Incorporating Typological Information into Pretrained Word Representations for Improved Performance

Author:

Vighnesh Subramaniam1

Affiliation:

1. Massachusetts Institute of Technology, USA

Publisher

ACM

Reference16 articles.

1. Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473(2014). Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473(2014).

2. Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2018 . Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805(2018). Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805(2018).

3. Timothy Dozat and Christopher D Manning. 2016. Deep biaffine attention for neural dependency parsing. arXiv preprint arXiv:1611.01734(2016). Timothy Dozat and Christopher D Manning. 2016. Deep biaffine attention for neural dependency parsing. arXiv preprint arXiv:1611.01734(2016).

4. To BERT or Not to BERT Dealing with Possible BERT Failures in an Entailment Task

5. Richard Futrell Ethan Wilcox Takashi Morita Peng Qian Miguel Ballesteros and Roger Levy. 2019. Neural language models as psycholinguistic subjects: Representations of syntactic state. arXiv preprint arXiv:1903.03260(2019). Richard Futrell Ethan Wilcox Takashi Morita Peng Qian Miguel Ballesteros and Roger Levy. 2019. Neural language models as psycholinguistic subjects: Representations of syntactic state. arXiv preprint arXiv:1903.03260(2019).

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