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        <title>How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT</title>
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    <abstract>This article evaluates purported progress over the past years in RST discourse parsing. Several studies report a relative error reduction of 24 to 51% on all metrics that authors attribute to the introduction of distributed representations of discourse units. We replicate the standard evaluation of 9 parsers, 5 of which use distributed representations, from 8 studies published between 2013 and 2017, using their predictions on the test set of the RST-DT. Our main finding is that most recently reported increases in RST discourse parser performance are an artefact of differences in implementations of the evaluation procedure. We evaluate all these parsers with the standard Parseval procedure to provide a more accurate picture of the actual RST discourse parsers performance in standard evaluation settings. Under this more stringent procedure, the gains attributable to distributed representations represent at most a 16% relative error reduction on fully-labelled structures.</abstract>
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%0 Conference Proceedings
%T How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT
%A Morey, Mathieu
%A Muller, Philippe
%A Asher, Nicholas
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F morey-etal-2017-much
%X This article evaluates purported progress over the past years in RST discourse parsing. Several studies report a relative error reduction of 24 to 51% on all metrics that authors attribute to the introduction of distributed representations of discourse units. We replicate the standard evaluation of 9 parsers, 5 of which use distributed representations, from 8 studies published between 2013 and 2017, using their predictions on the test set of the RST-DT. Our main finding is that most recently reported increases in RST discourse parser performance are an artefact of differences in implementations of the evaluation procedure. We evaluate all these parsers with the standard Parseval procedure to provide a more accurate picture of the actual RST discourse parsers performance in standard evaluation settings. Under this more stringent procedure, the gains attributable to distributed representations represent at most a 16% relative error reduction on fully-labelled structures.
%R 10.18653/v1/D17-1136
%U https://aclanthology.org/D17-1136
%U https://doi.org/10.18653/v1/D17-1136
%P 1319-1324

Markdown (Informal)

[How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT](https://aclanthology.org/D17-1136) (Morey et al., EMNLP 2017)

• How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT (Morey et al., EMNLP 2017)

ACL

• Mathieu Morey, Philippe Muller, and Nicholas Asher. 2017. How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1319–1324, Copenhagen, Denmark. Association for Computational Linguistics.