Search Results for author: Ricardo Rei

Found 24 papers, 16 papers with code

QUARTZ: Quality-Aware Machine Translation

no code implementations EAMT 2022 José G.C. de Souza, Ricardo Rei, Ana C. Farinha, Helena Moniz, André F. T. Martins

This paper presents QUARTZ, QUality-AwaRe machine Translation, a project led by Unbabel which aims at developing machine translation systems that are more robust and produce fewer critical errors.

Machine Translation Translation

Steering Large Language Models for Machine Translation with Finetuning and In-Context Learning

no code implementations20 Oct 2023 Duarte M. Alves, Nuno M. Guerreiro, João Alves, José Pombal, Ricardo Rei, José G. C. de Souza, Pierre Colombo, André F. T. Martins

Experiments on 10 language pairs show that our proposed approach recovers the original few-shot capabilities while keeping the added benefits of finetuning.

Machine Translation Translation

xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection

1 code implementation16 Oct 2023 Nuno M. Guerreiro, Ricardo Rei, Daan van Stigt, Luisa Coheur, Pierre Colombo, André F. T. Martins

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.

Machine Translation Translation

Scaling up COMETKIWI: Unbabel-IST 2023 Submission for the Quality Estimation Shared Task

1 code implementation21 Sep 2023 Ricardo Rei, Nuno M. Guerreiro, José Pombal, Daan van Stigt, Marcos Treviso, Luisa Coheur, José G. C. de Souza, André F. T. Martins

Our team participated on all tasks: sentence- and word-level quality prediction (task 1) and fine-grained error span detection (task 2).

The Inside Story: Towards Better Understanding of Machine Translation Neural Evaluation Metrics

1 code implementation19 May 2023 Ricardo Rei, Nuno M. Guerreiro, Marcos Treviso, Luisa Coheur, Alon Lavie, André F. T. Martins

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.

Decision Making Machine Translation +1

Towards a Sentiment-Aware Conversational Agent

no code implementations24 Jul 2022 Isabel Dias, Ricardo Rei, Patrícia Pereira, Luisa Coheur

In this paper, we propose an end-to-end sentiment-aware conversational agent based on two models: a reply sentiment prediction model, which leverages the context of the dialogue to predict an appropriate sentiment for the agent to express in its reply; and a text generation model, which is conditioned on the predicted sentiment and the context of the dialogue, to produce a reply that is both context and sentiment appropriate.

Sentiment Analysis Sentiment Classification +1

Quality-Aware Decoding for Neural Machine Translation

1 code implementation NAACL 2022 Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, André F. T. Martins

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 (MAP decoding), approximated with beam search.

Machine Translation NMT +1

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

Onception: Active Learning with Expert Advice for Real World Machine Translation

1 code implementation9 Mar 2022 Vânia Mendonça, Ricardo Rei, Luisa Coheur, Alberto Sardinha

Moreover, since we not know in advance which query strategy will be the most adequate for a certain language pair and set of Machine Translation models, we propose to dynamically combine multiple strategies using prediction with expert advice.

Active Learning Machine Translation +1

MT-Telescope: An interactive platform for contrastive evaluation of MT systems

no code implementations ACL 2021 Ricardo Rei, Ana C Farinha, Craig Stewart, Luisa Coheur, Alon Lavie

We present MT-Telescope, a visualization platform designed to facilitate comparative analysis of the output quality of two Machine Translation (MT) systems.

Machine Translation Translation

Multilingual Email Zoning

1 code implementation EACL 2021 Bruno Jardim, Ricardo Rei, Mariana S. C. Almeida

The segmentation of emails into functional zones (also dubbed email zoning) is a relevant preprocessing step for most NLP tasks that deal with emails.

Domain Adaptation Segmentation

Unbabel's Participation in the WMT20 Metrics Shared Task

1 code implementation29 Oct 2020 Ricardo Rei, Craig Stewart, Catarina Farinha, Alon Lavie

Overall, our systems achieve strong results for all language pairs on previous test sets and in many cases set a new state-of-the-art.


COMET: A Neural Framework for MT Evaluation

1 code implementation EMNLP 2020 Ricardo Rei, Craig Stewart, Ana C Farinha, Alon Lavie

We present COMET, a neural framework for training multilingual machine translation evaluation models which obtains new state-of-the-art levels of correlation with human judgements.

Language Modelling Machine Translation +1

Cannot find the paper you are looking for? You can Submit a new open access paper.