Papers by Shenbin Qian
What do Large Language Models Need for Machine Translation Evaluation? (2024.emnlp-main)
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Shenbin Qian, Archchana Sindhujan, Minnie Kabra, Diptesh Kanojia, Constantin Orasan, Tharindu Ranasinghe, Fred Blain
| Challenge: | Existing research shows that large language models can perform better in machine translation tasks. |
| Approach: | They propose to use large language models for machine translation evaluations . authors explore what translation information is needed for LLMs to evaluate MT quality . |
| Outcome: | The proposed model performs comparable to fine-tuned multilingual pre-trained models. |
Character-level Language Models for Abbreviation and Long-form Detection (2024.lrec-main)
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| Challenge: | Abbreviations and long forms are textual elements that are present in scientific communication . non-recognition of abbreviation and long form can lead to a negative impact on information retrieval . |
| Approach: | They propose to train and test language models for automatically identifying abbreviations and long forms . they use existing datasets annotated with abbrevations and their associated long forms to test them . |
| Outcome: | The proposed model can detect abbreviations and long forms on biomedical data . the proposed model improves on a previously untested dataset with biomedically-annotated datasets . |