most citedA Case Study on Context Encoding in Multi-Encoder based Document-Level Neural Machine Translation

1 citations · 1 across the 3 of their papers we have counts for

collaborators
Showing cs.CLShow all

5 papers · 1 filter

cs.CL2025

CorIL: Towards Enriching Indian Language to Indian Language Parallel Corpora and Machine Translation Systems

Soham Bhattacharjee, Mukund K Roy, Yathish Poojary +19

India's linguistic landscape is one of the most diverse in the world, comprising over 120 major languages and approximately 1,600 additional languages, with 22 officially recognize…

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…

cs.CL2023

Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards

Baban Gain, Ramakrishna Appicharla, Soumya Chennabasavaraj +3

Community Question-Answering (CQA) portals serve as a valuable tool for helping users within an organization. However, making them accessible to non-English-speaking users continue…

cs.CL20231 cited

A Case Study on Context Encoding in Multi-Encoder based Document-Level Neural Machine Translation

Ramakrishna Appicharla, Baban Gain, Santanu Pal +1

Recent studies have shown that the multi-encoder models are agnostic to the choice of context, and the context encoder generates noise which helps improve the models in terms of BL…