Fetching the paper…
Reading the bibliography…
Recent advances with language models (e.g.
Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., and Le, Q. V. (2019) · 1906
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019b) · 1907
Earlier work this paper cites.
Hardalov, M., Koychev, I., and Nakov, P. (2019) · 1908
Earlier work this paper cites.
Spamcop: A spam classification & organization program
Pantel, P., Lin, D., et al. (1998) · 1998
Earlier work this paper cites.
One-class svms for document classification
Manevitz, L. M. and Yousef, M. (2001) · 2001
Earlier work this paper cites.
Fquad: French question answering dataset
Martin d’Hoffschmidt, Wacim Belblidia, T. B. Q. H. M. V. (2020) · 2002
Earlier work this paper cites.
A statistical interpretation of term specificity and its application in retrieval
Jones, K. S. (2004) · 2004
Earlier work this paper cites.
Automatically constructing a corpus of sentential paraphrases
Dolan, W. B. and Brockett, C. (2005) · 2005
Earlier work this paper cites.
Semeval-2012 task 6: A pilot on semantic textual similarity
Agirre, E., Diab, M., Cer, D., and Gonzalez-Agirre, A. (2012) · 2012
Earlier work this paper cites.
The winograd schema challenge
Levesque, H., Davis, E., and Morgenstern, L. (2012) · 2012
Earlier work this paper cites.
Japanese and korean voice search
Schuster, M. and Nakajima, K. (2012) · 2012
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013) · 2013
Cited alongside, same era.
Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., and Potts, C. (2013) · 2013
Cited alongside, same era.
Distributed representations of sentences and documents
Le, Q. and Mikolov, T. (2014) · 2014
Cited alongside, same era.
Skip-thought vectors
Kiros, R., Zhu, Y., Salakhutdinov, R. R., Zemel, R., Urtasun, R., Torralba, A., and Fidler, S. (2015) · 2015
Cited alongside, same era.
Zero-resource translation with multi-lingual neural machine translation
Firat, O., Sankaran, B., Al-Onaizan, Y., Vural, F. T. Y., and Cho, K. (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
Later among the works it cites.
A broad-coverage challenge corpus for sentence understanding through inference
Williams, A., Nangia, N., and Bowman, S. R. (2017) · 2017
Later among the works it cites.
Multilingual extractive reading comprehension by runtime machine translation
Asai, A., Eriguchi, A., Hashimoto, K., and Tsuruoka, Y. (2018) · 2018
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018) · 2018
Later among the works it cites.
Universal language model fine-tuning for text classification
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Joulin, A., Grave, E., Bojanowski, P., and Mikolov, T. (2016) · 2016
Cited alongside, same era.
Ms marco: A human-generated machine reading comprehension dataset
Nguyen, T., Rosenberg, M., Song, X., Gao, J., Tiwary, S., Majumder, R., and Deng, L. (2016) · 2016
Cited alongside, same era.
Squad: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P. (2016) · 2016
Cited alongside, same era.
Google’s multilingual neural machine translation system: Enabling zero-shot translation
Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z., Thorat, N., Viégas, F., Wattenberg, M., Corrado, G., et al. (2017) · 2017
Cited alongside, same era.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Joshi, M., Choi, E., Weld, D. S., and Zettlemoyer, L. (2017) · 2017
Cited alongside, same era.
Race: Large-scale reading comprehension dataset from examinations
Lai, G., Xie, Q., Liu, H., Yang, Y., and Hovy, E. (2017) · 2017
Cited alongside, same era.
Xqa: A cross-lingual open-domain question answering dataset
Liu, J., Lin, Y., Liu, Z., and Sun, M. (2019a)
Cited in the paper.
Howard, J. and Ruder, S. (2018) · 2018
Later among the works it cites.
An efficient framework for learning sentence representations
Logeswaran, L. and Lee, H. (2018) · 2018
Later among the works it cites.
Deep contextualized word representations
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L. (2018) · 2018
Later among the works it cites.
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
Later among the works it cites.
Neural network acceptability judgments
Warstadt, A., Singh, A., and Bowman, S. R. (2018) · 2018
Later among the works it cites.
How multilingual is multilingual bert?
Pires, T., Schlinger, E., and Garrette, D. (2019) · 2019
Closest in time.