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Transformer-based language models, more specifically BERT-based architectures have achieved state-of-the-art performance in many downstream tasks.
Roberta: A robustly optimized bert pretraining approach. arxiv 2019
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019) · 1907
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CTRL: A conditional transformer language model for controllable generation
Keskar, N. S., McCann, B., Varshney, L. R., Xiong, C., and Socher, R. (2019) · 1909
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Albert: A lite bert for self-supervised learning of language representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R. (2019) · 1909
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The woman worked as a babysitter: On biases in language generation
Sheng, E., Chang, K.-W., Natarajan, P., and Peng, N. (2019) · 1909
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Unsupervised cross-lingual representation learning at scale
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., and Stoyanov, V. (2019) · 1911
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Camembert: a tasty french language model
Martin, L., Muller, B., Suárez, P. J. O., Dupont, Y., Romary, L., de la Clergerie, É. V., Seddah, D., and Sagot, B. (2019) · 1911
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Orchid: Thai part-of-speech tagged corpus
Sornlertlamvanich, V., Charoenporn, T., and Isahara, H. (1997) · 1997
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
Lafferty, J., McCallum, A., and Pereira, F. C. (2001) · 2001
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Electra: Pre-training text encoders as discriminators rather than generators
Clark, K., Luong, M.-T., Le, Q. V., and Manning, C. D. (2020) · 2003
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Deberta: Decoding-enhanced bert with disentangled attention
He, P., Liu, X., Gao, J., and Chen, W. (2020) · 2006
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scb-mt-en-th-2020: A large english-thai parallel corpus
Lowphansirikul, L., Polpanumas, C., Rutherford, A. T., and Nutanong, S. (2020) · 2007
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Crfsuite: a fast implementation of conditional random fields, 2007
Okazaki, N. (2007) · 2007
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The annotation guideline of lst20 corpus
Boonkwan, P., Luantangsrisuk, V., Phaholphinyo, S., Kriengket, K., Leenoi, D., Phrombut, C., Boriboon, M., Kosawat, K., and Supnithi, T. (2020) · 2008
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Thai national corpus: a progress report
Aroonmanakun, W., Tansiri, K., and Nittayanuparp, P. (2009) · 2009
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Thai ner using crf model based on surface features
Tirasaroj, N. and Aroonmanakun, W. (2012) · 2011
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Extracting training data from large language models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. (2020) · 2012
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Parallel data, tools and interfaces in opus
Tiedemann, J. (2012) · 2012
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Baselines and bigrams: Simple, good sentiment and topic classification
Wang, S. I. and Manning, C. D. (2012) · 2012
Cited alongside, same era.
The AMARA corpus: Building parallel language resources for the educational domain
Glue: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R. (2018) · 2018
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Wongnai-corpus
Wongnai.com (2018) · 2018
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DeepCut: A Thai word tokenization library using Deep Neural Network
Kittinaradorn, R., Achakulvisut, T., Chaovavanich, K., Srithaworn, K., Chormai, P., Kaewkasi, C., Ruangrong, T., and Oparad, K. (2019) · 2019
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wannaphongcom/thai-ner: Thainer 1.3
Phatthiyaphaibun, W. (2019) · 2019
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Pythainlp/wisesight-sentiment: First release
Suriyawongkul, A., Chuangsuwanich, E., Chormai, P., and Polpanumas, C. (2019) · 2019
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Superglue: A stickier benchmark for general-purpose language understanding systems
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Abdelali, A., Guzman, F., Sajjad, H., and Vogel, S. (2014) · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. and Ba, J. (2014) · 2014
Cited alongside, same era.
OpenSubtitles2016: Extracting large parallel corpora from movie and TV subtitles
Lison, P. and Tiedemann, J. (2016) · 2016
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017) · 2017
Cited alongside, same era.
Universal language model fine-tuning for text classification
Howard, J. and Ruder, S. (2018) · 2018
Cited alongside, same era.
Subword regularization: Improving neural network translation models with multiple subword candidates
Kudo, T. (2018) · 2018
Cited alongside, same era.
SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Kudo, T. and Richardson, J. (2018) · 2018
Cited alongside, same era.
Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. (2019) · 2019
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Spanbert: Improving pre-training by representing and predicting spans
Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., and Levy, O. (2020) · 2020
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Domain adaptation of thai word segmentation models using stacked ensemble
Limkonchotiwat, P., Phatthiyaphaibun, W., Sarwar, R., Chuangsuwanich, E., and Nutanong, S. (2020) · 2020
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Stereoset: Measuring stereotypical bias in pretrained language models
Nadeem, M., Bethke, A., and Reddy, S. (2020) · 2020
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Crows-pairs: A challenge dataset for measuring social biases in masked language models
Nangia, N., Vania, C., Bhalerao, R., and Bowman, S. R. (2020) · 2020
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Pythainlp/pythainlp: Pythainlp 2.1.4
Phatthiyaphaibun, W., Chaovavanich, K., Polpanumas, C., Suriyawongkul, A., Lowphansirikul, L., and Chormai, P. (2020) · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J. (2020) · 2020
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thai2fit: Thai language implementation of ulmfit
Polpanumas, C. and Phatthiyaphaibun, W. (2021) · 2021
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