Search Results for author: Patrick Fernandes

Found 8 papers, 5 papers with code

CMU’s IWSLT 2022 Dialect Speech Translation System

no code implementations IWSLT (ACL) 2022 Brian Yan, Patrick Fernandes, Siddharth Dalmia, Jiatong Shi, Yifan Peng, Dan Berrebbi, Xinyi Wang, Graham Neubig, Shinji Watanabe

We use additional paired Modern Standard Arabic data (MSA) to directly improve the speech recognition (ASR) and machine translation (MT) components of our cascaded systems.

Knowledge Distillation Machine Translation +3

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 Translation

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.

Meta-Learning

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

When Does Translation Require Context? A Data-driven, Multilingual Exploration

no code implementations15 Sep 2021 Kayo Yin, Patrick Fernandes, André F. T. Martins, Graham Neubig

Although proper handling of discourse phenomena significantly contributes to the quality of machine translation (MT), common translation quality metrics do not adequately capture them.

Machine Translation Translation

Measuring and Increasing Context Usage in Context-Aware Machine Translation

1 code implementation ACL 2021 Patrick Fernandes, Kayo Yin, Graham Neubig, André F. T. Martins

Recent work in neural machine translation has demonstrated both the necessity and feasibility of using inter-sentential context -- context from sentences other than those currently being translated.

Document Level Machine Translation Machine Translation +1

Structured Neural Summarization

3 code implementations ICLR 2019 Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt

Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input.

Source Code Summarization

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