通过对话上下文提升音乐需求识别准确率
Predicting User Intents and Musical Attributes from Music Discovery Conversations
- 用历史对话拼接单轮查询，增强上下文理解
- 在两个任务上均显著提升F1分数
- 适合做音乐推荐系统和对话式交互研究
意图分类是识别用户输入中需求的任务。尽管该任务在多个领域被广泛研究，但在音乐领域仍缺乏关注。本文针对音乐发现对话场景，研究预训练语言模型的意图分类方法，不仅预测功能需求（意图分类），还新增音乐属性分类任务。提出将前序对话历史与单轮用户查询拼接作为输入，以更好理解整体对话上下文。实验表明，所提模型在用户意图和音乐属性分类上均显著提升F1分数，优于Llama 3的零样本和少样本表现。
原文摘要 · Abstract (English)
Intent classification is a text understanding task that identifies user needs from input text queries. While intent classification has been extensively studied in various domains, it has not received much attention in the music domain. In this paper, we investigate intent classification models for music discovery conversation, focusing on pre-trained language models. Rather than only predicting functional needs: intent classification, we also include a task for classifying musical needs: musical attribute classification. Additionally, we propose a method of concatenating previous chat history with just single-turn user queries in the input text, allowing the model to understand the overall conversation context better. Our proposed model significantly improves the F1 score for both user intent and musical attribute classification, and surpasses the zero-shot and few-shot performance of the pretrained Llama 3 model.
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