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We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search.
Efficient algorithms for agglomerative hierarchical clustering methods
William H. E. Day and Herbert Edelsbrunner. 1984 · 1984
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Natural Language Arguments: A Combined Approach
Elena Cabrio and Serena Villata. 2012 · 2012
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Learning whom to trust with MACE
Dirk Hovy, Taylor Berg-Kirkpatrick, Ashish Vaswani, and Eduard Hovy. 2013 · 2013
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Efficient Estimation of Word Representations in Vector Space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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Context dependent claim detection
Ran Levy, Yonatan Bilu, Daniel Hershcovich, Ehud Aharoni, and Noam Slonim. 2014 · 2014
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GloVe: Global Vectors for Word Representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Identifying Argumentative Discourse Structures in Persuasive Essays
Christian Stab and Iryna Gurevych. 2014 · 2014
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Identifying prominent arguments in online debates using semantic textual similarity
Filip Boltužić and Jan Šnajder. 2015 · 2015
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Show me your evidence - an automatic method for context dependent evidence detection
Ruty Rinott, Lena Dankin, Carlos Alzate Perez, Mitesh M. Khapra, Ehud Aharoni, and Noam Slonim. 2015 · 2015
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Extraction and clustering of arguing expressions in contentious text
Amine Trabelsi and Osmar R Zaïane. 2015 · 2015
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Measuring the similarity of sentential arguments in dialogue
Amita Misra, Brian Ecker, and Marilyn A. Walker. 2016 · 2016
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Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
Cited alongside, same era.
Neural end-to-end learning for computational argumentation mining
Steffen Eger, Johannes Daxenberger, and Iryna Gurevych. 2017 · 2017
Cited alongside, same era.
Attention is all you need
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Nils Reimers and Iryna Gurevych. 2018 · 2018
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Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining
Eyal Shnarch, Carlos Alzate, Lena Dankin, Martin Gleize, Yufang Hou, Leshem Choshen, Ranit Aharonov, and Noam Slonim. 2018 · 2018
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ArgumenText: Searching for Arguments in Heterogeneous Sources
Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, and Iryna Gurevych. 2018a · 2018
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Argumentation quality assessment: Theory vs. practice
Henning Wachsmuth, Nona Naderi, Ivan Habernal, Yufang Hou, Graeme Hirst, Iryna Gurevych, and Benno Stein. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
Cross-topic argument mining from heterogeneous sources
Christian Stab, Tristan Miller, Benjamin Schiller, Pranav Rai, and Iryna Gurevych. 2018b · 2018
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Retrieval of the best counterargument without prior topic knowledge
Henning Wachsmuth, Shahbaz Syed, and Benno Stein. 2018 · 2018
Later among the works it cites.