Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash 100 tok/s
Gemini 2.5 Pro 58 tok/s Pro
GPT-5 Medium 29 tok/s
GPT-5 High 29 tok/s Pro
GPT-4o 103 tok/s
GPT OSS 120B 480 tok/s Pro
Kimi K2 215 tok/s Pro
2000 character limit reached

Effective Incorporation of Speaker Information in Utterance Encoding in Dialog (1907.05599v1)

Published 12 Jul 2019 in eess.AS, cs.CL, cs.LG, and cs.SD

Abstract: In dialog studies, we often encode a dialog using a hierarchical encoder where each utterance is converted into an utterance vector, and then a sequence of utterance vectors is converted into a dialog vector. Since knowing who produced which utterance is essential to understanding a dialog, conventional methods tried integrating speaker labels into utterance vectors. We found the method problematic in some cases where speaker annotations are inconsistent among different dialogs. A relative speaker modeling method is proposed to address the problem. Experimental evaluations on dialog act recognition and response generation show that the proposed method yields superior and more consistent performances.

Citations (8)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Ai Generate Text Spark Streamline Icon: https://streamlinehq.com

Paper Prompts

Sign up for free to create and run prompts on this paper using GPT-5.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.