Revisiting the Practical Effectiveness of Constituency Parse Extraction from Pre-trained Language Models

• URL: http://arxiv.org/abs/2211.00479v1
• Date: Thu, 15 Sep 2022 09:41:19 GMT
• Title: Revisiting the Practical Effectiveness of Constituency Parse Extraction from Pre-trained Language Models
• Authors: Taeuk Kim
• Abstract summary: Constituency Parse Extraction from Pre-trained Language Models (CPE-PLM) is a recent paradigm that attempts to induce constituency parse trees relying only on the internal knowledge of pre-trained language models. We show that CPE-PLM is more effective than typical supervised parsing in few-shot settings.
• Score: 6.850627526999892
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Constituency Parse Extraction from Pre-trained Language Models (CPE-PLM) is a recent paradigm that attempts to induce constituency parse trees relying only on the internal knowledge of pre-trained language models. While attractive in the perspective that similar to in-context learning, it does not require task-specific fine-tuning, the practical effectiveness of such an approach still remains unclear, except that it can function as a probe for investigating language models' inner workings. In this work, we mathematically reformulate CPE-PLM and propose two advanced ensemble methods tailored for it, demonstrating that the new parsing paradigm can be competitive with common unsupervised parsers by introducing a set of heterogeneous PLMs combined using our techniques. Furthermore, we explore some scenarios where the trees generated by CPE-PLM are practically useful. Specifically, we show that CPE-PLM is more effective than typical supervised parsers in few-shot settings.

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