activity
20152021
most citedEvaluating the Underlying Gender Bias in Contextualized Word Embeddings

27 citations · 51 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CL20212 cited

Sparsely Factored Neural Machine Translation

Noe Casas, Jose A. R. Fonollosa, Marta R. Costa-jussà

The standard approach to incorporate linguistic information to neural machine translation systems consists in maintaining separate vocabularies for each of the annotated features t…

cs.CL20202 cited

Aspects of Terminological and Named Entity Knowledge within Rule-Based Machine Translation Models for Under-Resourced Neural Machine Translation Scenarios

Daniel Torregrosa, Nivranshu Pasricha, Maraim Masoud +4

Rule-based machine translation is a machine translation paradigm where linguistic knowledge is encoded by an expert in the form of rules that translate text from source to target l…

cs.CL2020

Syntax-driven Iterative Expansion Language Models for Controllable Text Generation

Noe Casas, José A. R. Fonollosa, Marta R. Costa-jussà

The dominant language modeling paradigm handles text as a sequence of discrete tokens. While that approach can capture the latent structure of the text, it is inherently constraine…

cs.CL201919 cited

Joint Source-Target Self Attention with Locality Constraints

José A. R. Fonollosa, Noe Casas, Marta R. Costa-jussà

The dominant neural machine translation models are based on the encoder-decoder structure, and many of them rely on an unconstrained receptive field over source and target sequence…

cs.CL201927 cited

Evaluating the Underlying Gender Bias in Contextualized Word Embeddings

Christine Basta, Marta R. Costa-jussà, Noe Casas

Gender bias is highly impacting natural language processing applications. Word embeddings have clearly been proven both to keep and amplify gender biases that are present in curren…

cs.CL2018

English-Catalan Neural Machine Translation in the Biomedical Domain through the cascade approach

Marta R. Costa-jussà, Noe Casas, Maite Melero

This paper describes the methodology followed to build a neural machine translation system in the biomedical domain for the English-Catalan language pair. This task can be consider…