mlsanityData to 14 Oct 2026

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

arXiv 2304.13292 ↗First posted 26 Apr 2023cs.CLcited by 4

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Abstract (arXiv)

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

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 27 of its 46 references that are in the map. First posted on arXiv 26 Apr 2023.

Builds on

27 of 46 references.

References · 27

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15 more
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  14. 2021Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detectioncited by 22
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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2023Findings of the VarDial Evaluation Campaign 2023cited by 14
  2. 2024ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Fillercited by 5
  3. 2025Add Noise, Tasks, or Layers? MaiNLP at the VarDial 2025 Shared Task on Norwegian Dialectal Slot and Intent Detectioncited by 2
  4. 2024Task-Oriented Dialog Systems for the Senegalese Wolof Languagecited by 1