1 citations · 1 across the 5 of their papers we have counts for
5 papers
Source-Free MT Evaluation Is Not MT Evaluation
Baban Gain, Ramakrishna Appicharla, Asif Ekbal
Reference-based metrics remain the standard choice in machine translation evaluation, partly because quality estimation methods often correlate less well with human judgments. As a…
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…
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…
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…
Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation
Kartik Kartik, Sanjana Soni, Anoop Kunchukuttan +2
The widespread online communication in a modern multilingual world has provided opportunities to blend more than one language (aka code-mixed language) in a single utterance. This…