May 29, 2019
A good conversation requires balance – between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this work, we examine two controllable neural text generation methods, conditional training and weighted decoding, in order to control four important attributes for chitchat dialogue: repetition, specificity, response-relatedness and question-asking. We conduct a large-scale human evaluation to measure the effect of these control parameters on multi-turn interactive conversations on the PersonaChat task. We provide a detailed analysis of their relationship to high-level aspects of conversation, and show that by controlling combinations of these variables our models obtain clear improvements in human quality judgments.
Publisher
NAACL
April 23, 2024
Sachit Menon, Ishan Misra, Rohit Girdhar
April 23, 2024
April 05, 2024
Suyu Ge, Chunting Zhou, Rui Hou, Madian Khabsa, Yi-Chia Wang, Qifan Wang, Jiawei Han, Yuning Mao
April 05, 2024
December 07, 2023
Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Davide Testuggine, Madian Khabsa
December 07, 2023
November 06, 2023
David Esiobu, Ellen Tan, Saghar Hosseini, Megan Ung, Yuchen Zhang, Jude Fernandes, Jane Yu, Eleonora Presani, Adina Williams, Eric Smith
November 06, 2023
Product experiences
Foundational models
Product experiences
Latest news
Foundational models