@inproceedings{tigunova-etal-2021-pride,
title = "{PRIDE}: {P}redicting {R}elationships in {C}onversations",
author = "Tigunova, Anna and
Mirza, Paramita and
Yates, Andrew and
Weikum, Gerhard",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.380",
doi = "10.18653/v1/2021.emnlp-main.380",
pages = "4636--4650",
abstract = "Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots. To infer speakers{'} relationships from dialogues we propose PRIDE, a neural multi-label classifier, based on BERT and Transformer for creating a conversation representation. PRIDE utilizes dialogue structure and augments it with external knowledge about speaker features and conversation style. Unlike prior works, we address multi-label prediction of fine-grained relationships. We release large-scale datasets, based on screenplays of movies and TV shows, with directed relationships of conversation participants. Extensive experiments on both datasets show superior performance of PRIDE compared to the state-of-the-art baselines.",
}
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<abstract>Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots. To infer speakers’ relationships from dialogues we propose PRIDE, a neural multi-label classifier, based on BERT and Transformer for creating a conversation representation. PRIDE utilizes dialogue structure and augments it with external knowledge about speaker features and conversation style. Unlike prior works, we address multi-label prediction of fine-grained relationships. We release large-scale datasets, based on screenplays of movies and TV shows, with directed relationships of conversation participants. Extensive experiments on both datasets show superior performance of PRIDE compared to the state-of-the-art baselines.</abstract>
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%0 Conference Proceedings
%T PRIDE: Predicting Relationships in Conversations
%A Tigunova, Anna
%A Mirza, Paramita
%A Yates, Andrew
%A Weikum, Gerhard
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F tigunova-etal-2021-pride
%X Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots. To infer speakers’ relationships from dialogues we propose PRIDE, a neural multi-label classifier, based on BERT and Transformer for creating a conversation representation. PRIDE utilizes dialogue structure and augments it with external knowledge about speaker features and conversation style. Unlike prior works, we address multi-label prediction of fine-grained relationships. We release large-scale datasets, based on screenplays of movies and TV shows, with directed relationships of conversation participants. Extensive experiments on both datasets show superior performance of PRIDE compared to the state-of-the-art baselines.
%R 10.18653/v1/2021.emnlp-main.380
%U https://aclanthology.org/2021.emnlp-main.380
%U https://doi.org/10.18653/v1/2021.emnlp-main.380
%P 4636-4650
Markdown (Informal)
[PRIDE: Predicting Relationships in Conversations](https://aclanthology.org/2021.emnlp-main.380) (Tigunova et al., EMNLP 2021)
ACL
- Anna Tigunova, Paramita Mirza, Andrew Yates, and Gerhard Weikum. 2021. PRIDE: Predicting Relationships in Conversations. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4636–4650, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.