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1. ArgU: A Controllable Factual Argument Generator

   https://doi.org/10.18653/v1/2023.acl-long.466  

   Saha, Sougata; Srihari, Rohini (July 2023, Proceedings of ACL 2023)

   Effective argumentation is essential towards a purposeful conversation with a satisfactory outcome. For example, persuading someone to reconsider smoking might involve empathetic, well founded arguments based on facts and expert opinions about its ill-effects and the consequences on one’s family. However, the automatic generation of high-quality factual arguments can be challenging. Addressing existing controllability issues can make the recent advances in computational models for argument generation a potential solution. In this paper, we introduce ArgU: a neural argument generator capable of producing factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure using Walton’s argument scheme-based control codes. Unfortunately, computational argument generation is a relatively new field and lacks datasets conducive to training. Hence, we have compiled and released an annotated corpora of 69,428 arguments spanning six topics and six argument schemes, making it the largest publicly available corpus for identifying argument schemes; the paper details our annotation and dataset creation framework. We further experiment with an argument generation strategy that establishes an inference strategy by generating an “argument template” before actual argument generation. Our results demonstrate that it is possible to automatically generate diverse arguments exhibiting different inference patterns for the same set of facts by using control codes based on argument schemes and stance. 

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2. Consolidating Strategies for Countering Hate Speech Using Persuasive Dialogues

   Saha, Sougata; Srihari, Rohini K (December 2023, ACL Anthology)

   Hateful comments are prevalent on social media platforms. Although tools for automatically detecting, flagging, and blocking such false, offensive, and harmful content online have lately matured, such reactive and brute force methods alone provide short-term and superficial remedies while the perpetrators persist. With the public availability of large language models which can generate articulate synthetic and engaging content at scale, there are concerns about the rapid growth of dissemination of such malicious content on the web. There is now a need to focus on deeper, long-term solutions that involve engaging with the human perpetrator behind the source of the content to change their viewpoint or at least bring down the rhetoric using persuasive means. To do that, we propose defining and experimenting with controllable strategies for generating counterarguments to hateful comments in online conversations. We experiment with controlling response generation using features based on (i) argument structure and reasoning-based Walton argument schemes, (ii) counter-argument speech acts, and (iii) human characteristicsbased qualities such as Big-5 personality traits and human values. Using automatic and human evaluations, we determine the best combination of features that generate fluent, argumentative, and logically sound arguments for countering hate. We further share the developed computational models for automatically annotating text with such features, and a silver-standard annotated version of an existing hate speech dialog corpora. 

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3. Using an Observation Protocol To Evaluate Student Argumentation Skills in Introductory Biology Laboratories

   https://doi.org/10.1128/jmbe.00209-22  

   Clevenger, Lindsey; Teshera-Levye, Jennifer; Walker, Joi P.; Vance-Chalcraft, Heather D. (August 2023, Journal of Microbiology & Biology Education)

   Shaffer, Justin (Ed.)

   ABSTRACT Argumentation is vital in the development of scientific knowledge, and students who can argue from evidence and support their claims develop a deeper understanding of science. In this study, the Argument-Driven Inquiry instruction model was implemented in a two-semester sequence of introductory biology laboratories. Student’s scientific argumentation sessions were video recorded and analyzed using the Assessment of Scientific Argumentation in the Classroom observation protocol. This protocol separates argumentation into three subcategories: cognitive (how the group develops understanding), epistemic (how consistent the group’s process is with the culture of science), and social (how the group members interact with each other). We asked whether students are equally skilled in all subcategories of argumentation and how students’ argumentation skills differ based on lab exercise and course. Students scored significantly higher on the social than the cognitive and epistemic subcategories of argumentation. Total argumentation scores were significantly different between the two focal investigations in Biology Laboratory I but not between the two focal investigations in Biology Laboratory II. Therefore, student argumentation skills were not consistent across content; the design of the lab exercises and their implementation impacted the level of argumentation that occurred. These results will ultimately aid in the development and expansion of Argument-Driven Inquiry instructional models, with the goal of further enhancing students’ scientific argumentation skills and understanding of science. 

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4. Neural Argument Generation Augmented with Externally Retrieved Evidence

   https://doi.org/10.18653/v1/P18-1021  

   Hua, X.; Wang, L. (January 2018, Annual Meeting of the Association for Computational Linguistics)

   High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than popular sequence-to-sequence generation models according to automatic evaluation and human assessments. 

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5. Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable

   https://doi.org/3412815.3416893  

   Tan, Sarah; Soloviev, Matvey; Hooker, Giles; Wells, Martin (October 2020, Foundations of data science)

   null (Ed.)

   Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships between features and predictions, we propose an alternative approach to interpret tree ensemble classifiers by surfacing representative points for each class -- prototypes. We introduce a new distance for Gradient Boosted Tree models, and propose new, adaptive prototype selection methods with theoretical guarantees, with the flexibility to choose a different number of prototypes in each class. We demonstrate our methods on random forests and gradient boosted trees, showing that the prototypes can perform as well as or even better than the original tree ensemble when used as a nearest-prototype classifier. In a user study, humans were better at predicting the output of a tree ensemble classifier when using prototypes than when using Shapley values, a popular feature attribution method. Hence, prototypes present a viable alternative to feature-based explanations for tree ensembles. 

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