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        <title>TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events</title>
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    <abstract>Prior work on temporal relation classification has focused extensively on event pairs in the same or adjacent sentences (local), paying scant attention to discourse-level (global) pairs. This restricts the ability of systems to learn temporal links between global pairs, since reliance on local syntactic features suffices to achieve reasonable performance on existing datasets. However, systems should be capable of incorporating cues from document-level structure to assign temporal relations. In this work, we take a first step towards discourse-level temporal ordering by creating TDDiscourse, the first dataset focusing specifically on temporal links between event pairs which are more than one sentence apart. We create TDDiscourse by augmenting TimeBank-Dense, a corpus of English news articles, manually annotating global pairs that cannot be inferred automatically from existing annotations. Our annotations double the number of temporal links in TimeBank-Dense, while possessing several desirable properties such as focusing on long-distance pairs and not being automatically inferable. We adapt and benchmark the performance of three state-of-the-art models on TDDiscourse and observe that existing systems indeed find discourse-level temporal ordering harder.</abstract>
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    <identifier type="doi">10.18653/v1/W19-5929</identifier>
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%0 Conference Proceedings
%T TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events
%A Naik, Aakanksha
%A Breitfeller, Luke
%A Rose, Carolyn
%Y Nakamura, Satoshi
%Y Gasic, Milica
%Y Zukerman, Ingrid
%Y Skantze, Gabriel
%Y Nakano, Mikio
%Y Papangelis, Alexandros
%Y Ultes, Stefan
%Y Yoshino, Koichiro
%S Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
%D 2019
%8 September
%I Association for Computational Linguistics
%C Stockholm, Sweden
%F naik-etal-2019-tddiscourse
%X Prior work on temporal relation classification has focused extensively on event pairs in the same or adjacent sentences (local), paying scant attention to discourse-level (global) pairs. This restricts the ability of systems to learn temporal links between global pairs, since reliance on local syntactic features suffices to achieve reasonable performance on existing datasets. However, systems should be capable of incorporating cues from document-level structure to assign temporal relations. In this work, we take a first step towards discourse-level temporal ordering by creating TDDiscourse, the first dataset focusing specifically on temporal links between event pairs which are more than one sentence apart. We create TDDiscourse by augmenting TimeBank-Dense, a corpus of English news articles, manually annotating global pairs that cannot be inferred automatically from existing annotations. Our annotations double the number of temporal links in TimeBank-Dense, while possessing several desirable properties such as focusing on long-distance pairs and not being automatically inferable. We adapt and benchmark the performance of three state-of-the-art models on TDDiscourse and observe that existing systems indeed find discourse-level temporal ordering harder.
%R 10.18653/v1/W19-5929
%U https://aclanthology.org/W19-5929
%U https://doi.org/10.18653/v1/W19-5929
%P 239-249

Markdown (Informal)

[TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events](https://aclanthology.org/W19-5929) (Naik et al., SIGDIAL 2019)

• TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events (Naik et al., SIGDIAL 2019)

ACL

• Aakanksha Naik, Luke Breitfeller, and Carolyn Rose. 2019. TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 239–249, Stockholm, Sweden. Association for Computational Linguistics.