This paper presents multiple question generation strategies for document-level event argument extraction. These strategies do not require human involvement and result in uncontextualized questions as well as contextualized questions grounded on the event and document of interest. Experimental results show that combining uncontextualized and contextualized questions is beneficial,especially when event triggers and arguments appear in different sentences. Our approach does not have corpus-specific components, in particular, the question generation strategies transfer across corpora. We also present a qualitative analysis of the most common errors made by our best model.

We describe the N2 (Narrative Networks) Corpus, a new language resource. The corpus is unique in three important ways. First, every text in the corpus is a story, which is in contrast to other language resources that may contain stories or story-like texts, but are not specifically curated to contain only stories. Second, the unifying theme of the corpus is material relevant to Islamist Extremists, having been produced by or often referenced by them. Third, every text in the corpus has been annotated for 14 layers of syntax and semantics, including: referring expressions and co-reference; events, time expressions, and temporal relationships; semantic roles; and word senses. In cases where analyzers were not available to do high-quality automatic annotations, layers were manually double-annotated and adjudicated by trained annotators. The corpus comprises 100 texts and 42,480 words. Most of the texts were originally in Arabic but all are provided in English translation. We explain the motivation for constructing the corpus, the process for selecting the texts, the detailed contents of the corpus itself, the rationale behind the choice of annotation layers, and the annotation procedure.