Discover and read the best of Twitter Threads about #eacl2021
Most recents (2)
🤦♀️ The only good thing about this is how much attention it attracted, so hopefully @icmlconf would reconsider.
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It can't even work, since peer review is only reliable for the clearly bad papers. Decisions on borderline papers are as good as random. This won't "raise the bar", it'll only reinforce the AC/SAC preferences. And likely improve the chances for preprinted papers by famous ppl.
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Check out our new awesome word aligner, AWESOME aligner by @ZiYiDou 😀: github.com/neulab/awesome…
* Uses multilingual BERT and can align sentences in all included languages
* No additional training needed, so you can align even a single sentence pair!
* Excellent accuracy
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* Uses multilingual BERT and can align sentences in all included languages
* No additional training needed, so you can align even a single sentence pair!
* Excellent accuracy
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A paper describing the methodology will appear at #EACL2021: arxiv.org/abs/2101.08231
The model is trained on parallel data using contrastive and self-training losses. But it generalizes zero-shot to new language pairs without any training data! 2/3
The model is trained on parallel data using contrastive and self-training losses. But it generalizes zero-shot to new language pairs without any training data! 2/3
Why do we need word alignments in the first place? We use them for lexicon learning, model analysis, and cross-lingual learning. For example, AWESOME aligner results in better cross-lingual results for annotation projection in NER. Check it out and we welcome comments/issues! 3/3
