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Existing work on probing of pretrained language models (LMs) has predominantly focused on sentence-level syntactic tasks.
RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Assertions from discourse structure
William C. Mann and Sandra A. Thompson. 1986 · 1986
Earlier work this paper cites.
Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory
Lynn Carlson, Daniel Marcu, and Mary Ellen Okurovsky. 2001 · 2001
Earlier work this paper cites.
Modeling local coherence: An entity-based approach
Regina Barzilay and Mirella Lapata. 2008 · 2008
Earlier work this paper cites.
On the development of the RST Spanish Treebank
Iria da Cunha, Juan-Manuel Torres-Moreno, and Gerardo Sierra. 2011 · 2011
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
Earlier work this paper cites.
A linear-time bottom-up discourse parser with constraints and post-editing
Vanessa Wei Feng and Graeme Hirst. 2014 · 2014
Earlier work this paper cites.
Building Chinese discourse corpus with connective-driven dependency tree structure
Yancui Li, Wenhe Feng, Jing Sun, Fang Kong, and Guodong Zhou. 2014 · 2014
Earlier work this paper cites.
A corpus and cloze evaluation for deeper understanding of commonsense stories
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016 · 2016
Earlier work this paper cites.
Story comprehension for predicting what happens next
Snigdha Chaturvedi, Haoruo Peng, and Dan Roth. 2017 · 2017
Earlier work this paper cites.
Narrative modeling with memory chains and semantic supervision
Fei Liu, Trevor Cohn, and Timothy Baldwin. 2018 · 2018
Earlier work this paper cites.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Cited alongside, same era.
Tackling the story ending biases in the story cloze test
Rishi Sharma, James Allen, Omid Bakhshandeh, and Nasrin Mostafazadeh. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
A structural probe for finding syntax in word representations
John Hewitt and Christopher D. Manning. 2019 · 2019
Cited alongside, same era.
What does BERT learn about the structure of language?
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019 · 2019
Cited alongside, same era.
DisSent: Learning sentence representations from explicit discourse relations
The bottom-up evolution of representations in the transformer: A study with machine translation and language modeling objectives
Elena Voita, Rico Sennrich, and Ivan Titov. 2019 · 2019
Later among the works it cites.
The Potsdam commentary corpus 2.2: Extending annotations for shallow discourse parsing
Peter Bourgonje and Manfred Stede. 2020 · 2020
Later among the works it cites.
ELECTRA: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2020
Later among the works it cites.
What’s so special about BERT’s layers? A closer look at the NLP pipeline in monolingual and multilingual models
Wietse de Vries, Andreas van Cranenburgh, and Malvina Nissim. 2020 · 2020
Later among the works it cites.
IndoLEM and IndoBERT: A benchmark dataset and pre-trained language model for Indonesian NLP
Fajri Koto, Afshin Rahimi, Jey Han Lau, and Timothy Baldwin. 2020 · 2020
Later among the works it cites.
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Allen Nie, Erin Bennett, and Noah Goodman. 2019 · 2019
Cited alongside, same era.
Deep bidirectional transformers for relation extraction without supervision
Yannis Papanikolaou, Ian Roberts, and Andrea Pierleoni. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019 · 2019
Cited alongside, same era.
Next sentence prediction helps implicit discourse relation classification within and across domains
Wei Shi and Vera Demberg. 2019 · 2019
Cited alongside, same era.
Mining discourse markers for unsupervised sentence representation learning
Damien Sileo, Tim Van De Cruys, Camille Pradel, and Philippe Muller. 2019 · 2019
Cited alongside, same era.
Top-down discourse parsing via sequence labelling
Fajri Koto, Jey Han Lau, and Timothy Baldwin. to appear
Cited in the paper.
ALBERT: A lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
Later among the works it cites.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Later among the works it cites.
Asking without telling: Exploring latent ontologies in contextual representations
Julian Michael, Jan A. Botha, and Ian Tenney. 2020 · 2020
Later among the works it cites.
Predicting reference: What do language models learn about discourse models?
Shiva Upadhye, Leon Bergen, and Andrew Kehler. 2020 · 2020
Later among the works it cites.
Examining the rhetorical capacities of neural language models
Zining Zhu, Chuer Pan, Mohamed Abdalla, and Frank Rudzicz. 2020 · 2020
Later among the works it cites.