A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Towards Automatic Short Answer Assessment for Finnish as a Paraphrase Retrieval Task
Tekijät: Chang Li-Hsin, Kanerva Jenna, Ginter Filip
Toimittaja: Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, Torsten Zesch
Konferenssin vakiintunut nimi: Workshop on Innovative Use of NLP for Building Educational Applications
Kustantaja: Association for Computational Linguistics (ACL)
Julkaisuvuosi: 2022
Kokoomateoksen nimi: Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
Tietokannassa oleva lehden nimi: BEA 2022 - 17th Workshop on Innovative Use of NLP for Building Educational Applications, Proceedings
Aloitussivu: 262
Lopetussivu: 271
ISBN: 978-1-955917-83-4
DOI: https://doi.org/10.18653/v1/2022.bea-1.30
Verkko-osoite: https://aclanthology.org/2022.bea-1.30/
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/176823390
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
