| Challenge: | Existing methods to induce grammars of multiple languages do not consider language similarity measures. |
| Approach: | They propose a universal grammar induction approach that captures similarity between languages . they use vector representations to capture similarity and softly tie grammar parameters . |
| Outcome: | The proposed approach performs well over monolingual and multilingual datasets. |
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| Challenge: | Existing methods for learning vector space representations of words are based on word-context information. |
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Multilingual Culture-Independent Word Analogy Datasets (2020.lrec-1)
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| Challenge: | Recent work on latent tree learning attempts to develop models with parse-valued latent variables and train them on non-parsing tasks. |
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| Challenge: | Existing grammar induction methods do not provide sufficient performance in downstream tasks. |
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