| Internet-Draft | Improving Data Quality through Special T | July 2023 |
| Ovcharenko | Expires 26 January 2024 | [Page] |
This document proposes the use of special text tags to enhance data quality and improve the understanding of user queries in conversational AI models. By incorporating these tags, models can benefit from additional context and structure during training and inference, leading to more accurate and relevant responses.¶
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Conversational AI models often face challenges in data collection and text parsing, impacting their performance and reliability. This proposal aims to address these challenges by introducing special text tags. This approach draws inspiration from related works in natural language processing, information retrieval, and conversational AI.¶
The motivation behind this proposal is to improve the quality of training data and enhance the understanding of user queries by incorporating special text tags. The idea is influenced by research on intent recognition, entity extraction, and context modeling in natural language understanding. Notable works include:¶
Intent tags are used to label the intent or purpose of user queries, providing guidance to the model in generating more contextually appropriate responses.¶
Entity tags are used to identify and label specific entities within the text, improving the model's understanding of user queries related to those entities.¶
Emotion or tone markers indicate the emotional or tonal aspects of the text, enabling the model to generate more appropriate and empathetic responses.¶
This memo includes no request to IANA.¶
The security considerations section highlights that implementing special text tags does not introduce inherent security risks. However, it emphasizes the need to ensure secure and privacy-conscious practices during the tagging process and data collection, adhering to existing guidelines[usage-policies].¶
Interoperability is crucial for the widespread adoption of special text tags. This section recognizes the importance of standardization efforts to ensure consistent usage and interpretation of tags across different conversational AI models and platforms. It encourages collaboration with standardization bodies and references existing efforts in the field[caml-dialogue-systems].¶
The implementation and deployment section discuss the practical aspects of integrating special text tags. It suggests involving human annotators or domain experts to accurately tag training data, modifying training processes to consider the tags, and updating inference systems to interpret and respond to tagged user queries effectively.¶
The proposed special text tags offer a structured approach to enrich the training data of conversational AI models. By incorporating these tags, models can improve data quality, enhance understanding of user queries, and generate more accurate and contextually relevant responses. The conclusion section summarizes the potential benefits and encourages further research and experimentation.¶