Fetching the paper…
Reading the bibliography…
Though the pre-trained contextualized language model (PrLM) has made a significant impact on NLP, training PrLMs in languages other than English can be impractical for two reasons: other languages often lack corpora sufficient for training powerful PrLMs, and because of the commonalities among human languages, computationally expensive PrLM training for different languages is somewhat redundant.
M. Zhang, Y. Liu, H. Luan, and M. Sun, “Adversarial training for unsupervised bilingual lexicon induction,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Vancouver, Canada: Association for Computational Linguistics, Jul. 2017, pp. 1959–1970. [Online]. Available: https://www.aclweb.org/anthology/P17-1179
1970
Earlier work this paper cites.
D. Zeman, J. Hajič, M. Popel, M. Potthast, M. Straka, F. Ginter, J. Nivre, and S. Petrov, “CoNLL 2018 shared task: Multilingual parsing from raw text to Universal Dependencies,” in Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies . Brussels, Belgium: Association for Computational Linguistics, Oct. 2018, pp. 1–21. [Online]. Available: https://www.aclweb.org/anthology/K18-2001
2001
Earlier work this paper cites.
N. Chomsky, Syntactic structures . Walter de Gruyter, 2002
2002
Earlier work this paper cites.
D. McClosky, E. Charniak, and M. Johnson, “Automatic domain adaptation for parsing,” in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics . Los Angeles, California: Association for Computational Linguistics, Jun. 2010, pp. 28–36. [Online]. Available: https://www.aclweb.org/anthology/N10-1004
2010
Earlier work this paper cites.
A. Rush, R. Reichart, M. Collins, and A. Globerson, “Improved parsing and POS tagging using inter-sentence consistency constraints,” in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning . Jeju Island, Korea: Association for Computational Linguistics, Jul. 2012, pp. 1434–1444. [Online]. Available: https://www.aclweb.org/anthology/D12-1131
2012
Earlier work this paper cites.
M. Schuster and K. Nakajima, “Japanese and korean voice search,” in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2012, pp. 5149–5152
2012
Earlier work this paper cites.
J. Tiedemann, “Parallel data, tools and interfaces in OPUS,” in Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12) . Istanbul, Turkey: European Language Resources Association (ELRA), May 2012, pp. 2214–2218. [Online]. Available: http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
2012
Earlier work this paper cites.
T. Schnabel and H. Schütze, “Towards robust cross-domain domain adaptation for part-of-speech tagging,” in Proceedings of the Sixth International Joint Conference on Natural Language Processing . Nagoya, Japan: Asian Federation of Natural Language Processing, Oct. 2013, pp. 198–206. [Online]. Available: https://www.aclweb.org/anthology/I13-1023
2013
Earlier work this paper cites.
C. Dyer, V. Chahuneau, and N. A. Smith, “A simple, fast, and effective reparameterization of IBM model 2,” in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Atlanta, Georgia: Association for Computational Linguistics, Jun. 2013, pp. 644–648. [Online]. Available: https://www.aclweb.org/anthology/N13-1073
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” 2015
2015
Earlier work this paper cites.
B. Zoph, D. Yuret, J. May, and K. Knight, “Transfer learning for low-resource neural machine translation,” 2016
2016
Earlier work this paper cites.
M. Ziemski, M. Junczys-Dowmunt, and B. Pouliquen, “The United Nations parallel corpus v1.0,” in Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16) . Portorož, Slovenia: European Language Resources Association (ELRA), May 2016, pp. 3530–3534. [Online]. Available: https://www.aclweb.org/anthology/L16-1561
2016
Earlier work this paper cites.
P. Lison and J. Tiedemann, “OpenSubtitles2016: Extracting large parallel corpora from movie and TV subtitles,” in Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16) . Portorož, Slovenia: European Language Resources Association (ELRA), May 2016, pp. 923–929. [Online]. Available: https://www.aclweb.org/anthology/L16-1147
2016
Earlier work this paper cites.
T. Dozat and C. D. Manning, “Deep biaffine attention for neural dependency parsing,” 2016
2016
Earlier work this paper cites.
R. Dabre, T. Nakagawa, and H. Kazawa, “An empirical study of language relatedness for transfer learning in neural machine translation,” in Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation . The National University (Phillippines), Nov. 2017, pp. 282–286. [Online]. Available: https://www.aclweb.org/anthology/Y17-1038
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, “Enriching word vectors with subword information,” Transactions of the Association for Computational Linguistics , vol. 5, pp. 135–146, 2017
2017
Earlier work this paper cites.
J. Gu, Y. Wang, Y. Chen, V. O. K. Li, and K. Cho, “Meta-learning for low-resource neural machine translation,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018, pp. 3622–3631. [Online]. Available: https://www.aclweb.org/anthology/D18-1398
2018
Cited alongside, same era.
T. Kocmi and O. Bojar, “Trivial transfer learning for low-resource neural machine translation,” in Proceedings of the Third Conference on Machine Translation: Research Papers . Brussels, Belgium: Association for Computational Linguistics, Oct. 2018, pp. 244–252. [Online]. Available: https://www.aclweb.org/anthology/W18-6325
2018
Cited alongside, same era.
