Multi-Relational Graph Transformer for Automatic Short Answer Grading

Rajat Agarwal, Varun Khurana, Karish Grover, Mukesh Mohania, Vikram Goyal


Abstract
The recent transition to the online educational domain has increased the need for Automatic Short Answer Grading (ASAG). ASAG automatically evaluates a student’s response against a (given) correct response and thus has been a prevalent semantic matching task. Most existing methods utilize sequential context to compare two sentences and ignore the structural context of the sentence; therefore, these methods may not result in the desired performance. In this paper, we overcome this problem by proposing a Multi-Relational Graph Transformer, MitiGaTe, to prepare token representations considering the structural context. Abstract Meaning Representation (AMR) graph is created by parsing the text response and then segregated into multiple subgraphs, each corresponding to a particular relationship in AMR. A Graph Transformer is used to prepare relation-specific token embeddings within each subgraph, then aggregated to obtain a subgraph representation. Finally, we compare the correct answer and the student response subgraph representations to yield a final score. Experimental results on Mohler’s dataset show that our system outperforms the existing state-of-the-art methods. We have released our implementation https://github.com/kvarun07/asag-gt, as we believe that our model can be useful for many future applications.
Anthology ID:
2022.naacl-main.146
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2001–2012
Language:
URL:
https://aclanthology.org/2022.naacl-main.146
DOI:
10.18653/v1/2022.naacl-main.146
Bibkey:
Cite (ACL):
Rajat Agarwal, Varun Khurana, Karish Grover, Mukesh Mohania, and Vikram Goyal. 2022. Multi-Relational Graph Transformer for Automatic Short Answer Grading. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2001–2012, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Multi-Relational Graph Transformer for Automatic Short Answer Grading (Agarwal et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.146.pdf
Code
 kvarun07/asag-gt