@inproceedings{anikina-etal-2022-anaphora,
title = "Anaphora Resolution in Dialogue: System Description ({CODI}-{CRAC} 2022 Shared Task)",
author = "Anikina, Tatiana and
Skachkova, Natalia and
Renner, Joseph and
Trivedi, Priyansh",
booktitle = "Proceedings of the CODI-CRAC 2022 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.codi-crac.2",
pages = "15--27",
abstract = "We describe three models submitted for the CODI-CRAC 2022 shared task. To perform identity anaphora resolution, we test several combinations of the incremental clustering approach based on the Workspace Coreference System (WCS) with other coreference models. The best result is achieved by adding the {``}cluster merging{''} version of the coref-hoi model, which brings up to 10.33{\%} improvement1 over vanilla WCS clustering. Discourse deixis resolution is implemented as multi-task learning: we combine the learning objective of coref-hoi with anaphor type classification. We adapt the higher-order resolution model introduced in Joshi et al. (2019) for bridging resolution given gold mentions and anaphors.",
}
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%0 Conference Proceedings
%T Anaphora Resolution in Dialogue: System Description (CODI-CRAC 2022 Shared Task)
%A Anikina, Tatiana
%A Skachkova, Natalia
%A Renner, Joseph
%A Trivedi, Priyansh
%S Proceedings of the CODI-CRAC 2022 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F anikina-etal-2022-anaphora
%X We describe three models submitted for the CODI-CRAC 2022 shared task. To perform identity anaphora resolution, we test several combinations of the incremental clustering approach based on the Workspace Coreference System (WCS) with other coreference models. The best result is achieved by adding the “cluster merging” version of the coref-hoi model, which brings up to 10.33% improvement1 over vanilla WCS clustering. Discourse deixis resolution is implemented as multi-task learning: we combine the learning objective of coref-hoi with anaphor type classification. We adapt the higher-order resolution model introduced in Joshi et al. (2019) for bridging resolution given gold mentions and anaphors.
%U https://aclanthology.org/2022.codi-crac.2
%P 15-27
Markdown (Informal)
[Anaphora Resolution in Dialogue: System Description (CODI-CRAC 2022 Shared Task)](https://aclanthology.org/2022.codi-crac.2) (Anikina et al., CODI 2022)
ACL