STEPS: Semantic Typing of Event Processes with a Sequence-to-Sequence Approach

Authors

  • Sveva Pepe Sapienza University of Rome
  • Edoardo Barba Sapienza University of Rome
  • Rexhina Blloshmi Sapienza University of Rome
  • Roberto Navigli Sapienza University of Rome

DOI:

https://doi.org/10.1609/aaai.v36i10.21365

Keywords:

Speech & Natural Language Processing (SNLP)

Abstract

Enabling computers to comprehend the intent of human actions by processing language is one of the fundamental goals of Natural Language Understanding. An emerging task in this context is that of free-form event process typing, which aims at understanding the overall goal of a protagonist in terms of an action and an object, given a sequence of events. This task was initially treated as a learning-to-rank problem by exploiting the similarity between processes and action/object textual definitions. However, this approach appears to be overly complex, binds the output types to a fixed inventory for possible word definitions and, moreover, leaves space for further enhancements as regards performance. In this paper, we advance the field by reformulating the free-form event process typing task as a sequence generation problem and put forward STEPS, an end-to-end approach for producing user intent in terms of actions and objects only, dispensing with the need for their definitions. In addition to this, we eliminate several dataset constraints set by previous works, while at the same time significantly outperforming them. We release the data and software at https://github.com/SapienzaNLP/steps.

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Published

2022-06-28

How to Cite

Pepe, S., Barba, E., Blloshmi, R., & Navigli, R. (2022). STEPS: Semantic Typing of Event Processes with a Sequence-to-Sequence Approach. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10), 11156-11164. https://doi.org/10.1609/aaai.v36i10.21365

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing