@inproceedings{collins-etal-2019-lida,
title = "{LIDA}: Lightweight Interactive Dialogue Annotator",
author = "Collins, Edward and
Rozanov, Nikolai and
Zhang, Bingbing",
editor = "Pad{\'o}, Sebastian and
Huang, Ruihong",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-3021",
doi = "10.18653/v1/D19-3021",
pages = "121--126",
abstract = "Dialogue systems have the potential to change how people interact with machines but are highly dependent on the quality of the data used to train them. It is therefore important to develop good dialogue annotation tools which can improve the speed and quality of dialogue data annotation. With this in mind, we introduce LIDA, an annotation tool designed specifically for conversation data. As far as we know, LIDA is the first dialogue annotation system that handles the entire dialogue annotation pipeline from raw text, as may be the output of transcription services, to structured conversation data. Furthermore it supports the integration of arbitrary machine learning mod-els as annotation recommenders and also has a dedicated interface to resolve inter-annotator disagreements such as after crowdsourcing an-notations for a dataset. LIDA is fully open source, documented and publicly available.[\url{https://github.com/Wluper/lida}] {--}{\textgreater} Screen Cast: \url{https://vimeo.com/329824847}",
}
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<abstract>Dialogue systems have the potential to change how people interact with machines but are highly dependent on the quality of the data used to train them. It is therefore important to develop good dialogue annotation tools which can improve the speed and quality of dialogue data annotation. With this in mind, we introduce LIDA, an annotation tool designed specifically for conversation data. As far as we know, LIDA is the first dialogue annotation system that handles the entire dialogue annotation pipeline from raw text, as may be the output of transcription services, to structured conversation data. Furthermore it supports the integration of arbitrary machine learning mod-els as annotation recommenders and also has a dedicated interface to resolve inter-annotator disagreements such as after crowdsourcing an-notations for a dataset. LIDA is fully open source, documented and publicly available.[https://github.com/Wluper/lida] –\textgreater Screen Cast: https://vimeo.com/329824847</abstract>
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%0 Conference Proceedings
%T LIDA: Lightweight Interactive Dialogue Annotator
%A Collins, Edward
%A Rozanov, Nikolai
%A Zhang, Bingbing
%Y Padó, Sebastian
%Y Huang, Ruihong
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F collins-etal-2019-lida
%X Dialogue systems have the potential to change how people interact with machines but are highly dependent on the quality of the data used to train them. It is therefore important to develop good dialogue annotation tools which can improve the speed and quality of dialogue data annotation. With this in mind, we introduce LIDA, an annotation tool designed specifically for conversation data. As far as we know, LIDA is the first dialogue annotation system that handles the entire dialogue annotation pipeline from raw text, as may be the output of transcription services, to structured conversation data. Furthermore it supports the integration of arbitrary machine learning mod-els as annotation recommenders and also has a dedicated interface to resolve inter-annotator disagreements such as after crowdsourcing an-notations for a dataset. LIDA is fully open source, documented and publicly available.[https://github.com/Wluper/lida] –\textgreater Screen Cast: https://vimeo.com/329824847
%R 10.18653/v1/D19-3021
%U https://aclanthology.org/D19-3021
%U https://doi.org/10.18653/v1/D19-3021
%P 121-126
Markdown (Informal)
[LIDA: Lightweight Interactive Dialogue Annotator](https://aclanthology.org/D19-3021) (Collins et al., EMNLP-IJCNLP 2019)
ACL
- Edward Collins, Nikolai Rozanov, and Bingbing Zhang. 2019. LIDA: Lightweight Interactive Dialogue Annotator. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 121–126, Hong Kong, China. Association for Computational Linguistics.