@inproceedings{takmaz-etal-2020-refer,
title = "{R}efer, {R}euse, {R}educe: {G}enerating {S}ubsequent {R}eferences in {V}isual and {C}onversational {C}ontexts",
author = "Takmaz, Ece and
Giulianelli, Mario and
Pezzelle, Sandro and
Sinclair, Arabella and
Fern{\'a}ndez, Raquel",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.353",
doi = "10.18653/v1/2020.emnlp-main.353",
pages = "4350--4368",
abstract = "Dialogue participants often refer to entities or situations repeatedly within a conversation, which contributes to its cohesiveness. Subsequent references exploit the common ground accumulated by the interlocutors and hence have several interesting properties, namely, they tend to be shorter and reuse expressions that were effective in previous mentions. In this paper, we tackle the generation of first and subsequent references in visually grounded dialogue. We propose a generation model that produces referring utterances grounded in both the visual and the conversational context. To assess the referring effectiveness of its output, we also implement a reference resolution system. Our experiments and analyses show that the model produces better, more effective referring utterances than a model not grounded in the dialogue context, and generates subsequent references that exhibit linguistic patterns akin to humans.",
}
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<abstract>Dialogue participants often refer to entities or situations repeatedly within a conversation, which contributes to its cohesiveness. Subsequent references exploit the common ground accumulated by the interlocutors and hence have several interesting properties, namely, they tend to be shorter and reuse expressions that were effective in previous mentions. In this paper, we tackle the generation of first and subsequent references in visually grounded dialogue. We propose a generation model that produces referring utterances grounded in both the visual and the conversational context. To assess the referring effectiveness of its output, we also implement a reference resolution system. Our experiments and analyses show that the model produces better, more effective referring utterances than a model not grounded in the dialogue context, and generates subsequent references that exhibit linguistic patterns akin to humans.</abstract>
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%0 Conference Proceedings
%T Refer, Reuse, Reduce: Generating Subsequent References in Visual and Conversational Contexts
%A Takmaz, Ece
%A Giulianelli, Mario
%A Pezzelle, Sandro
%A Sinclair, Arabella
%A Fernández, Raquel
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F takmaz-etal-2020-refer
%X Dialogue participants often refer to entities or situations repeatedly within a conversation, which contributes to its cohesiveness. Subsequent references exploit the common ground accumulated by the interlocutors and hence have several interesting properties, namely, they tend to be shorter and reuse expressions that were effective in previous mentions. In this paper, we tackle the generation of first and subsequent references in visually grounded dialogue. We propose a generation model that produces referring utterances grounded in both the visual and the conversational context. To assess the referring effectiveness of its output, we also implement a reference resolution system. Our experiments and analyses show that the model produces better, more effective referring utterances than a model not grounded in the dialogue context, and generates subsequent references that exhibit linguistic patterns akin to humans.
%R 10.18653/v1/2020.emnlp-main.353
%U https://aclanthology.org/2020.emnlp-main.353
%U https://doi.org/10.18653/v1/2020.emnlp-main.353
%P 4350-4368
Markdown (Informal)
[Refer, Reuse, Reduce: Generating Subsequent References in Visual and Conversational Contexts](https://aclanthology.org/2020.emnlp-main.353) (Takmaz et al., EMNLP 2020)
ACL