# Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering

Source: [Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering](<https://www.sota2.com/research/paper/68fa63a61e209131cea0016e>)

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

Data updated: 2025-10-25T20:46:17.527Z

Authors: Wei Li, Yunfang Wu

Published: 2017-09-17T02:56:40.000Z

## Abstract

In this paper, we focus on the problem of answer triggering ad-dressed by Yang et al. (2015), which is a critical component for a real-world question answering system. We employ a hierarchical gated recurrent neural tensor (HGRNT) model to capture both the context information and the deep in-teractions between the candidate answers and the question. Our result on F val-ue achieves 42.6%, which surpasses the baseline by over 10 %.

## 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/1709.05599>)

[PDF](<https://arxiv.org/pdf/1709.05599.pdf>)