> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/generative-ai/conversation.md). # Conversation Once a model takes part in conversations, you need a way to measure what happens in them, from politeness to structure to how people's language changes. This page points at Cornell's ConvoKit and then walks its feature extractors in order: politeness strategies, expected conversational context, hypergraph structure, and linguistic diversity. Each one pairs the research page with its API documentation. [ConvoKit](https://github.com/CornellNLP/ConvoKit) by Cornell — is a toolkit for extracting conversational features and analyzing social phenomena in conversations. It includes several large conversational datasets along with scripts exemplifying the use of the toolkit on these datasets. The first feature is politeness. [Politeness strategies](https://www.cs.cornell.edu/~cristian/Politeness.html) is Cristian Danescu-Niculescu-Mizil's research on a computational approach to politeness with application to social factors, and the toolkit's version is documented in the politenessStrategies [(API)](https://convokit.cornell.edu/documentation/politenessStrategies.html). It gives a set of lexical and parse-based features correlating with politeness and impoliteness. Politeness looks at single utterances; the next framework looks at the context an utterance is expected to appear in. [Expected Conversational Context Framework](https://tisjune.github.io/research/dissertation) is the dissertation page behind it, and the Expected Context Framework [(API)](https://convokit.cornell.edu/documentation/expected_context_model.html) is the documentation. It is a framework for characterizing utterances and terms based on their expected conversational context, consisting of model implementations and wrapper pipelines. From utterances the toolkit moves to the shape of a whole discussion. [Hypergraph conversation representation](http://www.cs.cornell.edu/~cristian/Patterns_of_participant_interactions.html) is Cristian Danescu-Niculescu-Mizil's research on characterizing online public discussions through patterns of participant interactions, and Hyperconvo is the [(API)](https://convokit.cornell.edu/documentation/hyperconvo.html). It is a method for extracting structural features of conversations through a hypergraph representation. The last extractor follows speakers rather than conversations. [Linguistic diversity in conversations](http://www.cs.cornell.edu/~cristian/Finding_your_voice__linguistic_development.html) is Cristian Danescu-Niculescu-Mizil's research "Finding Your Voice: The Linguistic Development of Mental Health Counselors", and Speaker Convo Diversity is the [(API)](https://convokit.cornell.edu/documentation/speakerConvoDiversity.html). It is a method to compute the linguistic diversity of individuals within their own conversations, and between other individuals in a population. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/generative-ai/conversation.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.