Papers by Karthikeyan K
Multilingual CheckList: Generation and Evaluation (2022.findings-aacl)
Copied to clipboard
Karthikeyan K, Shaily Bhatt, Pankaj Singh, Somak Aditya, Sandipan Dandapat, Sunayana Sitaram, Monojit Choudhury
| Challenge: | Multilingual evaluation benchmarks usually contain limited high-resource languages and do not test models for specific linguistic capabilities. |
| Approach: | They propose an algorithm for automatically extracting target language CheckList templates from machine translated instances of a source language templates. |
| Outcome: | The proposed algorithm compares with CheckLists created with human verification in Hindi and 9 other languages. |
Taxonomy Expansion for Named Entity Recognition (2023.emnlp-main)
Copied to clipboard
Karthikeyan K, Yogarshi Vyas, Jie Ma, Giovanni Paolini, Neha John, Shuai Wang, Yassine Benajiba, Vittorio Castelli, Dan Roth, Miguel Ballesteros
| Challenge: | Training a Named Entity Recognition model involves fixing a taxonomy of entity types . however, requirements evolve and a model may need to recognize additional entity types. |
| Approach: | They propose a method that uses only partially annotated datasets to train a model to recognize additional entity types. |
| Outcome: | The proposed approach performs better with partially annotated datasets than other approaches . the gap between the proposed approach and other approaches is large in additional datasets . |
Extending Multilingual BERT to Low-Resource Languages (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. |
| Approach: | They propose a simple but effective approach to extend multilingual BERT to any new language and show an increase in F1 on M-BERT and new languages. |
| Outcome: | The proposed approach improves on languages already in M-BERT and out of it on other languages. |