In this paper, we study the identity of textual events from different documents. While the complex nature of event identity is previously studied , the case of events across documents is unclear. Prior work on cross-document event coreference has two main drawbacks. First, they restrict the annotations to a limited set of event types. Second, they insufficiently tackle the concept of event identity. Such annotation setup reduces the pool of event mentions and prevents one from considering the possibility of quasiidentity relations. We propose a dense annotation approach for cross-document event coreference, comprising a rich source of event mentions and a dense annotation effort between related document pairs. To this end, we design a new annotation workflow with careful quality control and an easy-to-use annotation interface. In addition to the links, we further collect overlapping event contexts, including time, location, and participants, to shed some light on the relation between identity decisions and context. We present an open-access dataset for cross-document event coreference, CDEC-WN, collected from English Wikinews and open-source our annotation toolkit to encourage further research on cross-document tasks. 1

This paper proposes a novel question generation (QG) approach based on textual entailment. Many previous QG studies transform a single sentence into a question directly. They need hand-crafted templates or generate simple questions similar to the source texts. As a novel approach to QG, this research employs twostep QG: 1) generating new texts entailed by source documents, and 2) transforming the entailed sentences into questions. This process can generate questions that need the understanding of textual entailment to solve. Our system collected 1,367 English Wikipedia sentences as QG source, retrieved 647 entailed sentences from the web, and transformed them into questions. The evaluation result showed that our system successfully generated nontrivial questions based on textual entailment with 53% accuracy.

In this paper, we study the identity of textual events from different documents. While the complex nature of event identity is previously studied , the case of events across documents is unclear. Prior work on cross-document event coreference has two main drawbacks. First, they restrict the annotations to a limited set of event types. Second, they insufficiently tackle the concept of event identity. Such annotation setup reduces the pool of event mentions and prevents one from considering the possibility of quasiidentity relations. We propose a dense annotation approach for cross-document event coreference, comprising a rich source of event mentions and a dense annotation effort between related document pairs. To this end, we design a new annotation workflow with careful quality control and an easy-to-use annotation interface. In addition to the links, we further collect overlapping event contexts, including time, location, and participants, to shed some light on the relation between identity decisions and context. We present an open-access dataset for cross-document event coreference, CDEC-WN, collected from English Wikinews and open-source our annotation toolkit to encourage further research on cross-document tasks. 1 2 A mention is a linguistic expression in text that denotes a specific instance of an event.