# Direct optimization of F-measure for retrieval-based personal question answering

Source: [Direct optimization of F-measure for retrieval-based personal question answering](<https://www.sota2.com/research/paper/68fa64e31e209131cea0728b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:13:58.604Z

Authors: Rasool Fakoor, Amanjit Kainth, Siamak Shakeri, Christopher Winestock, Abdel-rahman Mohamed, Ruhi Sarikaya

Published: 2018-09-28T00:51:24.000Z

## 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).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1810.00679>)

[PDF](<https://arxiv.org/pdf/1810.00679.pdf>)