Farjana Sultana Mim


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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).


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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.