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cs.CL2026
Which Tokens Need Context? A Reference-Based Analysis of Translation Responsibility Using Fertility and Entropy
Ramakrishna Appicharla, Baban Gain, Santanu Pal +1
When humans translate, not every word depends equally on the surrounding context. Some tokens, particularly function words like pronouns and auxiliaries, rely heavily on preceding…
cs.CL2025
Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models
Ramakrishna Appicharla, Baban Gain, Santanu Pal +1
Despite the popularity of the large language models (LLMs), their application to machine translation is relatively underexplored, especially in context-aware settings. This work pr…
cs.CL2024
A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning
Ramakrishna Appicharla, Baban Gain, Santanu Pal +2
In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies \cite{li-etal-2020-multi-encoder…