Towards Automatic Short Answer Assessment for Finnish as a Paraphrase Retrieval Task

Li-Hsin Chang, Jenna Kanerva, Filip Ginter


Abstract
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.
Anthology ID:
2022.bea-1.30
Volume:
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
Month:
July
Year:
2022
Address:
Seattle, Washington
Venues:
BEA | NAACL
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
262–271
Language:
URL:
https://aclanthology.org/2022.bea-1.30
DOI:
10.18653/v1/2022.bea-1.30
Bibkey:
Cite (ACL):
Li-Hsin Chang, Jenna Kanerva, and Filip Ginter. 2022. Towards Automatic Short Answer Assessment for Finnish as a Paraphrase Retrieval Task. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022), pages 262–271, Seattle, Washington. Association for Computational Linguistics.
Cite (Informal):
Towards Automatic Short Answer Assessment for Finnish as a Paraphrase Retrieval Task (Chang et al., BEA 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.bea-1.30.pdf
Data
Finnish Paraphrase Corpus