This article was first published on SingularityNET - Medium
While recent advances in language processing with Deep Neural Networks (DNNs) present high-quality translation and classification of the texts, the Holy Grail of the language learning remains missed. That is, while humans appear capable to acquire languages in unsupervised way based on everyday conversations easily, the DNNs require extensive supervised training. Moreover, the humans are capable to acquire explainable and reasonable rules of connecting words into sentences based on grammatical rules and conversational patterns and have the grammatical and semantic categories of words well understood, with all that synonyms and homonyms. On the opposite, the very advanced DNN models remain black boxes not being understandable and inspectable.
That is why we are looking for Understandable Language Processing (ULP) which would let acquisition of the language, comprehension of textual communications and production of textual messages in reasonable and transparent way. One of the directions of our studies is Unsupervised Language Learning (ULL) project being executed by SingularityNET. The goal of the project is to enable acquisition of the language grammar from un-labeled text corpora programmatically in unsupervised way, with the learned knowledge stored in a human-readable and reasonable representation in form of Link Grammar dictionary, as it has been explained in our previous publication on the matter.
The latest update on the Unsupervised learning project has been delivered on AGI-2019 conference in Shenzhen, China. The paper called Programmatic Link Grammar Induction for Unsupervised Language Learning (with slides) has been presented. The following video shows that grammar can be learned based on the high-quality parses provided as the input, but the problem of getting these parses in ...
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SingularityNET - Medium