4 research outputs found
Trans-gram, Fast Cross-lingual Word-embeddings
We introduce Trans-gram, a simple and computationally-efficient method to
simultaneously learn and align wordembeddings for a variety of languages, using
only monolingual data and a smaller set of sentence-aligned data. We use our
new method to compute aligned wordembeddings for twenty-one languages using
English as a pivot language. We show that some linguistic features are aligned
across languages for which we do not have aligned data, even though those
properties do not exist in the pivot language. We also achieve state of the art
results on standard cross-lingual text classification and word translation
tasks.Comment: EMNLP 201
SeamlessM4T-Massively Multilingual & Multimodal Machine Translation
What does it take to create the Babel Fish, a tool that can help individuals
translate speech between any two languages? While recent breakthroughs in
text-based models have pushed machine translation coverage beyond 200
languages, unified speech-to-speech translation models have yet to achieve
similar strides. More specifically, conventional speech-to-speech translation
systems rely on cascaded systems that perform translation progressively,
putting high-performing unified systems out of reach. To address these gaps, we
introduce SeamlessM4T, a single model that supports speech-to-speech
translation, speech-to-text translation, text-to-speech translation,
text-to-text translation, and automatic speech recognition for up to 100
languages. To build this, we used 1 million hours of open speech audio data to
learn self-supervised speech representations with w2v-BERT 2.0. Subsequently,
we created a multimodal corpus of automatically aligned speech translations.
Filtered and combined with human-labeled and pseudo-labeled data, we developed
the first multilingual system capable of translating from and into English for
both speech and text. On FLEURS, SeamlessM4T sets a new standard for
translations into multiple target languages, achieving an improvement of 20%
BLEU over the previous SOTA in direct speech-to-text translation. Compared to
strong cascaded models, SeamlessM4T improves the quality of into-English
translation by 1.3 BLEU points in speech-to-text and by 2.6 ASR-BLEU points in
speech-to-speech. Tested for robustness, our system performs better against
background noises and speaker variations in speech-to-text tasks compared to
the current SOTA model. Critically, we evaluated SeamlessM4T on gender bias and
added toxicity to assess translation safety. Finally, all contributions in this
work are open-sourced and accessible at
https://github.com/facebookresearch/seamless_communicatio