Large Language Models for Expansion of Spoken Language Understanding Systems to New Languages

S. Schuster, S. Gupta, R. Shah, and M. Lewis, “Cross-lingual transfer learning for multilingual task oriented dialog,” in Proceedings of NAACL-HLT , 2019, pp. 3795–3805

2019

Earlier work this paper cites.

S. Garg, S. Peitz, U. Nallasamy, and M. Paulik, “Jointly learning to align and translate with transformer models,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , 2019, pp. 4453–4462

2019

Earlier work this paper cites.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in North American Chapter of the Association for Computational Linguistics , 2019

2019

Earlier work this paper cites.

Q. Chen, Z. Zhuo, and W. Wang, “Bert for joint intent classification and slot filling,” ArXiv , vol. abs/1902.10909, 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:67855472

Original

2019

Earlier work this paper cites.

T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020

2020

Earlier work this paper cites.

W. Xu, B. Haider, and S. Mansour, “End-to-end slot alignment and recognition for cross-lingual nlu,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2020, pp. 5052–5063

2020

Earlier work this paper cites.

E. Bastianelli, A. Vanzo, P. Swietojanski, and V. Rieser, “Slurp: A spoken language understanding resource package,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2020, pp. 7252–7262

2020

Earlier work this paper cites.

S. Rongali, L. Soldaini, E. Monti, and W. Hamza, “Don’t parse, generate! a sequence to sequence architecture for task-oriented semantic parsing,” in Proceedings of The Web Conference 2020 , 2020, pp. 2962–2968

2020

Cited alongside, same era.

Q. Zhu, H. Khan, S. Soltan, S. Rawls, and W. Hamza, “Don’t parse, insert: Multilingual semantic parsing with insertion based decoding,” in Proceedings of the 24th Conference on Computational Natural Language Learning , 2020, pp. 496–506

2020

Cited alongside, same era.

M. Nicosia, Z. Qu, and Y. Altun, “Translate & fill: Improving zero-shot multilingual semantic parsing with synthetic data,” in Findings of the Association for Computational Linguistics: EMNLP 2021 , 2021, pp. 3272–3284

2021

Cited alongside, same era.

E. J. Hu, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen et al. , “Lora: Low-rank adaptation of large language models,” in International Conference on Learning Representations , 2021

2021

Cited alongside, same era.

H. Weld, X. Huang, S. Long, J. Poon, and S. C. Han, “A survey of joint intent detection and slot filling models in natural language understanding,” ACM Computing Surveys , vol. 55, no. 8, pp. 1–38, 2022

2022

Later among the works it cites.

X. Cheng, W. Xu, Z. Yao, Z. Zhu, Y. Li, H. Li, and Y. Zou, “Fc-mtlf: a fine-and coarse-grained multi-task learning framework for cross-lingual spoken language understanding,” in Proc. of Interspeech , vol. 2, 2023

2023

Later among the works it cites.

Y. Chen, C. Jiang, A. Ritter, and W. Xu, “Frustratingly easy label projection for cross-lingual transfer,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Findings) , 2023

2023

Later among the works it cites.

J. FitzGerald, C. Hench, C. Peris, S. Mackie, K. Rottmann, A. Sanchez, A. Nash, L. Urbach, V. Kakarala, R. Singh et al. , “Massive: A 1m-example multilingual natural language understanding dataset with 51 typologically-diverse languages,” in The 61st Annual Meeting Of The Association For Computational Linguistics , 2023