Look Who's Talking: Inferring Speaker Attributes from Personal Longitudinal Dialog
We examine a large dialog corpus obtained from the conversation history of a single individual with 104 conversation partners. The corpus consists of half a million instant messages, across several messaging platforms. We focus our analyses on seven speaker attributes, each of which partitions the set of speakers, namely: gender; relative age; family member; romantic partner; classmate; co-worker; and native to the same country. In addition to the content of the messages, we examine conversational aspects such as the time messages are sent, messaging frequency, psycholinguistic word categories, linguistic mirroring, and graph-based features reflecting how people in the corpus mention each other. We present two sets of experiments predicting each attribute using (1) short context windows; and (2) a larger set of messages. We find that using all features leads to gains of 9-14% over using message text only.
Code (1)
Tasks
AttributeSimilar Papers 제목 키워드 기반
DFA-NeRF: Personalized Talking Head Generation via Disentangled Face Attributes Neural Rendering
While recent advances in deep neural networks have made it possible to render high-quality images, generating photo-realistic and personalized talking head remains challenging. With given audio, the key to tackling this …
NeRFNeural RenderingTalking Head GenerationListening between the Lines: Learning Personal Attributes from Conversations
Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation. In this work we address the acquisition of such knowledge, for personalization in…
ArticlesAttributeExtracting and Inferring Personal Attributes from Dialogue
Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes. We introduce the tasks of extracting and inferring personal attributes from human-human dialog…
AttributeLanguage ModelingLanguage ModellingExtracting and Inferring Personal Attributes from Dialogue
Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes. We introduce the tasks of extracting and inferring personal attributes from human-human dialog…
AttributeLanguage ModelingLanguage ModellingMIRRORTALK: Forging Personalized Avatars Via Disentangled Style and Hierarchical Motion Control
Synthesizing personalized talking faces that uphold and highlight a speaker's unique style while maintaining lip-sync accuracy remains a significant challenge. A primary limitation of existing approaches is the intrinsic…