Zobrazeno 1 - 10
of 1 831
pro vyhledávání: '"Zerva, A."'
Autor:
Thomas, Konstantinos, Filandrianos, Giorgos, Lymperaiou, Maria, Zerva, Chrysoula, Stamou, Giorgos
Equivocation and ambiguity in public speech are well-studied discourse phenomena, especially in political science and analysis of political interviews. Inspired by the well-grounded theory on equivocation, we aim to resolve the closely related proble
Externí odkaz:
http://arxiv.org/abs/2409.13879
Autor:
Campos, Margarida M., Farinhas, António, Zerva, Chrysoula, Figueiredo, Mário A. T., Martins, André F. T.
The rapid proliferation of large language models and natural language processing (NLP) applications creates a crucial need for uncertainty quantification to mitigate risks such as hallucinations and to enhance decision-making reliability in critical
Externí odkaz:
http://arxiv.org/abs/2405.01976
Quantifying uncertainty in automatically generated text is important for letting humans check potential hallucinations and making systems more reliable. Conformal prediction is an attractive framework to provide predictions imbued with statistical gu
Externí odkaz:
http://arxiv.org/abs/2402.00707
Publikováno v:
In Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track (pp. 272-285) 2023
Despite the remarkable advancements in machine translation, the current sentence-level paradigm faces challenges when dealing with highly-contextual languages like Japanese. In this paper, we explore how context-awareness can improve the performance
Externí odkaz:
http://arxiv.org/abs/2311.11976
Split conformal prediction has recently sparked great interest due to its ability to provide formally guaranteed uncertainty sets or intervals for predictions made by black-box neural models, ensuring a predefined probability of containing the actual
Externí odkaz:
http://arxiv.org/abs/2310.01262
Autor:
Baan, Joris, Daheim, Nico, Ilia, Evgenia, Ulmer, Dennis, Li, Haau-Sing, Fernández, Raquel, Plank, Barbara, Sennrich, Rico, Zerva, Chrysoula, Aziz, Wilker
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 interf
Externí odkaz:
http://arxiv.org/abs/2307.15703
Autor:
Zerva, Chrysoula, Martins, André F. T.
Several uncertainty estimation methods have been recently proposed for machine translation evaluation. While these methods can provide a useful indication of when not to trust model predictions, we show in this paper that the majority of them tend to
Externí odkaz:
http://arxiv.org/abs/2306.06221
BLEU Meets COMET: Combining Lexical and Neural Metrics Towards Robust Machine Translation Evaluation
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, su
Externí odkaz:
http://arxiv.org/abs/2305.19144
Autor:
Filandrianos, Giorgos, Dervakos, Edmund, Menis-Mastromichalakis, Orfeas, Zerva, Chrysoula, Stamou, Giorgos
In the wake of responsible AI, interpretability methods, which attempt to provide an explanation for the predictions of neural models have seen rapid progress. In this work, we are concerned with explanations that are applicable to natural language p
Externí odkaz:
http://arxiv.org/abs/2305.17055
Autor:
Rei, Ricardo, Treviso, Marcos, Guerreiro, Nuno M., Zerva, Chrysoula, Farinha, Ana C., Maroti, Christine, de Souza, José G. C., Glushkova, Taisiya, Alves, Duarte M., Lavie, Alon, Coheur, Luisa, Martins, André F. T.
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-level Quality Prediction; (ii) Explainable QE; and (iii) Critical Error De
Externí odkaz:
http://arxiv.org/abs/2209.06243