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Temporal relation extraction models have thus far been hindered by a numberof issues in existing temporal relation-annotated news datasets, including: (1)low inter-annotator agreement due to the lack of specificity of theirannotation guidelines in terms of what counts as a temporal relation; (2) theexclusion of long-distance relations within a given document (those spanningacross different paragraphs); and (3) the exclusion of events that are notcentred on verbs. This paper aims to alleviate these issues by presenting a newannotation scheme that clearly defines the criteria based on which temporalrelations should be annotated. Additionally, the scheme includes events even ifthey are not expressed as verbs (e.g., nominalised events). Furthermore, wepropose a method for annotating all temporal relations -- includinglong-distance ones -- which automates the process, hence reducing time andmanual effort on the part of annotators. The result is a new dataset, theTIMELINE corpus, in which improved inter-annotator agreement was obtained, incomparison with previously reported temporal relation datasets. We report theresults of training and evaluating baseline temporal relation extraction modelson the new corpus, and compare them with results obtained on the widely usedMATRES corpus.

TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articles