G. Neubig and J. Hu, “Rapid adaptation of neural machine translation to new languages,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018, pp. 875–880. [Online]. Available: https://www.aclweb.org/anthology/D18-1103
2018
Cited alongside, same era.
Y. Cui, T. Liu, W. Che, L. Xiao, Z. Chen, W. Ma, S. Wang, and G. Hu, “A span-extraction dataset for Chinese machine reading comprehension,” 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) . Hong Kong, China: Association for Computational Linguistics, Nov. 2019, pp. 5883–5889. [Online]. Available: https://www.aclweb.org/anthology/D19-1600
2019
Later among the works it cites.
C. Zheng, M. Huang, and A. Sun, “ChID: A large-scale Chinese IDiom dataset for cloze test,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Florence, Italy: Association for Computational Linguistics, Jul. 2019, pp. 778–787. [Online]. Available: https://www.aclweb.org/anthology/P19-1075
2019
Later among the works it cites.
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
G. Lample, A. Conneau, M. Ranzato, L. Denoyer, and H. Jégou, “Word translation without parallel data,” in International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
R. Xu, Y. Yang, N. Otani, and Y. Wu, “Unsupervised cross-lingual transfer of word embedding spaces,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018, pp. 2465–2474. [Online]. Available: https://www.aclweb.org/anthology/D18-1268
2018
Cited alongside, same era.
J. Cai, S. He, Z. Li, and H. Zhao, “A full end-to-end semantic role labeler, syntactic-agnostic over syntactic-aware?” in Proceedings of the 27th International Conference on Computational Linguistics . Santa Fe, New Mexico, USA: Association for Computational Linguistics, Aug. 2018, pp. 2753–2765. [Online]. Available: https://www.aclweb.org/anthology/C18-1233
2018
Cited alongside, same era.
Y. Doval, J. Camacho-Collados, L. Espinosa-Anke, and S. Schockaert, “Improving cross-lingual word embeddings by meeting in the middle,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018, pp. 294–304. [Online]. Available: https://www.aclweb.org/anthology/D18-1027
2018
Cited alongside, same era.
E. Grave, P. Bojanowski, P. Gupta, A. Joulin, and T. Mikolov, “Learning word vectors for 157 languages,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) . Miyazaki, Japan: European Language Resources Association (ELRA), May 2018. [Online]. Available: https://www.aclweb.org/anthology/L18-1550
2018
Cited alongside, same era.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “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) . Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4171–4186. [Online]. Available: https://www.aclweb.org/anthology/N19-1423
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Y. Kim, Y. Gao, and H. Ney, “Effective cross-lingual transfer of neural machine translation models without shared vocabularies,” 2019
2019
Cited alongside, same era.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. de Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” 2019
2019
Cited alongside, same era.
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” in Advances in neural information processing systems , 2019, pp. 5753–5763
2019
Later among the works it cites.
A. F. Aji, N. Bogoychev, K. Heafield, and R. Sennrich, “In neural machine translation, what does transfer learning transfer?” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Online: Association for Computational Linguistics, Jul. 2020, pp. 7701–7710. [Online]. Available: https://www.aclweb.org/anthology/2020.acl-main.688
2020
Later among the works it cites.
V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,” 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
S. Cao, N. Kitaev, and D. Klein, “Multilingual alignment of contextual word representations,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=r1xCMyBtPS
2020
Later among the works it cites.
M. Artetxe, S. Ruder, and D. Yogatama, “On the cross-lingual transferability of monolingual representations,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Online: Association for Computational Linguistics, Jul. 2020, pp. 4623–4637. [Online]. Available: https://www.aclweb.org/anthology/2020.acl-main.421
2020
Later among the works it cites.
K. Tran, “From english to foreign languages: Transferring pre-trained language models,” 2020
2020
Later among the works it cites.
B. Ji, Z. Zhang, X. Duan, M. Zhang, B. Chen, and W. Luo, “Cross-lingual pre-training based transfer for zero-shot neural machine translation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 01, 2020, pp. 115–122
2020
Later among the works it cites.
2020
Later among the works it cites.
J. Tiedemann and S. Thottingal, “OPUS-MT — Building open translation services for the World,” in Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT) , Lisbon, Portugal, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
T. Q. Nguyen and D. Chiang, “Transfer learning across low-resource, related languages for neural machine translation,” in Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers) . Taipei, Taiwan: Asian Federation of Natural Language Processing, Nov. 2017, pp. 296–301. [Online]. Available: https://www.aclweb.org/anthology/I17-2050
2050
Closest in time.
Y. Qi, D. Sachan, M. Felix, S. Padmanabhan, and G. Neubig, “When and why are pre-trained word embeddings useful for neural machine translation?” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) . New Orleans, Louisiana: Association for Computational Linguistics, Jun. 2018, pp. 529–535. [Online]. Available: https://www.aclweb.org/anthology/N18-2084
2084
Closest in time.
A. Chronopoulou, C. Baziotis, and A. Potamianos, “An embarrassingly simple approach for transfer learning from pretrained language models,” 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) . Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 2089–2095. [Online]. Available: https://www.aclweb.org/anthology/N19-1213
2095
Closest in time.