2 citations · 5 across the 17 of their papers we have counts for
9 papers · 1 filter
Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings
Miguel Moura Ramos, Tomás Almeida, Daniel Vareta +4
Reinforcement learning (RL) has been proven to be an effective and robust method for training neural machine translation systems, especially when paired with powerful reward models…
Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation
Sweta Agrawal, José G. C. de Souza, Ricardo Rei +5
Alignment with human preferences is an important step in developing accurate and safe large language models. This is no exception in machine translation (MT), where better handling…
Watching the Watchers: Exposing Gender Disparities in Machine Translation Quality Estimation
Emmanouil Zaranis, Giuseppe Attanasio, Sweta Agrawal +1
Quality estimation (QE)-the automatic assessment of translation quality-has recently become crucial across several stages of the translation pipeline, from data curation to trainin…
Reranking Laws for Language Generation: A Communication-Theoretic Perspective
António Farinhas, Haau-Sing Li, André F. T. Martins
To ensure large language models (LLMs) are used safely, one must reduce their propensity to hallucinate or to generate unacceptable answers. A simple and often used strategy is to…
DOCE: Finding the Sweet Spot for Execution-Based Code Generation
Haau-Sing Li, Patrick Fernandes, Iryna Gurevych +1
Recently, a diverse set of decoding and reranking procedures have been shown effective for LLM-based code generation. However, a comprehensive framework that links and experimental…
QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation
Gonçalo R. A. Faria, Sweta Agrawal, António Farinhas +3
An important challenge in machine translation (MT) is to generate high-quality and diverse translations. Prior work has shown that the estimated likelihood from the MT model correl…