Besides these traditional NLP tasks, we also present a multilingual model for lost language deciphersment. We test this model on the ancient Ugaritic language.
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Apr 10, 2020 · We propose a simple, two-stage framework in which we decouple the above two sub-problems and solve them separately using existing techniques.
When distant languages are involved, the proposed approach shows robust behavior and outperforms existing unsupervised multilingual word embedding approaches.
Unsupervised MWE (UMWE) methods acquire multilingual embeddings without cross-lingual supervision, which is a significant advantage over traditional supervised ...
Duration: 1:23:35
Posted: Aug 14, 2023
Posted: Aug 14, 2023
We study unsupervised multilingual alignment, the problem of finding word-to-word translations between multiple languages without using any parallel data.
For the task of part-of-speech tagging, we developed a model and learning algorithm that scale linearly in the number of languages in terms of both time and ...
This unsupervised algorithm is capable of learning complex syntax, generating grammatical novel sentences, and proving useful in other fields that call for ...
Besides these traditional NLP tasks, we also present a multilingual model for lost language decipherment. We test this model on the ancient Ugaritic language.
Jan 28, 2020 · We study unsupervised multilingual alignment, the problem of finding word-to-word translations between multiple languages without using any parallel data.