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pro vyhledávání: '"Coheur P"'
Human biases have been shown to influence the performance of models and algorithms in various fields, including Natural Language Processing. While the study of this phenomenon is garnering focus in recent years, the available resources are still rela
Externí odkaz:
http://arxiv.org/abs/2408.07479
Brazilian Portuguese and European Portuguese are two varieties of the same language and, despite their close similarities, they exhibit several differences. However, there is a significant disproportion in the availability of resources between the tw
Externí odkaz:
http://arxiv.org/abs/2408.07457
Autor:
Guerreiro, Nuno M., Rei, Ricardo, van Stigt, Daan, Coheur, Luisa, Colombo, Pierre, Martins, André F. T.
Widely used learned metrics for machine translation evaluation, such as COMET and BLEURT, estimate the quality of a translation hypothesis by providing a single sentence-level score. As such, they offer little insight into translation errors (e.g., w
Externí odkaz:
http://arxiv.org/abs/2310.10482
Autor:
Rei, Ricardo, Guerreiro, Nuno M., Pombal, José, van Stigt, Daan, Treviso, Marcos, Coheur, Luisa, de Souza, José G. C., Martins, André F. T.
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- and word-level quality prediction (task 1) and fine-grained error span
Externí odkaz:
http://arxiv.org/abs/2309.11925
Fuzzy Fingerprints have been successfully used as an interpretable text classification technique, but, like most other techniques, have been largely surpassed in performance by Large Pre-trained Language Models, such as BERT or RoBERTa. These models
Externí odkaz:
http://arxiv.org/abs/2309.04292
State of the art models in intent induction require annotated datasets. However, annotating dialogues is time-consuming, laborious and expensive. In this work, we propose a completely unsupervised framework for intent induction within a dialogue. In
Externí odkaz:
http://arxiv.org/abs/2307.15410
Current signing avatars are often described as unnatural as they cannot accurately reproduce all the subtleties of synchronized body behaviors of a human signer. In this paper, we propose a new dynamic approach for transitions between signs, focusing
Externí odkaz:
http://arxiv.org/abs/2307.06124
Autor:
Rei, Ricardo, Guerreiro, Nuno M., Treviso, Marcos, Coheur, Luisa, Lavie, Alon, Martins, André F. T.
Neural metrics for machine translation evaluation, such as COMET, exhibit significant improvements in their correlation with human judgments, as compared to traditional metrics based on lexical overlap, such as BLEU. Yet, neural metrics are, to a gre
Externí odkaz:
http://arxiv.org/abs/2305.11806
Question Generation (QG) is a task of Natural Language Processing (NLP) that aims at automatically generating questions from text. Many applications can benefit from automatically generated questions, but often it is necessary to curate those questio
Externí odkaz:
http://arxiv.org/abs/2304.13664
Recent approaches have attempted to personalize dialogue systems by leveraging profile information into models. However, this knowledge is scarce and difficult to obtain, which makes the extraction/generation of profile information from dialogues a f
Externí odkaz:
http://arxiv.org/abs/2304.06634