@inproceedings{vanderlyn-etal-2022-toward,
title = "Toward Implicit Reference in Dialog: A Survey of Methods and Data",
author = "Vanderlyn, Lindsey and
Anthonio, Talita and
Ortega, Daniel and
Roth, Michael and
Vu, Ngoc Thang",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.45",
doi = "10.18653/v1/2022.aacl-main.45",
pages = "587--600",
abstract = "Communicating efficiently in natural language requires that we often leave information implicit, especially in spontaneous speech. This frequently results in phenomena of incompleteness, such as omitted references, that pose challenges for language processing. In this survey paper, we review the state of the art in research regarding the automatic processing of such \textit{implicit references} in dialog scenarios, discuss weaknesses with respect to inconsistencies in task definitions and terminologies, and outline directions for future work. Among others, these include a unification of existing tasks, addressing data scarcity, and taking into account model and annotator uncertainties.",
}
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<abstract>Communicating efficiently in natural language requires that we often leave information implicit, especially in spontaneous speech. This frequently results in phenomena of incompleteness, such as omitted references, that pose challenges for language processing. In this survey paper, we review the state of the art in research regarding the automatic processing of such implicit references in dialog scenarios, discuss weaknesses with respect to inconsistencies in task definitions and terminologies, and outline directions for future work. Among others, these include a unification of existing tasks, addressing data scarcity, and taking into account model and annotator uncertainties.</abstract>
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%0 Conference Proceedings
%T Toward Implicit Reference in Dialog: A Survey of Methods and Data
%A Vanderlyn, Lindsey
%A Anthonio, Talita
%A Ortega, Daniel
%A Roth, Michael
%A Vu, Ngoc Thang
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F vanderlyn-etal-2022-toward
%X Communicating efficiently in natural language requires that we often leave information implicit, especially in spontaneous speech. This frequently results in phenomena of incompleteness, such as omitted references, that pose challenges for language processing. In this survey paper, we review the state of the art in research regarding the automatic processing of such implicit references in dialog scenarios, discuss weaknesses with respect to inconsistencies in task definitions and terminologies, and outline directions for future work. Among others, these include a unification of existing tasks, addressing data scarcity, and taking into account model and annotator uncertainties.
%R 10.18653/v1/2022.aacl-main.45
%U https://aclanthology.org/2022.aacl-main.45
%U https://doi.org/10.18653/v1/2022.aacl-main.45
%P 587-600
Markdown (Informal)
[Toward Implicit Reference in Dialog: A Survey of Methods and Data](https://aclanthology.org/2022.aacl-main.45) (Vanderlyn et al., AACL-IJCNLP 2022)
ACL
- Lindsey Vanderlyn, Talita Anthonio, Daniel Ortega, Michael Roth, and Ngoc Thang Vu. 2022. Toward Implicit Reference in Dialog: A Survey of Methods and Data. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 587–600, Online only. Association for Computational Linguistics.