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cs.CL2024
What do Large Language Models Need for Machine Translation Evaluation?
Shenbin Qian, Archchana Sindhujan, Minnie Kabra +4
Leveraging large language models (LLMs) for various natural language processing tasks has led to superlative claims about their performance. For the evaluation of machine translati…
cs.CL2024
Are Large Language Models State-of-the-art Quality Estimators for Machine Translation of User-generated Content?
Shenbin Qian, Constantin Orăsan, Diptesh Kanojia +1
This paper investigates whether large language models (LLMs) are state-of-the-art quality estimators for machine translation of user-generated content (UGC) that contains emotional…
cs.CL2024
A Multi-task Learning Framework for Evaluating Machine Translation of Emotion-loaded User-generated Content
Shenbin Qian, Constantin Orăsan, Diptesh Kanojia +1
Machine translation (MT) of user-generated content (UGC) poses unique challenges, including handling slang, emotion, and literary devices like irony and sarcasm. Evaluating the qua…