A4 Refereed article in a conference publication
Towards Automatic Short Answer Assessment for Finnish as a Paraphrase Retrieval Task
Authors: Chang Li-Hsin, Kanerva Jenna, Ginter Filip
Editors: Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, Torsten Zesch
Conference name: Workshop on Innovative Use of NLP for Building Educational Applications
Publisher: Association for Computational Linguistics (ACL)
Publication year: 2022
Book title : Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
Journal name in source: BEA 2022 - 17th Workshop on Innovative Use of NLP for Building Educational Applications, Proceedings
First page : 262
Last page: 271
ISBN: 978-1-955917-83-4
DOI: https://doi.org/10.18653/v1/2022.bea-1.30(external)
Web address : https://aclanthology.org/2022.bea-1.30/(external)
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/176823390(external)
Automatic grouping of textual answers has the potential of allowing batch grading, but is challenging because the answers, especially longer essays, have many claims. To explore the feasibility of grouping together answers based on their semantic meaning, this paper investigates the grouping of short textual answers, proxies of single claims. This is approached as a paraphrase identification task, where neural and non-neural sentence embeddings and a paraphrase identification model are tested. These methods are evaluated on a dataset consisting of over 4000 short textual answers from various disciplines. The results map out the suitable question types for the paraphrase identification model and those for the neural and non-neural methods.
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