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Found 13 papers, 4 papers with code
Monolingual Adapters for Zero-Shot Neural Machine Translation
We propose a novel adapter layer formalism for adapting multilingual models.
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.
Memory-efficient NLLB-200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Model
The recently released NLLB-200 is a set of multilingual Neural Machine Translation models that cover 202 languages.
SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages
In recent years, multilingual machine translation models have achieved promising performance on low-resource language pairs by sharing information between similar languages, thus enabling zero-shot translation.
What Do Compressed Multilingual Machine Translation Models Forget?
In this work, we assess the impact of compression methods on Multilingual Neural Machine Translation models (MNMT) for various language groups, gender, and semantic biases by extensive analysis of compressed models on different machine translation benchmarks, i. e. FLORES-101, MT-Gender, and DiBiMT.
Continual Learning in Multilingual NMT via Language-Specific Embeddings
This paper proposes a technique for adding a new source or target language to an existing multilingual NMT model without re-training it on the initial set of languages.
Efficient Inference for Multilingual Neural Machine Translation
Multilingual NMT has become an attractive solution for MT deployment in production.
Unsupervised Word Segmentation from Speech with Attention
We present a first attempt to perform attentional word segmentation directly from the speech signal, with the final goal to automatically identify lexical units in a low-resource, unwritten language (UL).
Unwritten Languages Demand Attention Too! Word Discovery with Encoder-Decoder Models
Word discovery is the task of extracting words from unsegmented text.
LIG-CRIStAL System for the WMT17 Automatic Post-Editing Task
This paper presents the LIG-CRIStAL submission to the shared Automatic Post- Editing task of WMT 2017.
Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation
This paper proposes a first attempt to build an end-to-end speech-to-text translation system, which does not use source language transcription during learning or decoding.
Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?
This paper presents an approach combining lexico-semantic resources and distributed representations of words applied to the evaluation in machine translation (MT).
