Presentation # | 1 |
Session: | Dialogue |
Session Time: | Thursday, December 20, 10:00 - 12:00 |
Presentation Time: | Thursday, December 20, 10:00 - 12:00 |
Presentation: |
Poster
|
Topic: |
Spoken document retrieval: |
Paper Title: |
DIRECT OPTIMIZATION OF F-MEASURE FOR RETRIEVAL-BASED PERSONAL QUESTION ANSWERING |
Authors: |
Rasool Fakoor; Amazon | | |
| Amanjit Kainth; Amazon | | |
| Siamak Shakeri; Amazon | | |
| Christopher Winestock; Amazon | | |
| Abdel-rahman Mohamed; Amazon | | |
| Ruhi Sarikaya; Amazon | | |
Abstract: |
Recent advances in spoken language technologies and the introduction of many customer facing products, have given rise to a wide customer reliance on smart personal assistants for many of their daily tasks. In this paper, we present a system to reduce users' cognitive load by extending personal assistants with long-term personal memory where users can store and retrieve by voice, arbitrary pieces of information. The problem is framed as a neural retrieval based question answering system where answers are selected from previously stored user memories. We propose to directly optimize the end-to-end retrieval performance, measured by the F1-score, using reinforcement learning, leading to better performance on our experimental test set(s). |