LIDA: Lightweight Interactive Dialogue Annotator

Edward Collins, Nikolai Rozanov, Bingbing Zhang


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] –> Screen Cast: https://vimeo.com/329824847
Anthology ID:
D19-3021
Volume:
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:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
121–126
Language:
URL:
https://aclanthology.org/D19-3021
DOI:
10.18653/v1/D19-3021
Bibkey:
Cite (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.
Cite (Informal):
LIDA: Lightweight Interactive Dialogue Annotator (Collins et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-3021.pdf
Code
 Wluper/lida
Data
MultiWOZ