Tatoeba-Challenge VS AutomaticKeyphraseExtraction

Automatically translate a text from a language A to a language B. 1/ Dataset : we chose a dataset from the Language Technology Research Group at the University of Helsinki’s Tatoeba Translation Challenge . We took 100 of examples from different latin languages pairs : deu-fra, eng-fra, fra -ita, deu-spa , deu-swe which constitutes a 500 example test dataset.

https://translatelocally.com/ is a nice gui around marian/bergamot. So far not very many bundled pairs, though I would guess any of the models from https://github.com/Helsinki-NLP/Opus-MT-train/tree/master/mo... and https://github.com/Helsinki-NLP/Tatoeba-Challenge/blob/maste... should be usable.

There is also Apertium, a rule-based system which is very good for some closely-related pairs that have had a lot of work put into them (especially translation between Romance languages, e.g. Spanish→Catalan, and Norwegian Bokmål→Nynorsk), and the only OK translator for some lesser-resourced languages (e.g. Northern Saami→Norwegian Bokmål), but very underdeveloped for anything to/from English (it feels a bit pointless writing rules for English where there is so much available data; RBMT shines where there's not enough available data, ie. most of the languages of the world)

#1: University of Helsinki language technology professor Jörg Tiedemann has released a dataset with over 500 million translated sentences in 188 languages | 0 comments #2: The NLP Index: 3,000+ code repos for hackers and researchers. [self-promotion] #3: A Python library to boost T5 models speed up to 5x & reduce the model size by 3x.

#1: Matching GPT-3's performance with just 0.1% of its parameters #2: University of Helsinki language technology professor Jörg Tiedemann has released a dataset with over 500 million translated sentences in 188 languages | 0 comments #3: Trained a Markov Chain on a bunch of r/WSB posts and comments. Only 2-word conditional probabilities but honestly, that's all that's necessary 🚀🚀

Keyword or Keyphrase Extraction is about being able to extract the words or phrases that most represent a given text. 1/ Dataset: we selected our datasets from the public github repository AutomaticKeyphraseExtraction Most of the datasets listed there were too long for the 4k token limit of OpenAI so we had to go with the Hulth2003 abstracts dataset. Since the different providers are trained to return keywords and keyphrases present in the original text, we did some cleaning to remove all keywords that were not present in the abstracts. We ended up with 470 abstracts.