1 citations · 1 across the 7 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…
What do Large Language Models Need for Machine Translation Evaluation?
Shenbin Qian, Archchana Sindhujan, Minnie Kabra +4
Leveraging large language models (LLMs) for various natural language processing tasks has led to superlative claims about their performance. For the evaluation of machine translati…
SurreyAI 2023 Submission for the Quality Estimation Shared Task
Archchana Sindhujan, Diptesh Kanojia, Constantin Orasan +1
Quality Estimation (QE) systems are important in situations where it is necessary to assess the quality of translations, but there is no reference available. This paper describes t…