LIDA: Lightweight Interactive Dialogue Annotator
This tool addresses the need for efficient and high-quality annotation in dialogue systems, which is crucial for developers and researchers working on conversational AI, though it is incremental as it builds on existing annotation practices.
The authors tackled the problem of improving dialogue data annotation by introducing LIDA, a tool that handles the entire annotation pipeline from raw text to structured data, resulting in an open-source system that supports machine learning model integration and inter-annotator disagreement resolution.
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 models as annotation recommenders and also has a dedicated interface to resolve inter-annotator disagreements such as after crowdsourcing annotations for a dataset. LIDA is fully open source, documented and publicly available [ https://github.com/Wluper/lida ]