In recent years, there has been an increase in the number of devices with virtual assistants (e.g: Siri, Google Home, Alexa) in our living rooms and kitchens. As a result of this, these devices receive several queries about recipes. All these queries will contain terms relating to a "recipe-domain" i.e: they will contain dish-names, ingredients, cooking times, dietary preferences etc. Extracting these recipe-relevant aspects from the query thus becomes important when it comes to addressing the user's information need. Our project focuses on extracting ingredients from such plain-text user utterances. Our best performing model was a fine-tuned BERT which achieved an F1-score of $95.01$. We have released all our code in a GitHub repository.
翻译:近年来,我们客厅和厨房的虚拟助手(如Siri、谷歌之家、Alexa)的装置数量有所增加,因此,这些装置接受关于食谱的若干询问。所有这些查询都包含与“recipe-domain”有关的术语,即:它们将包含菜名、配料、烹饪时间、饮食偏好等。因此,从查询中提取这些食谱相关内容对于满足用户的信息需求非常重要。我们的项目侧重于从这种简便的用户语句中提取成分。我们最出色的模型是经过微调的BERT,它达到了95.011美元的F1芯。我们已经在GitHub存放处公布了我们所有的代码。