9 citations · 24 across the 17 of their papers we have counts for
17 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…
Mind the Pause: Disfluency-Aware Objective Tuning for Multilingual Speech Correction with LLMs
Deepak Kumar, Baban Gain, Asif Ekbal
Automatic Speech Recognition (ASR) transcripts often contain disfluencies, such as fillers, repetitions, and false starts, which reduce readability and hinder downstream applicatio…
One Model to Translate Them All? A Journey to Mount Doom for Multilingual Model Merging
Baban Gain, Trilok Nath Singh, Asif Ekbal
Weight-space model merging combines independently fine-tuned checkpoints without access to the original training data. While merging has shown promise in multitask settings, its be…
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…
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…
Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
Baban Gain, Dibyanayan Bandyopadhyay, Asif Ekbal +1
Large Language Models (LLMs) are rapidly reshaping machine translation (MT), particularly by introducing instruction-following, in-context learning, and preference-based alignment…
Balancing Global Quality and Pronoun-Specific Feedback for Context-Aware Machine Translation
Harshit Dhankhar, Baban Gain, Asif Ekbal +1
Context-aware machine translation can expose the evidence needed for pronoun choice, but standard fine-tuning does not explicitly prioritize these sparse discourse-sensitive decisi…
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…
Universal Adversarial Framework to Improve Adversarial Robustness for Diabetic Retinopathy Detection
Samrat Mukherjee, Dibyanayan Bandyopadhyay, Baban Gain +1
Diabetic Retinopathy (DR) is a prevalent illness associated with Diabetes which, if left untreated, can result in irreversible blindness. Deep Learning based systems are gradually…
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…
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…
Impact of Visual Context on Noisy Multimodal NMT: An Empirical Study for English to Indian Languages
Baban Gain, Dibyanayan Bandyopadhyay, Samrat Mukherjee +2
Neural Machine Translation (NMT) has made remarkable progress using large-scale textual data, but the potential of incorporating multimodal inputs, especially visual information, r…
IITP at WAT 2021: System description for English-Hindi Multimodal Translation Task
Baban Gain, Dibyanayan Bandyopadhyay, Asif Ekbal
Neural Machine Translation (NMT) is a predominant machine translation technology nowadays because of its end-to-end trainable flexibility. However, NMT still struggles to translate…
IITP at AILA 2019: System Report for Artificial Intelligence for Legal Assistance Shared Task
Baban Gain, Dibyanayan Bandyopadhyay, Arkadipta De +2
In this article, we present a description of our systems as a part of our participation in the shared task namely Artificial Intelligence for Legal Assistance (AILA 2019). This is…
IITP@COLIEE 2019: Legal Information Retrieval using BM25 and BERT
Baban Gain, Dibyanayan Bandyopadhyay, Tanik Saikh +1
Natural Language Processing (NLP) and Information Retrieval (IR) in the judicial domain is an essential task. With the advent of availability domain-specific data in electronic for…
IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering
Dibyanayan Bandyopadhyay, Baban Gain, Tanik Saikh +1
This paper presents the experiments accomplished as a part of our participation in the MEDIQA challenge, an (Abacha et al., 2019) shared task. We participated in all the three task…