mteb/MTOPIntentClassification

Tasks

• text-classification

Citation text from the card

@inproceedings{li-etal-2021-mtop,
  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.},
  address = {Online},
  author = {Li, Haoran  and
Arora, Abhinav  and
Chen, Shuohui  and
Gupta, Anchit  and
Gupta, Sonal  and
Mehdad, Yashar},
  booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
  doi = {10.18653/v1/2021.eacl-main.257},
  e

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