x-ECON: Explainable Event-Consumption Commonsense Reasoning

This study investigates a novel commonsense inference task that comprises reasoning about the subsequent events chains and probable consumption intents, given an event described in brief free-form text. For instance, given the event “
fall in love
”, the system anticipates ensuing events such as “
get married
”→“
be pregnant
”→“
have a baby
”. Concurrently, the system predicts the likely consumption intents of the event participants, such as purchasing “
jewelry
”, “
maternity clothing
”, and “
baby food
”. The event chains generated in this process provide explicit, meaningful explanations, aiding the understanding of inferred consumption intents. To facilitate this study, we construct a new crowdsourced corpus: x-ECON. This corpus comprises 10,144 event chains and 150 event-related product categories, offering a wide range of everyday consumer events and situations. We introduce a baseline reasoning framework that not only infers consumption intent but also generates an event chain to explain its inference. Our experimental results suggest that involving the event chain reasoning in the event-consumption reasoning system can help improve the neural networks' reason about the probable consumption intents of the event participants. Additionally, our method demonstrates the applicability of our approach in improving the performance of recommendation systems, by highlighting the role of explainable commonsense inference on the consumption intent. We also evaluated the performance of ChatGPT on our x-ECON dataset, showing that explainable event-consumption commonsense reasoning is a challenging task for large language models.