# Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding

## TL;DR

This paper introduces a novel time-aware attention model that automatically learns time decay without manual tuning and incorporates speaker identification to enhance spoken language understanding accuracy.

## Contribution

It presents a decay-function-free, time-aware attention mechanism and a speaker labeling method, improving SLU performance over existing models.

## Key findings

- Achieved higher F1 scores than state-of-the-art models on DSTC4 dataset.
- Demonstrated effectiveness of time-aware attention in SLU.
- Showed that speaker identification further improves SLU accuracy.

## Abstract

To capture salient contextual information for spoken language understanding (SLU) of a dialogue, we propose time-aware models that automatically learn the latent time-decay function of the history without a manual time-decay function. We also propose a method to identify and label the current speaker to improve the SLU accuracy. In experiments on the benchmark dataset used in Dialog State Tracking Challenge 4, the proposed models achieved significantly higher F1 scores than the state-of-the-art contextual models. Finally, we analyze the effectiveness of the introduced models in detail. The analysis demonstrates that the proposed methods were effective to improve SLU accuracy individually.