1 citations · 1 across the 5 of their papers we have counts for
8 papers
In the Blind: Building Pseudo-References for MT Evaluation
Diptesh Kanojia, Chi-kiu Lo, Archchana Sindhujan +3
The WMT26 General MT task evaluates systems on 10 language pairs that have no human references (neither translated from scratch nor post-edited from MT output by humans). We descri…
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…
Domain-Specific Quality Estimation for Machine Translation in Low-Resource Scenarios
Namrata Patil Gurav, Akashdeep Ranu, Archchana Sindhujan +1
Quality Estimation (QE) is essential for assessing machine translation quality in reference-less settings, particularly for domain-specific and low-resource language scenarios. In…
Beyond Scalar Scores: Reinforcement Learning for Error-Aware Quality Estimation of Machine Translation
Archchana Sindhujan, Girish A. Koushik, Shenbin Qian +2
Quality Estimation (QE) aims to assess the quality of machine translation (MT) outputs without relying on reference translations, making it essential for real-world, large-scale MT…
ALOPE: Adaptive Layer Optimization for Translation Quality Estimation using Large Language Models
Archchana Sindhujan, Shenbin Qian, Chan Chi Chun Matthew +2
Large Language Models (LLMs) have shown remarkable performance across a wide range of natural language processing tasks. Quality Estimation (QE) for Machine Translation (MT), which…
When LLMs Struggle: Reference-less Translation Evaluation for Low-resource Languages
Archchana Sindhujan, Diptesh Kanojia, Constantin Orasan +1
This paper investigates the reference-less evaluation of machine translation for low-resource language pairs, known as quality estimation (QE). Segment-level QE is a challenging cr…