基於多任務與遷移學習的對話情感預測

Abstract

情感狀態對於人類的行為、動機和決策具有重要影響，因此旨在模擬或預測人類反應的對話系統必須仔細考慮情感這個因素。為了優化對話系統的對話體驗與使用者滿意度，進行使用者情感狀態的預測至關重要。目前的研究主要集中於識別現有對話中的情感狀態，忽略了對即將到來的情感狀態進行主動預測的重要性。然而，若能主動預測對話中即將到來的情感狀態，就能使對話系統有能力事前主動調整將給予使用者的回覆。 因此在本研究中，我們專注於探討對話情感預測這個任務，並提出了一個多任務學習模型，將歷史對話情感識別、歷史對話行為識別，以及未來對話行為預測作為輔助任務，並發展一個新穎的機制讓模型在訓練過程中動態調整不同任務之間的重要性。實驗證實了我們的多任務學習模型能有效地捕獲更多面向的情感相關資訊，並讓模型能夠學習到更好的情感特徵表示，從而提高了情感預測任務的表現，並在整體的準確率上優於當今表現最好的方法。 此外，為了能更貼近現實應用，我們也創建了一個基於常見對話系統場景的全新對話資料集，並在此資料集上進行了領域遷移實驗，最後也驗證了我們提出的領域遷移方法的有效性。我們的研究強調了多任務學習和領域遷移學習在情感預測任務中的重要性，也為開發更複雜的情感分析技術提供了基礎，以提升對話系統中的情感理解能力並改善使用者體驗。

Affective states profoundly influence human behaviors, motivations, and decisions, making them a crucial factor to consider in dialogue systems aimed at simulating or predicting human reactions. To improve the conversational experience and user satisfaction in dialogue systems, prediction of users' affective states is essential. Existing research primarily focuses on recognizing affective states within dialogue history, neglecting the proactive forecasting of upcoming affective states. However, the ability to forecast upcoming affective states proactively can enable dialogue systems to adjust responses in advance. Therefore, in this research, we concentrate on the task of Sentiment Forecasting in Dialogue and propose a multi-task learning model by incorporating sentiment recognition and dialogue act recognition within dialogue history sequence and upcoming dialogue act forecasting as auxiliary tasks. We also develop a novel mechanism to dynamically adjust the importance of each task during training. Experimental results demonstrate the effectiveness of our model in capturing diverse sentiment-related information and learning better sentiment representations, leading to improved sentiment forecasting performance, surpassing existing state-of-the-art methods. Additionally, to enhance real-world applicability, we collect a new dialogue dataset simulating common dialogue scenarios and conduct domain transfer experiments, further validating the efficacy of our proposed domain transfer methods. Our research emphasizes the significance of multi-task learning and domain transfer in sentiment forecasting tasks, providing a foundation for developing more sophisticated sentiment analysis techniques, improving sentiment understanding in dialogue systems, and enhancing user experiences.