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Found 16 papers, 6 papers with code
fairseq Sˆ2: A Scalable and Integrable Speech Synthesis Toolkit
This paper presents fairseq Sˆ2, a fairseq extension for speech synthesis.
Findings of the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment
Following two preceding WMT Shared Task on Parallel Corpus Filtering (Koehn et al., 2018, 2019), we posed again the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting the highest-quality data to be used to train ma-chine translation systems.
Facebook AI’s WMT20 News Translation Task Submission
We approach the low resource problem using two main strategies, leveraging all available data and adapting the system to the target news domain.
Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation
Direct speech-to-speech translation (S2ST) models suffer from data scarcity issues as there exists little parallel S2ST data, compared to the amount of data available for conventional cascaded systems that consist of automatic speech recognition (ASR), machine translation (MT), and text-to-speech (TTS) synthesis.
Textless Speech-to-Speech Translation on Real Data
To our knowledge, we are the first to establish a textless S2ST technique that can be trained with real-world data and works for multiple language pairs.
Direct Simultaneous Speech-to-Speech Translation with Variational Monotonic Multihead Attention
We present a direct simultaneous speech-to-speech translation (Simul-S2ST) model, Furthermore, the generation of translation is independent from intermediate text representations.
fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit
This paper presents fairseq S^2, a fairseq extension for speech synthesis.
Direct speech-to-speech translation with discrete units
When target text transcripts are available, we design a joint speech and text training framework that enables the model to generate dual modality output (speech and text) simultaneously in the same inference pass.
The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation
One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks.
Facebook AI's WMT20 News Translation Task Submission
We approach the low resource problem using two main strategies, leveraging all available data and adapting the system to the target news domain.
Multilingual Translation with Extensible Multilingual Pretraining and Finetuning
Recent work demonstrates the potential of multilingual pretraining of creating one model that can be used for various tasks in different languages.
The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali--English and Sinhala--English
For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available.
Facebook AI's WAT19 Myanmar-English Translation Task Submission
This paper describes Facebook AI's submission to the WAT 2019 Myanmar-English translation task.
The Source-Target Domain Mismatch Problem in Machine Translation
While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only to the specific place we live in.
The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English
For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available.
