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Found 9 papers, 1 papers with code
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness
We share a French-English parallel corpus of Foursquare restaurant reviews (https://europe. naverlabs. com/research/natural-language-processing/machine-translation-of-restaurant-reviews), and define a new task to encourage research on Neural Machine Translation robustness and domain adaptation, in a real-world scenario where better-quality MT would be greatly beneficial.
Naver Labs Europe's Systems for the WMT19 Machine Translation Robustness Task
This paper describes the systems that we submitted to the WMT19 Machine Translation robustness task.
Un syst\`eme hybride pour l'analyse de sentiments associ\'es aux aspects
Nous pr{\'e}sentons la t{\^a}che et d{\'e}crivons pr{\'e}cis{\'e}ment notre syst{\`e}me qui consiste en une combinaison de composants linguistiques et de modules de classification.
Investigating the Image of Entities in Social Media: Dataset Design and First Results
The objective of this paper is to describe the design of a dataset that deals with the image (i. e., representation, web reputation) of various entities populating the Internet: politicians, celebrities, companies, brands etc.
