Despite the progress in machine translation quality estimation and evaluation in the last
years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers …

Automatic evaluation of machine translation (MT) is a critical tool driving the rapid iterative
development of MT systems. While considerable progress has been made on estimating a …

We report the results of the WMT 2021 shared task on Quality Estimation, where the
challenge is to predict the quality of the output of neural machine translation systems at the …

While large language models (LLMs) have taken great strides towards helping humans with
a plethora of tasks, hallucinations remain a major impediment towards gaining user trust …

We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE)
and Automatic Post-Editing (APE). The dataset contains eleven language pairs, with human …

Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of
machine-translated text in real time without the need for reference translations, which is of …

Despite significant research effort in the development of automatic dialogue evaluation
metrics, little thought is given to evaluating dialogues other than in English. At the same time …

In recent years, generative artificial intelligence models, represented by Large Language
Models (LLMs) and Diffusion Models (DMs), have revolutionized content production …

We present the joint contribution of Instituto Superior Técnico (IST) and Unbabel to the
Explainable Quality Estimation (QE) shared task, where systems were submitted to two …

This paper presents submissions of the NJUNLP team in WMT 2022Quality Estimation
shared task 1, where the goal is to predict the sentence-level and word-level quality for …