Search Results for author: Chrysoula Zerva

Found 15 papers, 8 papers with code

Findings of the WMT 2021 Shared Task on Quality Estimation

no code implementations WMT (EMNLP) 2021 Lucia Specia, Frédéric Blain, Marina Fomicheva, Chrysoula Zerva, Zhenhao Li, Vishrav Chaudhary, André F. T. Martins

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 word and sentence levels.

Machine Translation Translation

Uncertainty in Natural Language Generation: From Theory to Applications

no code implementations28 Jul 2023 Joris Baan, Nico Daheim, Evgenia Ilia, Dennis Ulmer, Haau-Sing Li, Raquel Fernández, Barbara Plank, Rico Sennrich, Chrysoula Zerva, Wilker Aziz

Recent advances of powerful Language Models have allowed Natural Language Generation (NLG) to emerge as an important technology that can not only perform traditional tasks like summarisation or translation, but also serve as a natural language interface to a variety of applications.

Active Learning Text Generation

Conformalizing Machine Translation Evaluation

no code implementations9 Jun 2023 Chrysoula Zerva, André F. T. Martins

Several uncertainty estimation methods have been recently proposed for machine translation evaluation.

Conformal Prediction Machine Translation +1

BLEU Meets COMET: Combining Lexical and Neural Metrics Towards Robust Machine Translation Evaluation

1 code implementation30 May 2023 Taisiya Glushkova, Chrysoula Zerva, André F. T. Martins

Although neural-based machine translation evaluation metrics, such as COMET or BLEURT, have achieved strong correlations with human judgements, they are sometimes unreliable in detecting certain phenomena that can be considered as critical errors, such as deviations in entities and numbers.

Machine Translation Translation

Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors

1 code implementation26 May 2023 Giorgos Filandrianos, Edmund Dervakos, Orfeas Menis-Mastromichalakis, Chrysoula Zerva, Giorgos Stamou

We propose a new back translation-inspired evaluation methodology that utilises earlier outputs of the explainer as ground truth proxies to investigate the consistency of explainers.

Disentangling Uncertainty in Machine Translation Evaluation

1 code implementation13 Apr 2022 Chrysoula Zerva, Taisiya Glushkova, Ricardo Rei, André F. T. Martins

Trainable evaluation metrics for machine translation (MT) exhibit strong correlation with human judgements, but they are often hard to interpret and might produce unreliable scores under noisy or out-of-domain data.

Machine Translation Translation

Semantic Enrichment of Pretrained Embedding Output for Unsupervised IR

no code implementations AAAI-MAKE 2021 Edmund Dervakos, Giorgos Filandrianos, Konstantinos Thomas, Alexios Mandalios, Chrysoula Zerva, Giorgos Stamou

The rapid growth of scientific literature in the biomedical and clinical domain has significantly com- plicated the identification of information of interest by researchers as well as other practitioners.

Information Retrieval Navigate

Paths for uncertainty: Exploring the intricacies of uncertainty identification for news

no code implementations WS 2018 Chrysoula Zerva, Sophia Ananiadou

We compare the differences in the definition and expression of uncertainty between a scientific domain, i. e., biomedicine, and newswire.

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