Argument Relation Classification Using a Joint Inference Model

Hou, Yufang; Jochim, Charles

doi:10.18653/v1/w17-5107

Cited by 19 publications

(13 citation statements)

References 21 publications

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“…Similarly, some tasks can inform each other, for example, whereas Feng and Hirst (2011) showed that argument scheme instances could be classified given general relations between ADUs, Lawrence and Reed (2015) showed that such general relations can be determined by classifying argument scheme components directly from segmented text. This inter-dependency between tasks has given rise to a growth in the application of multi-objective learning approaches (e.g., Eger, Daxenberger, and Gurevych 2017;Hou and Jochim 2017;Galassi, Lippi, and Torroni 2018;Morio and Fujita 2018), where all tasks are learned and performed at the same time. These examples highlight how the simple pipeline view of argument mining, which characterizes a lot of older research work, is increasingly being superceded by more sophisticated and interconnected techniques.…”

Section: Figurementioning

confidence: 99%

Argument Mining: A Survey

Lawrence

Reed

2020

Computational Linguistics

282

186

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Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general.

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Section: Figurementioning

confidence: 99%

Argument Mining: A Survey

Lawrence

Reed

2020

Computational Linguistics

282

186

View full text Add to dashboard Cite

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“…Some use deep learning to find attacks in discussions (Cocarascu and Toni, 2017). Closer to this paper, others determine them in a given set of arguments, using textual entailment (Cabrio and Villata, 2012) or a combination of markov logic and stance classification (Hou and Jochim, 2017). In principle, any attacking argument denotes a counterargument.…”

Section: Related Workmentioning

confidence: 99%

Retrieval of the Best Counterargument without Prior Topic Knowledge

Wachsmuth¹,

Syed²,

Stein³

2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Given any argument on any controversial topic, how to counter it? This question implies the challenging retrieval task of finding the best counterargument. Since prior knowledge of a topic cannot be expected in general, we hypothesize the best counterargument to invoke the same aspects as the argument while having the opposite stance. To operationalize our hypothesis, we simultaneously model the similarity and dissimilarity of pairs of arguments, based on the words and embeddings of the arguments' premises and conclusions. A salient property of our model is its independence from the topic at hand, i.e., it applies to arbitrary arguments. We evaluate different model variations on millions of argument pairs derived from the web portal idebate.org. Systematic ranking experiments suggest that our hypothesis is true for many arguments: For 7.6 candidates with opposing stance on average, we rank the best counterargument highest with 60% accuracy. Even among all 2801 test set pairs as candidates, we still find the best one about every third time.

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“…Argument Mining. There is an increasing number of works in the computational argumentation research field in recent years, such as argument mining (Shnarch et al, 2018;Trautmann et al, 2020), argument relation detection (Rocha et al, 2018;Hou and Jochim, 2017), argument quality assessment (Wachsmuth et al, 2017;Gleize et al, 2019;, argument generation (Hua and Wang, 2018;Hua et al, 2019a;Schiller et al, 2020), etc. Stab and Gurevych (2014) and Persing and Ng (2016) both propose pipeline approaches to identify argumentative discourse structures in persuasive essays, which mainly includes two steps: extracting argument components and identifying relations.…”

Section: Related Workmentioning

confidence: 99%

APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning

Cheng¹,

Yu²,

Lu³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Peer review and rebuttal, with rich interactions and argumentative discussions in between, are naturally a good resource to mine arguments. However, few works study both of them simultaneously. In this paper, we introduce a new argument pair extraction (APE) task on peer review and rebuttal in order to study the contents, the structure and the connections between them. We prepare a challenging dataset that contains 4,764 fully annotated review-rebuttal passage pairs from an open review platform to facilitate the study of this task. To automatically detect argumentative propositions and extract argument pairs from this corpus, we cast it as the combination of a sequence labeling task and a text relation classification task. Thus, we propose a multitask learning framework based on hierarchical LSTM networks. Extensive experiments and analysis demonstrate the effectiveness of our multi-task framework, and also show the challenges of the new task as well as motivate future research directions. 1 * Liying Cheng is under the Joint Ph.D. Program between Alibaba and Singapore University of Technology and Design.† Qian Yu was an intern at Alibaba.

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Argument Relation Classification Using a Joint Inference Model

Cited by 19 publications

References 21 publications

Argument Mining: A Survey

Argument Mining: A Survey

Retrieval of the Best Counterargument without Prior Topic Knowledge

APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning

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