物語における話者と感情アノテーション付き会話コーパス:対話データセット
Uzma Patil1, Sejal Hanmante1, Shivani Patil1
1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.
Scientific data
|February 24, 2026
まとめ
本研究では、話者と感情のラベルが付いた、物語からの新しい対話データセットを紹介します。このリソースは、感情的に表現力のあるAIストーリーテリングのための言語モデルのトレーニングに役立ちます。
科学分野:
- 自然言語処理
- 計算言語学
- 人工知能
背景:
- 対話データセットは、会話型AIのトレーニングに不可欠です。
- 既存のデータセットには、微妙な感情と話者のアノテーションが欠けていることがよくあります。
- 物語フィクションは、データセット作成のためのコンテキスト埋め込み対話を豊富に提供します。
主な方法:
- データソースとしてHugging Faceストーリーコーパスを利用しました。
- 話者属性とエンティティ解決のためにFastCorefとBook NLPを採用しました。
- キーワード抽出のためにBi-GRU感情分類器とMistral-7Bを開発しました。
結論:
- 新しいデータセットは、感情認識対話システムのトレーニング機能を大幅に強化します。
- このリソースは、AI駆動のストーリーテリングと表現力のある音声合成の進歩を促進します。
- この方法論は、同様のアノテーション付き対話コーパスを作成するためのスケーラブルなアプローチを提供します。
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