  Temporal validity is an important property of text that is useful for many
downstream applications, such as recommender systems, conversational AI, or
story understanding. Existing benchmarking tasks often require models to
identify the temporal validity duration of a single statement. However, in many
cases, additional contextual information, such as sentences in a story or posts
on a social media profile, can be collected from the available text stream.
This contextual information may greatly alter the duration for which a
statement is expected to be valid. We propose Temporal Validity Change
Prediction, a natural language processing task benchmarking the capability of
machine learning models to detect contextual statements that induce such
change. We create a dataset consisting of temporal target statements sourced
from Twitter and crowdsource sample context statements. We then benchmark a set
of transformer-based language models on our dataset. Finally, we experiment
with temporal validity duration prediction as an auxiliary task to improve the
performance of the state-of-the-art model.
