Identifying temporal and subevent relationships between different events (ie, event relation
extraction) is an important step towards event-centric natural language processing, which …

Narratives are present in many forms of human expression and can be understood as a
fundamental way of communication between people. Computational understanding of the …

Recent machine reading comprehension datasets such as ReClor and LogiQA require
performing logical reasoning over text. Conventional neural models are insufficient for …

Prior work infers the causation between events mainly based on the knowledge induced
from the annotated causal event pairs. However, additional evidence information …

Remarkable success has been achieved in the last few years on some limited machine
reading comprehension (MRC) tasks. However, it is still difficult to interpret the predictions of …

Event forecasting is a challenging, yet important task, as humans seek to constantly plan for
the future. Existing automated forecasting studies rely mostly on structured data, such as …

Extracting temporal relations (eg, before, after, and simultaneous) among events is crucial to
natural language understanding. One of the key challenges of this problem is that when the …

This paper focuses on the topic of inferential machine comprehension, which aims to fully
understand the meanings of given text to answer generic questions, especially the ones …

Event temporal relation extraction is an important task for information extraction. The existing
methods usually rely on feature engineering and require post-process to achieve …

Time is deeply woven into how people perceive, and communicate about the world. Almost
unconsciously, we provide our language utterances with temporal cues, like verb tenses …