a new multilingual dataset, called MTOP, comprising of 100k annotated utterances in 6 languages across 11 domains. 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. 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.
• Score: 31.91964553419665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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