论文标题
使用轻量级神经网络模型在配方理解任务中的行动识别和状态变化预测
Action Recognition and State Change Prediction in a Recipe Understanding Task Using a Lightweight Neural Network Model
论文作者
论文摘要
考虑一个自然语言句子，描述食物配方中的特定步骤。在这样的说明中，识别动作（例如压榨，烘烤等）以及成分状态（形状模制，蛋奶煮熟，温度热等）的结果变化是一项艰巨的任务。应对这一挑战的一种方法是明确建模模拟模块，该模块将操作应用于实体并预测结果结果（Bosselut etal。2018）。但是，这样的模型可能不必要地复杂。在本文中，我们提出了一个简化的神经网络模型，该模型将动作识别和状态变化预测分开，同时通过新的损失函数耦合两者。这允许学习间接地相互影响。我们的模型虽然更简单，但可以实现较高的状态变化预测性能（我们的平均准确性为67％，而55％（Bosselut等人，2018年）），而训练的样本更少（Bosselut等人的65K++ 65K+）。
Consider a natural language sentence describing a specific step in a food recipe. In such instructions, recognizing actions (such as press, bake, etc.) and the resulting changes in the state of the ingredients (shape molded, custard cooked, temperature hot, etc.) is a challenging task. One way to cope with this challenge is to explicitly model a simulator module that applies actions to entities and predicts the resulting outcome (Bosselut et al. 2018). However, such a model can be unnecessarily complex. In this paper, we propose a simplified neural network model that separates action recognition and state change prediction, while coupling the two through a novel loss function. This allows learning to indirectly influence each other. Our model, although simpler, achieves higher state change prediction performance (67% average accuracy for ours vs. 55% in (Bosselut et al. 2018)) and takes fewer samples to train (10K ours vs. 65K+ by (Bosselut et al. 2018)).
