Open-Domain Frame Semantic Parsing Using Transformers

• URL: http://arxiv.org/abs/2010.10998v2
• Date: Fri, 23 Oct 2020 23:37:12 GMT
• Title: Open-Domain Frame Semantic Parsing Using Transformers
• Abstract summary: We show that a purely generative encoder-decoder architecture handily beats the previous state of the art in FrameNet 1.7 parsing. We also show that a mixed decoding multi-task approach achieves even better performance.
• Score: 4.335237318717164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Frame semantic parsing is a complex problem which includes multiple underlying subtasks. Recent approaches have employed joint learning of subtasks (such as predicate and argument detection), and multi-task learning of related tasks (such as syntactic and semantic parsing). In this paper, we explore multi-task learning of all subtasks with transformer-based models. We show that a purely generative encoder-decoder architecture handily beats the previous state of the art in FrameNet 1.7 parsing, and that a mixed decoding multi-task approach achieves even better performance. Finally, we show that the multi-task model also outperforms recent state of the art systems for PropBank SRL parsing on the CoNLL 2012 benchmark.

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