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pro vyhledávání: '"Jose, G. C."'
Autor:
Faria, Gonçalo R. A., Agrawal, Sweta, Farinhas, António, Rei, Ricardo, de Souza, José G. C., Martins, André F. T.
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 correlates poorly with translation quality. In contrast, quality evaluation
Externí odkaz:
http://arxiv.org/abs/2406.00049
Autor:
Alves, Duarte M., Pombal, José, Guerreiro, Nuno M., Martins, Pedro H., Alves, João, Farajian, Amin, Peters, Ben, Rei, Ricardo, Fernandes, Patrick, Agrawal, Sweta, Colombo, Pierre, de Souza, José G. C., Martins, André F. T.
While general-purpose large language models (LLMs) demonstrate proficiency on multiple tasks within the domain of translation, approaches based on open LLMs are competitive only when specializing on a single task. In this paper, we propose a recipe f
Externí odkaz:
http://arxiv.org/abs/2402.17733
Autor:
Alves, Duarte M., Guerreiro, Nuno M., Alves, João, Pombal, José, Rei, Ricardo, de Souza, José G. C., Colombo, Pierre, Martins, André F. T.
Large language models (LLMs) are a promising avenue for machine translation (MT). However, current LLM-based MT systems are brittle: their effectiveness highly depends on the choice of few-shot examples and they often require extra post-processing du
Externí odkaz:
http://arxiv.org/abs/2310.13448
Large language models (LLMs) are becoming a one-fits-many solution, but they sometimes hallucinate or produce unreliable output. In this paper, we investigate how hypothesis ensembling can improve the quality of the generated text for the specific pr
Externí odkaz:
http://arxiv.org/abs/2310.11430
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
Autor:
Huyan, S., Ryan, D. H., Slade, T. J., Lavina, B., Jose, G. C., Wang, H., Wilde, J. M., Ribeiro, R. A., Zhao, J., Xie, W., Bi, W., Alp, E. E., Bud'ko, S. L., Canfield, P. C.
Publikováno v:
Proc. Natl. Acad. Sci. 120 (52) e2310779120 (2023)
We present a comprehensive study of the mixed valent compound, EuPd3S4, by electrical transport, X-ray diffraction, time-domain 151Eu synchrotron M\"ossbauer spectroscopy, and X-ray absorption spectroscopy measurements under high pressure. The electr
Externí odkaz:
http://arxiv.org/abs/2306.16517
Autor:
Fernandes, Patrick, Madaan, Aman, Liu, Emmy, Farinhas, António, Martins, Pedro Henrique, Bertsch, Amanda, de Souza, José G. C., Zhou, Shuyan, Wu, Tongshuang, Neubig, Graham, Martins, André F. T.
Many recent advances in natural language generation have been fueled by training large language models on internet-scale data. However, this paradigm can lead to models that generate toxic, inaccurate, and unhelpful content, and automatic evaluation
Externí odkaz:
http://arxiv.org/abs/2305.00955
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
Autor:
Fernandes, Patrick, Farinhas, António, Rei, Ricardo, de Souza, José G. C., Ogayo, Perez, Neubig, Graham, Martins, André F. T.
Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model
Externí odkaz:
http://arxiv.org/abs/2205.00978
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