Zero-Shot Slot and Intent Detection in Low-Resource Languages

Sang Yun Kwon, Gagan Bhatia, El Moatez Billah Nagoudi, Alcides Alcoba Inciarte, Muhammad Abdul-Mageed
4/26/2023
cs.CL

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Abstract

Intent detection and slot filling are critical tasks in spoken and natural language understanding for task-oriented dialog systems. In this work we describe our participation in the slot and intent detection for low-resource language varieties (SID4LR; Aepli et al. (2023)). We investigate the slot and intent detection (SID) tasks using a wide range of models and settings. Given the recent success of multitask-prompted finetuning of large language models, we also test the generalization capability of the recent encoder-decoder model mT0 (Muennighoff et al., 2022) on new tasks (i.e., SID) in languages they have never intentionally seen. We show that our best model outperforms the baseline by a large margin (up to +30 F1 points) in both SID tasks

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Cite this paper

@article{kwon2023zeroshot,
  title  = {Zero-Shot Slot and Intent Detection in Low-Resource Languages},
  author = {Sang Yun Kwon and Gagan Bhatia and El Moatez Billah Nagoudi and Alcides Alcoba Inciarte and Muhammad Abdul-Mageed},
  year   = {2023},
  eprint = {2304.13292},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.13292v1}
}

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