Dave the debater: a retrieval-based and generative argumentative dialogue agent

Le, Dieu Thu; Nguyen, Cam-Tu; Nguyen, Kim Anh

doi:10.18653/v1/w18-5215

Cited by 37 publications

(23 citation statements)

References 24 publications

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“…In these cases, software such as Arvina or D-BAS (Krauthoff et al 2018) can be used to both run the dialogue according to the specified rules and automatically capture the argumentative structure generated as the dialogue progresses. These structures can then be used to allow for mixed initiative argumentation (Snaith, Lawrence, and Reed 2010), where a combination of human users and software agents representing the arguments made by other people can take part in the same conversation, using retrieval-based methods to select the most relevant response (Le, Nguyen, and Nguyen 2018). In such scenarios, the contributions of human participants can be interpreted by virtue of their dialogical connections to the discourse, allowing a small step toward mining argument structure from natural language.…”

Section: Dialogical Relationsmentioning

confidence: 99%

Argument Mining: A Survey

Lawrence

Reed

2020

Computational Linguistics

266

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: Dialogical Relationsmentioning

confidence: 99%

Argument Mining: A Survey

Lawrence

Reed

2020

Computational Linguistics

266

186

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“…Rakshit et al developed an arguing bot that chooses utterances from corpora, including debates on the relevant topic; they also explored potential structures of the corpora to expedite choices [20]. Dieu-Thu et al developed two types of argumentative dialogue agents: a retrievalbased system using long short-term memory (LSTM) and a generative model-based system using a recurrent neural network (RNN) [21]. Marumoto et al developed a debating system regarding TV news that generates claims and reasons by extracting appropriate sentences from the internet sources [22].…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Argumentation Structure for Persuasive Argumentative Dialogue Generation

Sakai

Higashinaka

Yoshikawa

et al. 2020

IEICE Trans. Inf. & Syst.

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Argumentation is a process of reaching a consensus through premises and rebuttals. If an artificial dialogue system can perform argumentation, it can improve users' decisions and ability to negotiate with the others. Previously, researchers have studied argumentative dialogue systems through a structured database regarding argumentation structure and evaluated the logical consistency of the dialogue. However, these systems could not change its response based on the user's agreement or disagreement to its last utterance. Furthermore, the persuasiveness of the generated dialogue has not been evaluated. In this study, a method is proposed to generate persuasive arguments through a hierarchical argumentation structure that considers human agreement and disagreement. Persuasiveness is evaluated through a crowd sourcing platform wherein participants' written impressions of shown dialogue texts are scored via a third person Likert scale evaluation. The proposed method was compared to the baseline method wherein argument response texts were generated without consideration of the user's agreement or disagreement. Experiment results suggest that the proposed method can generate a more persuasive dialogue than the baseline method. Further analysis implied that perceived persuasiveness was induced by evaluations of the behavior of the dialogue system, which was inherent in the hierarchical argumentation structure.

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“…This work involves the generation of claims but in relation to a topic. Other researchers generated political counter-arguments supported by external evidence (Hua and Wang, 2018) and generating argumentative dialogue by maximizing mutual information (Le et al, 2018). This research considers end-to-end argument generation, which may not be coherent, whereas we focus specifically on contrastive claims.…”

Section: Related Workmentioning

confidence: 99%

“…In the Toulmin model (1958), often used in computational argumentation research, the center of the argument is the claim, a statement that is in dispute (Govier, 2010). In recent years, there has been increased interest in argument generation (Bilu and Slonim, 2016;Hua and Wang, 2018;Le et al, 2018). Given an argument, a system that generates counter-arguments would need to 1) identify the claims to refute, 2) generate a new claim with a different view, and 3) find supporting evidence for the new claim.…”

Section: Introductionmentioning

confidence: 99%

Fixed That for You: Generating Contrastive Claims with Semantic Edits

Hidey¹,

McKeown²

2019

Proceedings of the 2019 Conference of the North

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Understanding contrastive opinions is a key component of argument generation. Central to an argument is the claim, a statement that is in dispute. Generating a counter-argument then requires generating a response in contrast to the main claim of the original argument. To generate contrastive claims, we create a corpus of Reddit comment pairs self-labeled by posters using the acronym FTFY (fixed that for you). We then train neural models on these pairs to edit the original claim and produce a new claim with a different view. We demonstrate significant improvement over a sequence-to-sequence baseline in BLEU score and a human evaluation for fluency, coherence, and contrast.

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Dave the debater: a retrieval-based and generative argumentative dialogue agent

Cited by 37 publications

References 24 publications

Argument Mining: A Survey

Argument Mining: A Survey

Hierarchical Argumentation Structure for Persuasive Argumentative Dialogue Generation

Fixed That for You: Generating Contrastive Claims with Semantic Edits

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