Aspect-Based Argument Mining

Computational Argumentation in general and Argument Mining in particular are important research fields. In previous works, many of the challenges to automatically extract and to some degree reason over natural language arguments were addressed. The tools to extract argument units are increasingly available and further open problems can be addressed. In this work, we are presenting the task of Aspect-Based Argument Mining (ABAM), with the essential subtasks of Aspect Term Extraction (ATE) and Nested Segmentation (NS). At the first instance, we create and release an annotated corpus with aspect information on the token-level. We consider aspects as the main point(s) argument units are addressing. This information is important for further downstream tasks such as argument ranking, argument summarization and generation, as well as the search for counter-arguments on the aspect-level. We present several experiments using stateof-the-art supervised architectures and demonstrate their performance for both of the subtasks. The annotated benchmark is available at https://github.com/trtm/ABAM.

[1]  Thomas Seidl,et al.  Relational and Fine-Grained Argument Mining , 2020, Datenbank-Spektrum.

[2]  Roy Bar-Haim,et al.  From Arguments to Key Points: Towards Automatic Argument Summarization , 2020, ACL.

[3]  Atsushi Fujii,et al.  A System for Summarizing and Visualizing Arguments in Subjective Documents: Toward Supporting Decision Making , 2006 .

[4]  Noam Slonim,et al.  A Benchmark Dataset for Automatic Detection of Claims and Evidence in the Context of Controversial Topics , 2014, ArgMining@ACL.

[5]  Iryna Gurevych,et al.  NLP Approaches to Computational Argumentation , 2016, ACL 2016.

[6]  Chris Reed,et al.  Decompositional Argument Mining: A General Purpose Approach for Argument Graph Construction , 2019, ACL.

[7]  Iryna Gurevych,et al.  Cross-topic Argument Mining from Heterogeneous Sources , 2018, EMNLP.

[8]  Frank Hutter,et al.  Decoupled Weight Decay Regularization , 2017, ICLR.

[9]  Kentaro Inui,et al.  An Empirical Study of Span Representations in Argumentation Structure Parsing , 2019, ACL.

[10]  Iryna Gurevych,et al.  Classification and Clustering of Arguments with Contextualized Word Embeddings , 2019, ACL.

[11]  Benjamin Roth,et al.  Domain adaptation for part-of-speech tagging of noisy user-generated text , 2019, NAACL-HLT.

[12]  Smaranda Muresan,et al.  AMPERSAND: Argument Mining for PERSuAsive oNline Discussions , 2019, EMNLP.

[13]  Georg Groh,et al.  Sequence Labeling: A Practical Approach , 2018, ArXiv.

[14]  Amita Misra,et al.  Measuring the Similarity of Sentential Arguments in Dialogue , 2016, SIGDIAL Conference.

[15]  Flavius Frasincar,et al.  A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical Attention , 2020, ICWE.

[16]  Akbar Karimi,et al.  Adversarial Training for Aspect-Based Sentiment Analysis with BERT , 2020, ArXiv.

[17]  Maria Leonor Pacheco,et al.  of the Association for Computational Linguistics: , 2001 .

[18]  Claire Cardie,et al.  Nested Named Entity Recognition Revisited , 2018, NAACL.

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

[20]  Georg Groh,et al.  Distant Supervision for Emotion Classification Task using emoji2emotion , 2018 .

[21]  Noam Slonim,et al.  Unsupervised corpus–wide claim detection , 2017, ArgMining@EMNLP.

[22]  Jan Hajic,et al.  Neural Architectures for Nested NER through Linearization , 2019, ACL.

[23]  Henning Wachsmuth,et al.  End-to-End Argumentation Knowledge Graph Construction , 2020, AAAI.

[24]  Amita Misra,et al.  Using Summarization to Discover Argument Facets in Online Idealogical Dialog , 2017, NAACL.

[25]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[26]  Haris Papageorgiou,et al.  SemEval-2016 Task 5: Aspect Based Sentiment Analysis , 2016, *SEMEVAL.

[27]  Andrew McCallum,et al.  An Introduction to Conditional Random Fields , 2010, Found. Trends Mach. Learn..

[28]  Benno Stein,et al.  SemEval-2018 Task 12: The Argument Reasoning Comprehension Task , 2018, *SEMEVAL.

[29]  A. Viera,et al.  Understanding interobserver agreement: the kappa statistic. , 2005, Family medicine.

[30]  Benno Stein,et al.  “PageRank” for Argument Relevance , 2017, EACL.

[31]  Suresh Manandhar,et al.  SemEval-2014 Task 4: Aspect Based Sentiment Analysis , 2014, *SEMEVAL.

[32]  Christopher D. Manning,et al.  Nested Named Entity Recognition , 2009, EMNLP.