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With a growing number of BERTology work analyzing different components of pre-trained language models, we extend this line of research through an in-depth analysis of discourse information in pre-trained and fine-tuned language models.
Docbert: Bert for document classification
Ashutosh Adhikari, Achyudh Ram, Raphael Tang, and Jimmy Lin. 2019 · 1904
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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
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Compressive transformers for long-range sequence modelling
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Rhetorical structure theory: Toward a functional theory of text organization
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Three new probabilistic models for dependency parsing: An exploration
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Reformer: The efficient transformer
Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya. 2020 · 2001
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RST discourse treebank
Lynn Carlson, Mary Ellen Okurowski, and Daniel Marcu. 2002 · 2002
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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020 · 2004
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The penn discourse treebank 2.0
Rashmi Prasad, Nikhil Dinesh, Alan Lee, Eleni Miltsakaki, Livio Robaldo, Aravind Joshi, and Bonnie Webber. 2008 · 2008
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Hilda: A discourse parser using support vector machine classification
Hugo Hernault, Helmut Prendinger, Mitsuru Ishizuka, et al. 2010 · 2010
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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Jointly modeling aspects, ratings and sentiments for movie recommendation (jmars)
Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J Smola, Jing Jiang, and Chong Wang. 2014 · 2014
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Representation learning for text-level discourse parsing
Yangfeng Ji and Jacob Eisenstein. 2014 · 2014
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Speech and language processing , volume 3
Dan Jurafsky and James H Martin. 2014 · 2014
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Text-level discourse dependency parsing
Sujian Li, Liang Wang, Ziqiang Cao, and Wenjie Li. 2014 · 2014
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Better document-level sentiment analysis from rst discourse parsing
Parminder Bhatia, Yangfeng Ji, and Jacob Eisenstein. 2015 · 2015
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Using rhetorical structure in sentiment analysis
Alexander Hogenboom, Flavius Frasincar, Franciska De Jong, and Uzay Kaymak. 2015 · 2015
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Codra: A novel discriminative framework for rhetorical analysis
Shafiq Joty, Giuseppe Carenini, and Raymond T Ng. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Abstractive text summarization using sequence-to-sequence RNNs and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Çağlar Gu̇lçehre, and Bing Xiang. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Neural discourse structure for text categorization
Yangfeng Ji and Noah A Smith. 2017 · 2017
Cited alongside, same era.
How much progress have we made on rst discourse parsing? a replication study of recent results on the rst-dt
Mathieu Morey, Philippe Muller, and Nicholas Asher. 2017 · 2017
Cited alongside, same era.
Exploring joint neural model for sentence level discourse parsing and sentiment analysis
Bita Nejat, Giuseppe Carenini, and Raymond Ng. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
A two-stage parsing method for text-level discourse analysis
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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Coreference for discourse parsing: A neural approach
Grigorii Guz and Giuseppe Carenini. 2020 · 2020
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Unleashing the power of neural discourse parsers-a context and structure aware approach using large scale pretraining
Grigorii Guz, Patrick Huber, and Giuseppe Carenini. 2020 · 2020
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Mega rst discourse treebanks with structure and nuclearity from scalable distant sentiment supervision
Patrick Huber and Giuseppe Carenini. 2020 · 2020
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Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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Yizhong Wang, Sujian Li, and Houfeng Wang. 2017 · 2017
Cited alongside, same era.
The GUM corpus: Creating multilayer resources in the classroom
Amir Zeldes. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Cited alongside, same era.
An analysis of encoder representations in transformer-based machine translation
Alessandro Raganato and Jörg Tiedemann. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G Carbonell, Quoc Le, and Ruslan Salakhutdinov. 2019 · 2019
Cited alongside, same era.
Top-down rst parsing utilizing granularity levels in documents
Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, and Masaaki Nagata. 2020 · 2020
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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
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Asking without telling: Exploring latent ontologies in contextual representations
Julian Michael, Jan A. Botha, and Ian Tenney. 2020 · 2020
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A primer in bertology: What we know about how bert works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020 · 2020
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Perturbed masking: Parameter-free probing for analyzing and interpreting bert
Zhiyong Wu, Yun Chen, Ben Kao, and Qun Liu. 2020 · 2020
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020 · 2020
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Examining the rhetorical capacities of neural language models
Zining Zhu, Chuer Pan, Mohamed Abdalla, and Frank Rudzicz. 2020 · 2020
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Discourse probing of pretrained language models
Fajri Koto, Jey Han Lau, and Timothy Baldwin. 2021a · 2021
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Probing multilingual language models for discourse
Murathan Kurfalı and Robert Östling. 2021 · 2021
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Rst parsing from scratch
Thanh-Tung Nguyen, Xuan-Phi Nguyen, Shafiq Joty, and Xiaoli Li. 2021 · 2021
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Domain-matched pre-training tasks for dense retrieval
Barlas Oğuz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Wen-tau Yih, Sonal Gupta, et al. 2021 · 2021
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Pragmatic competence of pre-trained language models through the lens of discourse connectives
Lalchand Pandia, Yan Cong, and Allyson Ettinger. 2021 · 2021
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Predicting discourse trees from transformer-based neural summarizers
Wen Xiao, Patrick Huber, and Giuseppe Carenini. 2021b · 2021
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Demoting the lead bias in news summarization via alternating adversarial learning
Linzi Xing, Wen Xiao, and Giuseppe Carenini. 2021 · 2021
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