Temporal Validity Change Prediction
This addresses the problem of enhancing temporal reasoning in text for applications like recommender systems and conversational AI, but it is incremental as it builds on existing benchmarking tasks.
The paper introduces Temporal Validity Change Prediction, a new NLP task to detect contextual statements that alter the temporal validity duration of a target statement, and benchmarks transformer-based models on a crowdsourced Twitter dataset, showing that using temporal validity duration prediction as an auxiliary task improves the state-of-the-art model's performance.
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.