QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search

Author:

Xie Jian1ORCID,Liang Yidan2ORCID,Liu Jingping3ORCID,Xiao Yanghua1ORCID,Wu Baohua2ORCID,Ni Shenghua2ORCID

Affiliation:

1. Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China

2. Alibaba Group, Hangzhou, China

3. School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

Funder

Science and Technology Commission of Shanghai Municipality

Publisher

ACM

Reference32 articles.

1. Dogu Araci . 2019 . Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063 (2019). Dogu Araci. 2019. Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063 (2019).

2. Tom B Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell etal 2020. Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165 (2020). Tom B Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165 (2020).

3. Named Entity Recognition in Travel-Related Search Queries

4. Pre-Training With Whole Word Masking for Chinese BERT

5. Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding . In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171--4186. https://doi.org/10. 18653/v1/N19--1423 10.18653/v1 Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171--4186. https://doi.org/10.18653/v1/N19--1423

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