| Presentation # | 1 |
| Session: | Dialogue |
| Location: | Kallirhoe Hall |
| 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, Amanjit Kainth, Siamak Shakeri, Christopher Winestock, Abdel-rahman Mohamed, Ruhi Sarikaya, Amazon, United States |
| 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). |