Causal BERT: Language Models for Causality Detection Between Events Expressed in Text

Vivek Khetan*, Roshni Ramnani, Mayuresh Anand, Subhashis Sengupta, Andrew E. Fano

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Causality understanding between events is a critical natural language processing task that is helpful in many areas, including health care, business risk management, and finance. On close examination, one can find a huge amount of textual content both in the form of formal documents or in content arising from social media like Twitter, dedicated to communicating and exploring various types of causality in the real world. Recognizing these “Cause-effect" relationships between natural language events continues to remain a challenge simply because it is often expressed implicitly. Implicit causality is hard to detect through most of the techniques employed in literature and can also, at times be perceived as ambiguous or vague. Also, although well-known datasets do exist for this problem, the examples in them are limited in the range and complexity of the causal relationships they depict especially when related to implicit relationships. Most of the contemporary methods are either based on lexico-semantic pattern matching or are feature-driven supervised algorithms. Therefore, these methods are more geared towards handling explicit causal relationships leading to limited coverage for implicit relationships, and are hard to generalize. In this paper, we investigate the language model’s capabilities for causal association among events expressed in natural language text using sentence context combined with event information, and by leveraging masked event context with in-domain and out-of-domain data distribution. Our proposed methods achieve the state-of-art performance in three different data distributions and can be leveraged for extraction of a causal diagram and/or building a chain of events from unstructured text.

Original languageEnglish (US)
Title of host publicationIntelligent Computing - Proceedings of the 2021 Computing Conference
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Pages965-980
Number of pages16
ISBN (Print)9783030801182
DOIs
StatePublished - 2022
Externally publishedYes
EventComputing Conference, 2021 - Virtual, Online
Duration: Jul 15 2021Jul 16 2021

Publication series

NameLecture Notes in Networks and Systems
Volume283
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceComputing Conference, 2021
CityVirtual, Online
Period7/15/217/16/21

Keywords

  • Causal relations
  • Causality extraction
  • Causality in natural language text
  • Cause-effect
  • Language models

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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