ユーザの特性情報付きチャットボットとの雑談対話コーパスの概要

Doshisha UniversityTokyo University of TechnologyShibaura Institute of TechnologyTokyo University of TechnologyDoshisha University会議名: 言語資源ワークショップ2022, 開催地: オンライン, 会期: 2022年8月30日-31日, 主催: 国立国語研究所 言語資源開発センター本発表では、構築中のチャットボットとの雑談対話コーパスについて紹介する。本コーパスは、参加者がチャットアプリを通してチャットボットと雑談したデータに対して「対話行為」のタグ、チャットボットの発言に対して「対話破綻」のタグを付与したものである。また、各雑談データに参加者の対話に関する印象・満足度、性格特性・社会的スキルの数値を付与している点で特色のあるコーパスとなっている。雑談データの概要を説明したのち、付与された2種類のタグと参加者の性別、性格特性、社会的スキルとの関連について予備的分析を行った結果を報告する

カオ ノ インショウ トクチョウ ノ ゲンゴテキ ヒョウゲン ニ モトズイタ ニガオエ ビョウシャ

筑波大学博士 (工学) 学位論文・平成13年3月23日授与 (甲第2616号)標題紙、目次 -- 第１章　序論 -- 第２章　印象の平均顔を用いた似顔絵描写手法 -- 第３章　印象の平均顔を用いた似顔絵描写手法の評価　 -- 第４章　主観的印象の獲得 -- 第５章　主観的印象を用いた似顔絵描写手法の評価 -- 第６章　結論 -- 謝辞 -- 参考文献 -- 著者文献 -- 付録A　入力言語表

Script Generation using Rhetorical Information in a Task Specification Text ∗

Abstract – In order to extend the application domain of natural language interfaces to more realistic tasks without the decrease of user’s performance, it is desirable for users to be able to specify their requests as coherent texts consisting of more than one sentence. This paper presents a processing model of a natural language interface that accepts such an input text. Rhetorical information in an input text is used to generate a control structure of an output script program. Detection of loop structures, paraphrasing and dialogue are also incorporated. The algorithms explained in this paper have been implemented and evaluated in a personal email management domain

複数ユーザの評価特性を内蔵するIEC実現に向けた実ユーザの評価特性解析

1. はじめに / 2. 他人の評価特性の代替利用 / 3. 擬似ユーザによるシミュレーション / 4. 実タスクに対する実ユーザの評価特性の違い / 5. 考察 / 6. まとめWe propose a method for accelerating interactive evolutionary computation (IEC) convergence by incorporating evaluation characteristics of multi-IEC users, evaluate it with simulation, and compare the simulation results with real JEC users. One of incorporating evaluation characteristics whose prediction of evaluation values are similar to those of a real IEC user is selected and used for EC search with big population size behind the IEC user's evaluation until the evaluation characteristics of the IEC user are leant. The method is evaluated with simulation, and the simulation results are compared with the real IEC user's evaluation characteristics. Through this evaluation, we find that this method is useful for real IEC use

Interactive Evolutionary Computation with Evaluation Characteristics of Multi-IEC Users

We (1) propose a method for accelerating the convergence of interactive evolutionary computation (IEC) by incorporating multiple evaluation models of previous IEC users, (2) evaluate the method's performance according to the similarity metric of users' evaluation characteristics, and (3) investigate its practical usefulness by measuring users' evaluation characteristics for real-world applications on the metric. Although conventional IEC with a function learning the current IEC user's evaluation characteristics cannot use the evaluation characteristics until the model is learned, the proposed IEC uses models learned from previous users until the current user's behavior is learned. The model from a previous IEC user whose evaluation values are most similar to those of the current IEC user is selected and used instead of the current IEC user's model till the current user's model is leaned. The viability of this method is evaluated on similarity distance of evaluation characteristics with simulation, and the simulation results are compared with the real IEC user's evaluation characteristics for four different types of real applications. Through this evaluation, we obtain a rating method for predicting the effectiveness of the proposed acceleration method for different types of IEC applications