Assessment of Pre-Trained Models Across Languages and Grammars

• URL: http://arxiv.org/abs/2309.11165v1
• Date: Wed, 20 Sep 2023 09:23:36 GMT
• Title: Assessment of Pre-Trained Models Across Languages and Grammars
• Authors: Alberto Mu\~noz-Ortiz and David Vilares and Carlos G\'omez-Rodr\'iguez
• Abstract summary: We aim to recover constituent and dependency structures by casting parsing as sequence labeling. Our results show that pre-trained word vectors do not favor constituency representations of syntax over dependencies. occurrence of a language in the pretraining data is more important than the amount of task data when recovering syntax from the word vectors.
• Score: 7.466159270333272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present an approach for assessing how multilingual large language models (LLMs) learn syntax in terms of multi-formalism syntactic structures. We aim to recover constituent and dependency structures by casting parsing as sequence labeling. To do so, we select a few LLMs and study them on 13 diverse UD treebanks for dependency parsing and 10 treebanks for constituent parsing. Our results show that: (i) the framework is consistent across encodings, (ii) pre-trained word vectors do not favor constituency representations of syntax over dependencies, (iii) sub-word tokenization is needed to represent syntax, in contrast to character-based models, and (iv) occurrence of a language in the pretraining data is more important than the amount of task data when recovering syntax from the word vectors.

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