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arXiv · 2010.10998

Open-Domain Frame Semantic Parsing Using Transformers

Aditya Kalyanpur·Or Biran·Tom Breloff·Jennifer Chu-Carroll·Ariel Diertani·Owen Rambow·Mark Sammons
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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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Aditya Kalyanpur, Or Biran, Tom Breloff, Jennifer Chu-Carroll, Ariel Diertani, Owen Rambow, Mark Sammons. 2020-10-21. Open-Domain Frame Semantic Parsing Using Transformers. https://arxiv.org/abs/2010.10998

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