End-to-End Argument Mining as Biaffine Dependency Parsing

Non-neural approaches to argument mining (AM) are often pipelined and require heavy feature-engineering. In this paper, we propose a neural end-to-end approach to AM which is based on dependency parsing, in contrast to the current state-of-the-art which relies on relation extraction. Our biaffine AM dependency parser significantly outperforms the state-of-the-art, performing at F1 = 73.5% for component identification and F1 = 46.4% for relation identification. One of the advantages of treating AM as biaffine dependency parsing is the simple neural architecture that results. The idea of treating AM as dependency parsing is not new, but has previously been abandoned as it was lagging far behind the state-of-the-art. In a thorough analysis, we investigate the factors that contribute to the success of our model: the biaffine model itself, our representation for the dependency structure of arguments, different encoders in the biaffine model, and syntactic information additionally fed to the model. Our work demonstrates that dependency parsing for AM, an overlooked idea from the past, deserves more attention in the future.

[1]  Timothy Dozat,et al.  Simpler but More Accurate Semantic Dependency Parsing , 2018, ACL.

[2]  Eduard H. Hovy,et al.  End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF , 2016, ACL.

[3]  Noah A. Smith,et al.  Transition-Based Dependency Parsing with Stack Long Short-Term Memory , 2015, ACL.

[4]  Fernando Pereira,et al.  Non-Projective Dependency Parsing using Spanning Tree Algorithms , 2005, HLT.

[5]  Karin Baier,et al.  The Uses Of Argument , 2016 .

[6]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[7]  Claire Cardie,et al.  A Corpus of eRulemaking User Comments for Measuring Evaluability of Arguments , 2018, LREC.

[8]  Manfred Stede,et al.  From Argument Diagrams to Argumentation Mining in Texts: A Survey , 2013, Int. J. Cogn. Informatics Nat. Intell..

[9]  Hal Daumé,et al.  Deep Unordered Composition Rivals Syntactic Methods for Text Classification , 2015, ACL.

[10]  Anders Søgaard,et al.  Deep multi-task learning with low level tasks supervised at lower layers , 2016, ACL.

[11]  Joakim Nivre,et al.  A Transition-Based System for Joint Part-of-Speech Tagging and Labeled Non-Projective Dependency Parsing , 2012, EMNLP.

[12]  Jean H. M. Wagemans,et al.  An annotated corpus of argument schemes in US election debates , 2019 .

[13]  Timothy Dozat,et al.  Deep Biaffine Attention for Neural Dependency Parsing , 2016, ICLR.

[14]  Chris Reed,et al.  Proceedings of the First Workshop on Argumentation Mining , 2014 .

[15]  J. Dessalles,et al.  Arguing, reasoning, and the interpersonal (cultural) functions of human consciousness , 2011, Behavioral and Brain Sciences.

[16]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[17]  Claire Cardie,et al.  Proceedings of the 2nd Workshop on Argumentation Mining , 2015 .

[18]  M. Dwass.  Modified Randomization Tests for Nonparametric Hypotheses , 1957 .

[19]  Vincent Ng,et al.  End-to-End Argumentation Mining in Student Essays , 2016, NAACL.

[20]  Ilya Sutskever,et al.  Language Models are Unsupervised Multitask Learners , 2019 .

[21]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[22]  Danqi Chen,et al.  A Fast and Accurate Dependency Parser using Neural Networks , 2014, EMNLP.

[23]  Chris Reed,et al.  Argument Mining: A Survey , 2020, Computational Linguistics.

[24]  Iryna Gurevych,et al.  Parsing Argumentation Structures in Persuasive Essays , 2016, CL.

[25]  Iryna Gurevych,et al.  Neural End-to-End Learning for Computational Argumentation Mining , 2017, ACL.

[26]  Makoto Miwa,et al.  End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures , 2016, ACL.

[27]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[28]  Claire Cardie,et al.  Argument Mining with Structured SVMs and RNNs , 2017, ACL.

[29]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[30]  Hiroaki Ozaki,et al.  Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization , 2020, ACL.

[31]  Eliyahu Kiperwasser,et al.  Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations , 2016, TACL.

[32]  F. V. Eemeren,et al.  Argumentation: Analysis and Evaluation , 2016 .

[33]  Katsuhide Fujita,et al.  Syntactic Graph Convolution in Multi-Task Learning for Identifying and Classifying the Argument Component , 2019, 2019 IEEE 13th International Conference on Semantic Computing (ICSC).

[34]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[35]  Iryna Gurevych,et al.  Argumentation Mining in User-Generated Web Discourse , 2016, CL.

[36]  Timothy Dozat,et al.  Universal Dependency Parsing from Scratch , 2019, CoNLL.