Search Results for author: Marcos Treviso

Found 15 papers, 10 papers with code

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).

CREST: A Joint Framework for Rationalization and Counterfactual Text Generation

1 code implementation26 May 2023 Marcos Treviso, Alexis Ross, Nuno M. Guerreiro, André F. T. Martins

Selective rationales and counterfactual examples have emerged as two effective, complementary classes of interpretability methods for analyzing and training NLP models.

Data Augmentation Text Generation

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

Learning to Scaffold: Optimizing Model Explanations for Teaching

1 code implementation22 Apr 2022 Patrick Fernandes, Marcos Treviso, Danish Pruthi, André F. T. Martins, Graham Neubig

In this work, leveraging meta-learning techniques, we extend this idea to improve the quality of the explanations themselves, specifically by optimizing explanations such that student models more effectively learn to simulate the original model.


Predicting Attention Sparsity in Transformers

no code implementations spnlp (ACL) 2022 Marcos Treviso, António Góis, Patrick Fernandes, Erick Fonseca, André F. T. Martins

Transformers' quadratic complexity with respect to the input sequence length has motivated a body of work on efficient sparse approximations to softmax.

Language Modelling Machine Translation +3

Sparse Continuous Distributions and Fenchel-Young Losses

1 code implementation4 Aug 2021 André F. T. Martins, Marcos Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae

In contrast, for finite domains, recent work on sparse alternatives to softmax (e. g., sparsemax, $\alpha$-entmax, and fusedmax), has led to distributions with varying support.

Audio Classification Question Answering +1

Sparse and Continuous Attention Mechanisms

2 code implementations NeurIPS 2020 André F. T. Martins, António Farinhas, Marcos Treviso, Vlad Niculae, Pedro M. Q. Aguiar, Mário A. T. Figueiredo

Exponential families are widely used in machine learning; they include many distributions in continuous and discrete domains (e. g., Gaussian, Dirichlet, Poisson, and categorical distributions via the softmax transformation).

Machine Translation Question Answering +4

Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks

3 code implementations WS 2017 Nathan Hartmann, Erick Fonseca, Christopher Shulby, Marcos Treviso, Jessica Rodrigues, Sandra Aluisio

Word embeddings have been found to provide meaningful representations for words in an efficient way; therefore, they have become common in Natural Language Processing sys- tems.

POS Semantic Similarity +2

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