Abstract

This paper studies a new cognitively motivated semantic typing task, multi-axis event process typing, that, given an event process, attempts to infer free-form type labels describing (i) the type of action made by the process and (ii) the type of object the process seeks to affect. This task is inspired by computational and cognitive studies of event understanding, which suggest that understanding processes of events is often directed by recognizing the goals, plans or intentions of the protagonist(s). We develop a large dataset containing over 60k event processes, featuring ultra fine-grained typing on both the action and object type axes with very large ($10^3\sim 10^4$) label vocabularies. We then propose a hybrid learning framework, P2GT, which addresses the challenging typing problem with indirect supervision from glosses1and a joint learning-to-rank framework. As our experiments indicate, P2GT supports identifying the intent of processes, as well as the fine semantic type of the affected object. It also demonstrates the capability of handling few-shot cases, and strong generalizability on out-of-domain event processes.

Highlights

  • IntroductionTo help machines understand events, extensive research effort has been devoted to inducing how events described in text are procedurally connected (Ning et al, 2017; Radinsky et al, 2012), and how they form event processes (Pichotta and Mooney, 2014; Berant et al, 2014; Jindal and Roth, 2013)

  • Events are the fundamental building blocks of natural languages

  • We report the results of event process typing on both axes in Table 1, whereof the results for typing actions are generally better than those for the object

Read more

Summary

Introduction

To help machines understand events, extensive research effort has been devoted to inducing how events described in text are procedurally connected (Ning et al, 2017; Radinsky et al, 2012), and how they form event processes (Pichotta and Mooney, 2014; Berant et al, 2014; Jindal and Roth, 2013). Inducing intentions is crucial to rich understanding of text (Rashkin et al, 2018), and could potentially support other applications such as commonsense reasoning (Sap et al, 2019), summarization (Daume III and Marcu, 2006), reading comprehension (Berant et al, 2014) and schema induction (Huang et al, 2016)

Methods
Results
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.