RSL19BD at DBDC4: Ensemble of Decision Tree-Based and LSTM-Based Models

Chih Hao Wang, Sosuke Kato, Tetsuya Sakai

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

RSL19BD (Waseda University Sakai Laboratory) participated in the Fourth Dialogue Breakdown Detection Challenge (DBDC4) and submitted five runs to both English and Japanese subtasks. In these runs, we utilise the Decision Tree-based model and the Long Short-Term Memory-based (LSTM-based) model following the approaches of RSL17BD and KTH in the Third Dialogue Breakdown Detection Challenge (DBDC3) respectively. The Decision Tree-based model follows the approach of RSL17BD but utilises RandomForestRegressor instead of ExtraTreesRegressor. In addition, instead of predicting the mean and the variance of the probability distribution of the three breakdown labels, it predicts the probability of each label directly. The LSTM-based model follows the approach of KTH with some changes in the architecture and utilises Convolutional Neural Network (CNN) to perform text feature extraction. In addition, instead of targeting the single breakdown label and minimising the categorical cross entropy loss, it targets the probability distribution of the three breakdown labels and minimises the mean squared error. Run 1 utilises a Decision Tree-based model; Run 2 utilises an LSTM-based model; Run 3 performs an ensemble of 5 LSTM-based models; Run 4 performs an ensemble of Run 1 and Run 2; Run 5 performs an ensemble of Run 1 and Run 3. Run 5 statistically significantly outperformed all other runs in terms of MSE (NB, PB, B) for the English data and all other runs except Run 4 in terms of MSE (NB, PB, B) for the Japanese data (alpha level = 0.05).

Original languageEnglish
Title of host publicationLecture Notes in Electrical Engineering
PublisherSpringer Science and Business Media Deutschland GmbH
Pages429-441
Number of pages13
DOIs
Publication statusPublished - 2021

Publication series

NameLecture Notes in Electrical Engineering
Volume714
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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