Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM

Habernal, Ivan; Gurevych, Iryna

doi:10.18653/v1/p16-1150

Cited by 158 publications

(201 citation statements)

References 34 publications

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“…Among the latter, Persing and Ng (2015) rely on manual annotations of essays to predict how strong an essay's argument is-a naturally subjective and non-scalable assessment. For scalability, Habernal and Gurevych (2016) learn on crowdsourced labels, which of two arguments is more convincing. Similar to us, they construct a graph to rank arguments, but since their graph is based on the labels, the subjectivity remains.…”

Section: Related Workmentioning

confidence: 99%

"PageRank" for Argument Relevance

Wachsmuth

Stein

Ajjour

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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Future search engines are expected to deliver pro and con arguments in response to queries on controversial topics. While argument mining is now in the focus of research, the question of how to retrieve the relevant arguments remains open. This paper proposes a radical model to assess relevance objectively at web scale: the relevance of an argument's conclusion is decided by what other arguments reuse it as a premise. We build an argument graph for this model that we analyze with a recursive weighting scheme, adapting key ideas of PageRank. In experiments on a large ground-truth argument graph, the resulting relevance scores correlate with human average judgments. We outline what natural language challenges must be faced at web scale in order to stepwise bring argument relevance to web search engines.

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Section: Related Workmentioning

confidence: 99%

"PageRank" for Argument Relevance

Wachsmuth

Stein

Ajjour

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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show abstract

“…Habernal and Gurevych [2016a;2016b] rank a pair of arguments w.r.t. persuasiveness, but Motion This House would ban teachers from interacting with students via social networking websites.…”

Section: Related Workmentioning

confidence: 99%

Why Can't You Convince Me? Modeling Weaknesses in Unpersuasive Arguments

Persing¹,

Ng²

2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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Recent work on argument persuasiveness has focused on determining how persuasive an argument is. Oftentimes, however, it is equally important to understand why an argument is unpersuasive, as it is difficult for an author to make her argument more persuasive unless she first knows what errors made it unpersuasive. Motivated by this practical concern, we (1) annotate a corpus of debate comments with not only their persuasiveness scores but also the errors they contain, (2) propose an approach to persuasiveness scoring and error identification that outperforms competing baselines, and (3) show that the persuasiveness scores computed by our approach can indeed be explained by the errors it identifies.

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“…This is due primarily to the lack applicable datasets. However, Habernal and Gurevych (2016b) have recently presented a dataset where argument pairs are annotated for argument convincingness, as well as finer-grained annotations related to the effectiveness of arguments (Habernal and Gurevych, 2016a). The authors experimented with featurebased classifiers, as well as various RNN architectures to construct predictive models for the dataset.…”

Section: Related Workmentioning

confidence: 99%

Towards Debate Automation: a Recurrent Model for Predicting Debate Winners

Potash¹,

Rumshisky²

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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In this paper we introduce a practical first step towards the creation of an automated debate agent: a state-of-the-art recurrent predictive model for predicting debate winners. By having an accurate predictive model, we are able to objectively rate the quality of a statement made at a specific turn in a debate. The model is based on a recurrent neural network architecture with attention, which allows the model to effectively account for the entire debate when making its prediction. Our model achieves state-of-the-art accuracy on a dataset of debate transcripts annotated with audience favorability of the debate teams. Finally, we discuss how future work can leverage our proposed model for the creation of an automated debate agent. We accomplish this by determining the model input that will maximize audience favorability toward a given side of a debate at an arbitrary turn.

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Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM

Cited by 158 publications

References 34 publications

"PageRank" for Argument Relevance

"PageRank" for Argument Relevance

Why Can't You Convince Me? Modeling Weaknesses in Unpersuasive Arguments

Towards Debate Automation: a Recurrent Model for Predicting Debate Winners

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