mlsanityData to 7 Oct 2026

Efficient slot labelling

arXiv 2401.09343 ↗First posted 17 Jan 2024cs.CLcited by 0

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Abstract (arXiv)

Slot labelling is an essential component of any dialogue system, aiming to find important arguments in every user turn. Common approaches involve large pre-trained language models (PLMs) like BERT or RoBERTa, but they face challenges such as high computational requirements and dependence on pre-training data. In this work, we propose a lightweight method which performs on par or better than the state-of-the-art PLM-based methods, while having almost 10x less trainable parameters. This makes it especially applicable for real-life industry scenarios.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 17 Jan 2024.

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15 of 22 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  3. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  4. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  5. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  6. 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 760
  7. 2020AdapterHub: A Framework for Adapting Transformerscited by 441
  8. 2020Efficient Intent Detection with Dual Sentence Encoderscited by 419
  9. 2020MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmarkcited by 156
  10. 2021Composable Sparse Fine-Tuning for Cross-Lingual Transfercited by 144
  11. 2017A Survey Of Cross-lingual Word Embedding Modelscited by 142
  12. 2020Rethinking embedding coupling in pre-trained language modelscited by 103
3 more
  1. 2019ConveRT: Efficient and Accurate Conversational Representations from Transformerscited by 95
  2. 2020End-to-End Slot Alignment and Recognition for Cross-Lingual NLUcited by 82
  3. 2020Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representationscited by 42

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