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Found 11 papers, 1 papers with code
Controlling Prosody in End-to-End TTS: A Case Study on Contrastive Focus Generation
In this paper, we investigate whether we can control prosody directly from the input text, in order to code information related to contrastive focus which emphasizes a specific word that is contrary to the presuppositions of the interlocutor.
Naver Labs Europe’s Participation in the Robustness, Chat, and Biomedical Tasks at WMT 2020
This paper describes Naver Labs Europe’s participation in the Robustness, Chat, and Biomedical Translation tasks at WMT 2020.
NAVER LABS Europe's Multilingual Speech Translation Systems for the IWSLT 2023 Low-Resource Track
This paper presents NAVER LABS Europe's systems for Tamasheq-French and Quechua-Spanish speech translation in the IWSLT 2023 Low-Resource track.
A Textless Metric for Speech-to-Speech Comparison
In this paper, we introduce a new and simple method for comparing speech utterances without relying on text transcripts.
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation
Domain Adaptation (DA) of Neural Machine Translation (NMT) model often relies on a pre-trained general NMT model which is adapted to the new domain on a sample of in-domain parallel data.
Naver Labs Europe's Systems for the Document-Level Generation and Translation Task at WNGT 2019
To address these challenges, we propose to leverage data from both tasks and do transfer learning between MT, NLG, and MT with source-side metadata (MT+NLG).
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.
Moment Matching Training for Neural Machine Translation: A Preliminary Study
In previous works, neural sequence models have been shown to improve significantly if external prior knowledge can be provided, for instance by allowing the model to access the embeddings of explicit features during both training and inference.
