37 citations · 42 across the 6 of their papers we have counts for
6 papers
IndicQE-APE: A Consolidated Benchmark for Quality Estimation and Automatic Post-Editing for Indic Languages
Diptesh Kanojia, Archchana Sindhujan, Sourabh Deoghare +14
Indic quality estimation (QE) and automatic post-editing (APE) data is spread across separate releases, so no single resource supports training and evaluation across tasks and lang…
Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization
Zilu Tang, Rajen Chatterjee, Sarthak Garg
Machine Translation (MT) is undergoing a paradigm shift, with systems based on fine-tuned large language models (LLM) becoming increasingly competitive with traditional encoder-dec…
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data
Haoran Sun, Renren Jin, Shaoyang Xu +10
Large language models (LLMs) have demonstrated prowess in a wide range of tasks. However, many LLMs exhibit significant performance discrepancies between high- and low-resource lan…
Automatic Post-Editing for Machine Translation
Rajen Chatterjee
Automatic Post-Editing (APE) aims to correct systematic errors in a machine translated text. This is primarily useful when the machine translation (MT) system is not accessible for…
Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System
Judith Gaspers, Penny Karanasou, Rajen Chatterjee
This paper investigates the use of Machine Translation (MT) to bootstrap a Natural Language Understanding (NLU) system for a new language for the use case of a large-scale voice-co…
eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing
Matteo Negri, Marco Turchi, Rajen Chatterjee +1
Training models for the automatic correction of machine-translated text usually relies on data consisting of (source, MT, human post- edit) triplets providing, for each source sent…