Large-scale generative models show an impressive ability to perform a wide range of
Natural Language Processing (NLP) tasks using in-context learning, where a few examples …

We present the joint contribution of IST and Unbabel to the WMT 2022 Shared Task on
Quality Estimation (QE). Our team participated on all three subtasks:(i) Sentence and Word …

We present the joint contribution of Unbabel and Instituto Superior T\'ecnico to the WMT
2023 Shared Task on Quality Estimation (QE). Our team participated on all tasks: sentence …

Quality estimation (QE) is a crucial method to investigate automatic methods for estimating
the quality of machine translation results without reference translations. This paper presents …

This paper describes our submissions to the 2023 Conference on Machine Translation
(WMT-23) Metrics shared task. Knowledge distillation is commonly used to create smaller …

Sentence-level translation, document-level translation, translation memory, and terminology
constrained translation play an important role in machine translation. Most of the previous …

This paper presents the contributions of Charles University teams to the WMT23 General
translation task (English to Czech and Czech to Ukrainian translation directions). Our main …

We propose a genetic algorithm (GA) based method for modifying n-best lists produced by a
machine translation (MT) system. Our method offers an innovative approach to improving MT …

Abstract Machine translation is natural language generation (NLG) problem of translating
source text from one language to another. As every task in machine learning domain it …

Existing text simplification or paraphrase datasets mainly focus on sentence-level text
generation in a general domain. These datasets are typically developed without using …