Internet DRAFT - draft-improving-data-quality-tags
draft-improving-data-quality-tags
Internet Engineering Task Force A. Ovcharenko
Internet-Draft 25 July 2023
Intended status: Informational
Expires: 26 January 2024
Improving Data Quality through Special Text Tags
draft-improving-data-quality-tags-00
Abstract
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.
Status of This Memo
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This Internet-Draft will expire on 26 January 2024.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . 2
3. Specification . . . . . . . . . . . . . . . . . . . . . . . . 3
3.1. Intent Tagging . . . . . . . . . . . . . . . . . . . . . 3
3.2. Entity Tagging . . . . . . . . . . . . . . . . . . . . . 3
3.3. Contextual Tags . . . . . . . . . . . . . . . . . . . . . 3
3.4. Quality Assessment Tags . . . . . . . . . . . . . . . . . 4
3.5. Emotion or Tone Markers . . . . . . . . . . . . . . . . . 4
4. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 4
5. Security Considerations . . . . . . . . . . . . . . . . . . . 5
6. Interoperability . . . . . . . . . . . . . . . . . . . . . . 5
7. Implementation and Deployment . . . . . . . . . . . . . . . . 5
8. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 5
9. Informative References . . . . . . . . . . . . . . . . . . . 5
Author's Address . . . . . . . . . . . . . . . . . . . . . . . . 6
1. Introduction
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.
2. Motivation
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:
* Previous studies on intent recognition in dialogue systems have
explored the use of intent tags to improve the accuracy of
responses[intent-recognition].
* Named Entity Recognition (NER) techniques have been widely studied
and applied in information extraction tasks. These approaches
inspire the entity tagging component proposed in this
study[gibbs-sampling].
* Research on dialogue modeling has emphasized the importance of
context and sequential information in generating coherent
responses. Contextual tags introduced in this proposal draw
inspiration from these studies[contextual-understanding].
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3. Specification
3.1. Intent Tagging
Intent tags are used to label the intent or purpose of user queries,
providing guidance to the model in generating more contextually
appropriate responses.
* [intent-def]: For queries seeking definitions of terms.
* [intent-comp]: For queries comparing two or more entities.
* [intent-ex]: For queries requesting examples or instances.
* [intent-steps]: For queries seeking step-by-step instructions.
* [intent-adv-disadv]: For queries exploring the pros and cons of a
topic.
3.2. Entity Tagging
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.
* [entity-person]: For queries related to people or individuals.
* [entity-organization]: For queries related to organizations or
companies.
* [entity-location]: For queries related to specific locations.
* [entity-date]: For queries related to dates or time.
* [entity-product]: For queries related to products or items.
3.3. Contextual Tags
Contextual tags mark contextual information, providing cues for
maintaining a coherent and context-aware conversation.
* [context-background]: For providing background information or
context.
* [context-constraints]: For indicating limitations or constraints.
* [context-previous-query]: For referring to a previous user query
or conversation context.
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* [context-next-steps]: For suggesting the next steps in a process
or task.
* [context-clarification]: For seeking clarification or additional
details.
3.4. Quality Assessment Tags
Quality assessment tags help identify the quality or reliability of
information, enabling the model to generate more cautious and
reliable responses.
* [qa-biased]: Indicating biased information.
* [qa-unverified]: Denoting information that is not verified or
lacks credibility.
* [qa-misleading]: Highlighting information that may be misleading
or deceptive.
* [qa-outdated]: Identifying information that is outdated or no
longer accurate.
* [qa-fact-check-needed]: Flagging information that requires fact-
checking.
3.5. Emotion or Tone Markers
Emotion or tone markers indicate the emotional or tonal aspects of
the text, enabling the model to generate more appropriate and
empathetic responses.
* [tone-positive]: Denoting a positive emotional tone.
* [tone-negative]: Indicating a negative emotional tone.
* [tone-neutral]: Denoting a neutral or unbiased tone.
* [tone-joy]: Indicating a joyful or happy emotion.
* [tone-sadness]: Denoting a sad or sorrowful emotion.
4. IANA Considerations
This memo includes no request to IANA.
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5. Security Considerations
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].
6. Interoperability
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].
7. Implementation and Deployment
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.
8. Conclusion
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.
9. Informative References
[intent-recognition]
Chen, M., Xu, Z., Weinberger, K., and O. Chapelle,
"Marginalized Denoising Autoencoders for Domain
Adaptation", 2012,
<https://www.cs.cornell.edu/~kilian/papers/
msdadomain.pdf>.
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[gibbs-sampling]
Finkel, J. R., Grenager, T., and C. Manning,
"Incorporating Non-local Information into Information
Extraction Systems by Gibbs Sampling", 2005,
<https://www.aclweb.org/anthology/P/P05/P05-1045.pdf>.
[contextual-understanding]
Ritter, A., Cherry, C., and B. Dolan, "Data-driven
Response Generation in Social Media", 2011,
<https://www.aclweb.org/anthology/D/D11/D11-1145.pdf>.
[usage-policies]
OpenAI, "Usage policies", 2021,
<https://openai.com/policies/usage-policies>.
[caml-dialogue-systems]
Kovasznai, G., Kotropoulos, C., and I. Pitas, "CAML - A
Universal Configuration Language for Dialogue Systems",
<https://citeseerx.ist.psu.edu/doc/10.1.1.1086.4050>.
Author's Address
Aleksey Ovcharenko
Email: aleksey.ovcharenko@gmail.com
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