Multilingual Zero-shot Constituency Parsing

Taeuk Kim, Sang-goo Lee

Zero-shot constituency parsing aims to extract parse trees from neural models such as pre-trained language models (PLMs) without further training or the need to train an additional parser. This paper improves upon existing zero-shot parsing paradigms by introducing a novel chart-based parsing method, showing gains in zero-shot parsing performance. Furthermore, we attempt to broaden the range of zero-shot parsing applications by examining languages other than English and by utilizing multilingual models, demonstrating that it is feasible to generate parse tree-like structures for sentences in eight other languages using our method.

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