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Award ID contains: 2241670

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  1. ; ; ; ; ; (, Association for Computational Linguistics)
    We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We design a multi-faceted prompt to extract a textual explanation of the reasoning that connects visible cues to underlying social meanings. These extracted explanations or rationales serve as augmentations to the conversational text to facilitate dialogue understanding and transfer. Our empirical results over 2,340 experimental settings demonstrate the significant positive impact of adding these rationales. Our findings hold true for in-domain classification, zero-shot, and few-shot domain transfer for two different social meaning detection tasks, each spanning two different corpora. 
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  2. ; ; (, ACL)
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  3. ; ; (, Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications)
    Kochmar, Ekaterina; Burstein, Jill; Horbach, Andrea; Laarmann-Quante, Ronja; Madnani, Nitin; Tack, Anaïs; Yaneva, Victoria; Yuan, Zheng; Zesch, Torsten (Ed.)
    By aligning the functional components derived from the activations of transformer models trained for AES with external knowledge such as human-understandable feature groups, the proposed method improves the interpretability of a Longformer Automatic Essay Scoring (AES) system and provides tools for performing such analyses on further neural AES systems. The analysis focuses on models trained to score essays based on organization, main idea, support, and language. The findings provide insights into the models’ decision-making processes, biases, and limitations, contributing to the development of more transparent and reliable AES systems. 
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