Mukesh Mohania


Multi-Relational Graph Transformer for Automatic Short Answer Grading
Rajat Agarwal | Varun Khurana | Karish Grover | Mukesh Mohania | Vikram Goyal
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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, as we believe that our model can be useful for many future applications.


Using Text Reviews for Product Entity Completion
Mrinmaya Sachan | Tanveer Faruquie | L. V. Subramaniam | Mukesh Mohania
Proceedings of 5th International Joint Conference on Natural Language Processing


Automatically Generating Term Frequency Induced Taxonomies
Karin Murthy | Tanveer A Faruquie | L Venkata Subramaniam | Hima Prasad K | Mukesh Mohania
Proceedings of the ACL 2010 Conference Short Papers