Farjana Sultana Mim


2023

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Teach Me How to Argue: A Survey on NLP Feedback Systems in Argumentation
Camelia Guerraoui | Paul Reisert | Naoya Inoue | Farjana Sultana Mim | Keshav Singh | Jungmin Choi | Irfan Robbani | Shoichi Naito | Wenzhi Wang | Kentaro Inui
Proceedings of the 10th Workshop on Argument Mining

The use of argumentation in education has shown improvement in students’ critical thinking skills, and computational models for argumentation have been developed to further assist this process. Although these models are useful for evaluating the quality of an argument, they often cannot explain why a particular argument score was predicted, i.e., why the argument is good or bad, which makes it difficult to provide constructive feedback to users, e.g., students, so that they can strengthen their critical thinking skills. In this survey, we explore current NLP feedback systems by categorizing each into four important dimensions of feedback (Richness, Visualization, Interactivity and Personalization). We discuss limitations for each dimension and provide suggestions to enhance the power of feedback and explanations to ultimately improve user critical thinking skills.

2022

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LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments
Farjana Sultana Mim | Naoya Inoue | Shoichi Naito | Keshav Singh | Kentaro Inui
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In argumentative discourse, persuasion is often achieved by refuting or attacking others’ arguments. Attacking an argument is not always straightforward and often consists of complex rhetorical moves in which arguers may agree with a logic of an argument while attacking another logic. Furthermore, an arguer may neither deny nor agree with any logics of an argument, instead ignore them and attack the main stance of the argument by providing new logics and presupposing that the new logics have more value or importance than the logics presented in the attacked argument. However, there are no studies in computational argumentation that capture such complex rhetorical moves in attacks or the presuppositions or value judgments in them. To address this gap, we introduce LPAttack, a novel annotation scheme that captures the common modes and complex rhetorical moves in attacks along with the implicit presuppositions and value judgments. Our annotation study shows moderate inter-annotator agreement, indicating that human annotation for the proposed scheme is feasible. We publicly release our annotated corpus and the annotation guidelines.

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IRAC: A Domain-Specific Annotated Corpus of Implicit Reasoning in Arguments
Keshav Singh | Naoya Inoue | Farjana Sultana Mim | Shoichi Naito | Kentaro Inui
Proceedings of the Thirteenth Language Resources and Evaluation Conference

The task of implicit reasoning generation aims to help machines understand arguments by inferring plausible reasonings (usually implicit) between argumentative texts. While this task is easy for humans, machines still struggle to make such inferences and deduce the underlying reasoning. To solve this problem, we hypothesize that as human reasoning is guided by innate collection of domain-specific knowledge, it might be beneficial to create such a domain-specific corpus for machines. As a starting point, we create the first domain-specific resource of implicit reasonings annotated for a wide range of arguments, which can be leveraged to empower machines with better implicit reasoning generation ability. We carefully design an annotation framework to collect them on a large scale through crowdsourcing and show the feasibility of creating a such a corpus at a reasonable cost and high-quality. Our experiments indicate that models trained with domain-specific implicit reasonings significantly outperform domain-general models in both automatic and human evaluations. To facilitate further research towards implicit reasoning generation in arguments, we present an in-depth analysis of our corpus and crowdsourcing methodology, and release our materials (i.e., crowdsourcing guidelines and domain-specific resource of implicit reasonings).

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TYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation
Shoichi Naito | Shintaro Sawada | Chihiro Nakagawa | Naoya Inoue | Kenshi Yamaguchi | Iori Shimizu | Farjana Sultana Mim | Keshav Singh | Kentaro Inui
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Providing feedback on the argumentation of the learner is essential for developing critical thinking skills, however, it requires a lot of time and effort. To mitigate the overload on teachers, we aim to automate a process of providing feedback, especially giving diagnostic comments which point out the weaknesses inherent in the argumentation. It is recommended to give specific diagnostic comments so that learners can recognize the diagnosis without misinterpretation. However, it is not obvious how the task of providing specific diagnostic comments should be formulated. We present a formulation of the task as template selection and slot filling to make an automatic evaluation easier and the behavior of the model more tractable. The key to the formulation is the possibility of creating a template set that is sufficient for practical use. In this paper, we define three criteria that a template set should satisfy: expressiveness, informativeness, and uniqueness, and verify the feasibility of creating a template set that satisfies these criteria as a first trial. We will show that it is feasible through an annotation study that converts diagnostic comments given in a text to a template format. The corpus used in the annotation study is publicly available.

2021

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Exploring Methodologies for Collecting High-Quality Implicit Reasoning in Arguments
Keshav Singh | Farjana Sultana Mim | Naoya Inoue | Shoichi Naito | Kentaro Inui
Proceedings of the 8th Workshop on Argument Mining

Annotation of implicit reasoning (i.e., warrant) in arguments is a critical resource to train models in gaining deeper understanding and correct interpretation of arguments. However, warrants are usually annotated in unstructured form, having no restriction on their lexical structure which sometimes makes it difficult to interpret how warrants relate to any of the information given in claim and premise. Moreover, assessing and determining better warrants from the large variety of reasoning patterns of unstructured warrants becomes a formidable task. Therefore, in order to annotate warrants in a more interpretative and restrictive way, we propose two methodologies to annotate warrants in a semi-structured form. To the best of our knowledge, we are the first to show how such semi-structured warrants can be annotated on a large scale via crowdsourcing. We demonstrate through extensive quality evaluation that our methodologies enable collecting better quality warrants in comparison to unstructured annotations. To further facilitate research towards the task of explicating warrants in arguments, we release our materials publicly (i.e., crowdsourcing guidelines and collected warrants).

2019

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Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring
Farjana Sultana Mim | Naoya Inoue | Paul Reisert | Hiroki Ouchi | Kentaro Inui
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Existing document embedding approaches mainly focus on capturing sequences of words in documents. However, some document classification and regression tasks such as essay scoring need to consider discourse structure of documents. Although some prior approaches consider this issue and utilize discourse structure of text for document classification, these approaches are dependent on computationally expensive parsers. In this paper, we propose an unsupervised approach to capture discourse structure in terms of coherence and cohesion for document embedding that does not require any expensive parser or annotation. Extrinsic evaluation results show that the document representation obtained from our approach improves the performance of essay Organization scoring and Argument Strength scoring.