Thursday, 16th of December 2021, 12:00 – 1:00

Predicting Temporal Validity of Text

Venue: 
online - link

Lecturer:
Adam Jatowt, Researcher at DS and DiSC

Abstract: 

Knowing whether and how long information remains valid is important in various applications including user state tracking in social network services and in chatbot conversations, as well as is beneficial for deep story understanding. However, such an inference remains still a difficult problem for machines as it often requires temporal common-sense reasoning. We propose a novel task, Temporal Natural Language Inference, designed to determine the temporal validity of text content, which is inspired by natural language reasoning in NLP. The task requires inference whether an action expressed in a sentence is still ongoing or has been rather completed (hence whether the sentence still remains valid or rather has become invalid) in view of additional information provided in the form of either supplementary sentence or of elapsed time period. We created a large-scale dataset for this task and develop effective models for its solution.

 

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