LASER VS MUSE

I'm curious how people are handling multi-lingual embeddings.

I've found LASER[1] which originally had the idea to embed all languages in the same vector space, though it's a bit harder to use than models available through SentenceTransformers. LASER2 stuck with this approach, but LASER3 switched to language-specific models. However, I haven't found benchmarks for these models, and they were released about 2 years ago.

Another alternative would be to translate everything before embedding, which would introduce some amount of error, though maybe it wouldn't be significant.

1. https://github.com/facebookresearch/LASER

You can check out some of our materials and open sourced artifacts here:  - Our latest blog post: https://ai.facebook.com/blog/nllb-200-high-quality-machine-translation - Project Overview: https://ai.facebook.com/research/no-language-left-behind/  - Product demo: https://nllb.metademolab.com/  - Research paper: https://research.facebook.com/publications/no-language-left-behind - NLLB-200: https://github.com/facebookresearch/fairseq/tree/nllb - FLORES-200: https://github.com/facebookresearch/flores - LASER3: https://github.com/facebookresearch/LASER   Joining us today for the AMA are: - Angela Fan (AF), Research Scientist  - Jean Maillard (JM), Research Scientist - Maha Elbayad (ME), Research Scientist - Philipp Koehn (PK), Research Scientist - Shruti Bhosale (SB), Software Engineer   We’ll be here from 07/21/2022 @09:00AM PT - 10:00AM PT    Thanks and we’re looking forward to answering your questions!

This may be helpful: https://github.com/facebookresearch/LASER

Check FB's LASER: https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix Also , Sentence-Transformers has a pretty neat model for crosslingual sentence similarity: https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual

You want LASER its a superbig model trained on tons of languages you can use it with sentence_transformers in python to compute embedings. Then you can use faiss or datasketch to find matches at K

This is a great guide.

Also - despite the fact that language model embedding [1] are currently the hot rage, good old embedding models are more than good enough for most tasks.

With just a bit of tuning, they're generally as good at many sentence embedding tasks [2], and with good libraries [3] you're getting something like 400k sentence/sec on laptop CPU versus ~4k-15k sentences/sec on a v100 for LM embeddings.

When you should use language model embeddings:

- Multilingual tasks. While some embedding models are multilingual aligned (eg. MUSE [4]), you still need to route the sentence to the correct embedding model file (you need something like langdetect). It's also cumbersome, with one 400mb file per language.

For LM embedding models, many are multilingual aligned right away.

- Tasks that are very context specific or require fine-tuning. For instance, if you're making a RAG system for medical documents, the embedding space is best when it creates larger deviations for the difference between seemingly-related medical words.

This means models with more embedding dimensions, and heavily favors LM models over classic embedding models.

1. sbert.net

2. https://collaborate.princeton.edu/en/publications/a-simple-b...

3. https://github.com/oborchers/Fast_Sentence_Embeddings

4. https://github.com/facebookresearch/MUSE

I am looking for the best way to get an AI-generated bilingual dictionary, so that I can get a list of words with their translations for each language pair I want. It is possible to get a list (with sometimes alright, sometimes bad results) using this project. Additionally, there exists this, but it does not have a whole lot of words unfortunately. I also read about the huge CCMatrix dataset which has millions of parallel sentences for many language pairs, but how would I extract direct word translations from it? (A naive python algorithm would probably take forever.)

We currently train our own vocabularies on Wikipedia and other sources, and we align the vocabularies using MUSE with default settings (0-5000 dictionary for training, 5000-6500 dictionary for evaluation and 5 refinements).

MUSE embeddings has an unsupervised approach based on adversarial training: https://github.com/facebookresearch/MUSE#the-unsupervised-way-adversarial-training-and-refinement-cpugpu