Papers by Katherine Stasaski

2 papers
Semantic Diversity in Dialogue with Natural Language Inference (2022.naacl-main)

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Challenge: Existing methods to measure diversity of chitchat model responses have been proposed to measure iteratively.
Approach: They propose a metric which uses Natural Language Inference to measure the semantic diversity of a set of model responses for a conversation.
Outcome: The proposed metric improves the diversity of a sampled set of responses using a new generation procedure called Diversity Threshold Generation.
More Diverse Dialogue Datasets via Diversity-Informed Data Collection (2020.acl-main)

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Challenge: Existing approaches to generate conversational dialogue produce uninteresting, predictable responses.
Approach: They propose a method to collect and determine more diverse data from conversational participants . they use dynamically computed corpus-level statistics to determine which conversational participant to collect data from .
Outcome: The proposed method produces significantly more diverse data than baseline methods and better results on emotion classification and dialogue generation tasks.

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