Exploiting Personal Characteristics of Debaters for Predicting Persuasiveness (2020.acl-main)
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| Challenge: | Several studies have examined persuasiveness in debates by probing the main factors for establishing persuasion, particularly regarding the role of linguistic features of debaters' arguments. |
| Approach: | They propose to model debaters’ prior beliefs, interests, and personality traits based on their previous activity without dependence on explicit user profiles or questionnaires. |
| Outcome: | The proposed model improves persuasiveness prediction and debater resistance to persuasion. |
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| Challenge: | Large language models (LLMs) are increasingly used in decision-support applications that aim to influence human behavior or beliefs, such as health coaching, tutoring, and targeted marketing. |
| Approach: | They propose a context-aware user profiling framework with two trainable components that generate optimal queries to retrieve persuasion-relevant records from a user’s history and a profiler that summarizes these records into a model. |
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A Corpus for Modeling User and Language Effects in Argumentation on Online Debating (P19-1)
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| Challenge: | Existing argumentation datasets have allowed only limited assessment of "user" traits because information on background of users is generally unavailable. |
| Approach: | They present a dataset of 78,376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles. |
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Leveraging Topic Relatedness for Argument Persuasion (2021.findings-acl)
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| Challenge: | Existing studies of argumentation focus on the effects of factors such as source, audience, and language style, but the impact of exploiting the relationships among controversial topics is under-explored. |
| Approach: | They propose to model topic relatedness among controversial topics using topic embedding features and topic semantics features extracted from the arguments. |
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Exploring the Role of Argument Structure in Online Debate Persuasion (2020.emnlp-main)
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| Challenge: | Existing work in NLP has shown that linguistic features extracted from debate text and features encoding the characteristics of the audience are both critical in persuasion studies. |
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The Role of Pragmatic and Discourse Context in Determining Argument Impact (D19-1)
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| Challenge: | Recent work shows that attributes of both the audience and communicator constitute important cues for determining argument strength. |
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“Tell me who you are and I tell you how you argue”: Predicting Stances and Arguments for Stakeholder Groups (2024.findings-naacl)
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| Challenge: | Argument mining has focused on the identification, extraction, and formalization of arguments. |
| Approach: | They propose a framework that relies on a recommender-based architecture to predict stances and argumentative main points on societally controversial topics for a given stakeholder. |
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Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays (P18-1)
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| Challenge: | Existing work on automated essay scoring has focused on holistic scoring, which summarizes the quality of an essay with a single score. |
| Approach: | They present a corpus of essays simultaneously annotated with argument components, argument persuasiveness scores, and attributes of argument components that impact an argument’s persuasiveness. |
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AMPERSAND: Argument Mining for PERSuAsive oNline Discussions (D19-1)
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| Challenge: | Argument mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text. |
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AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments (2024.emnlp-main)
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| Challenge: | Existing tools for persuasion are well-equipped to identify which of a pre-existing set of messages is most persuasive, but they do not offer causal evidence on whether or how they have succeeded. |
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Unveiling the Power of Argument Arrangement in Online Persuasive Discussions (2023.findings-emnlp)
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| Challenge: | a recent study shows that the CMV is the best time period in human history for the vast majority of people. |
| Approach: | They extend a semantic argumentation unit type model by clustering type sequences into different argument arrangement patterns and representing discussions as sequences of these patterns. |
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