A Sequence-to-Sequence Model for Semantic Role Labeling

By Angel Daza, Anette Frank. 2018

22 citations recorded by the source corpus.

We explore a novel approach for Semantic Role Labeling (SRL) by casting it as a sequence-to-sequence process. We employ an attention-based model enriched with a copying mechanism to ensure faithful regeneration of the input sequence, while enabling interleaved generation of argument role labels. We apply this model in a monolingual setting, performing PropBank SRL on English language data. The constrained sequence generation set-up enforced with the copying mechanism allows us to analyze the performance and special properties of the model on manually labeled data and benchmarking against state-of-the-art sequence labeling models. We show that our model is able to solve the SRL argument labeling task on English data, yet further structural decoding constraints will need to be added to make the model truly competitive. Our work represents the first step towards more advanced, generative SRL labeling setups.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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Citations in the corpus

Autoregressive Entity Retrieval (2020, background): “…sequence-to-sequence problems has been explored for different problems, including semantic parsing (Rongali et al., 2020), semantic role labelling (Daza & Frank, 2018), discourse representation structure parsing (Liu et al., 2018), generation of ﬂuent natural language responses from structured…”
COGS: A Compositional Generalization Challenge Based on Semantic Interpretation (2020, methodology): “Following Dong and Lapata (2016) and Daza and Frank (2018), we cast semantic parsing as a sequence-to-sequence problem.”