论文标题
部分可观测时空混沌系统的无模型预测
Opponent Modeling in Negotiation Dialogues by Related Data Adaptation
论文作者
论文摘要
对手建模是在社会互动的背景下推断另一方的精神状态的任务。在多发谈论中，它涉及推断对手对讨论的每个问题的相对重要性，这对于查找高价值交易至关重要。该任务的实用模型需要根据部分对话作为输入来推断对手的这些优先级，而无需额外的培训注释。在这项工作中，我们提出了一个从谈判对话中确定这些优先级的排名者。该模型将部分对话作为输入，并预测对手的优先顺序。我们进一步设计了适应此任务相关数据源的方法，以提供更明确的监督，以纳入对手的偏好和要约，以依靠依赖颗粒状的话语级注释。我们通过两个对话数据集通过广泛的实验来展示我们提出的方法的实用性。我们发现，提出的数据改编导致零击和几乎没有射击方案的强劲性能。此外，它们允许模型的性能比基线更好，同时访问对手的话语更少。我们发布我们的代码，以支持这一方向的未来工作。
Opponent modeling is the task of inferring another party's mental state within the context of social interactions. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training. In this work, we propose a ranker for identifying these priorities from negotiation dialogues. The model takes in a partial dialogue as input and predicts the priority order of the opponent. We further devise ways to adapt related data sources for this task to provide more explicit supervision for incorporating the opponent's preferences and offers, as a proxy to relying on granular utterance-level annotations. We show the utility of our proposed approach through extensive experiments based on two dialogue datasets. We find that the proposed data adaptations lead to strong performance in zero-shot and few-shot scenarios. Moreover, they allow the model to perform better than baselines while accessing fewer utterances from the opponent. We release our code to support future work in this direction.
