基于管道的方法是目前任务型对话系统的主要构建方式,在工业界具有广泛应用,而对话状态跟踪(dialogue state tracking,DST)是任务型对话系统中的核心任务。面对传统的方法在多领域场景下表现较差的问题,该文结合语言模型预训练的最新研究成果,该文提出了一种基于BERT的对话状态跟踪算法Q2SM(query to state model)。该模型的上游使用了基于BERT的句子表征与相似度交互的槽判定模块,下游使用了一种面向对话状态跟踪任务的自定义RNN:DST-RNN。在WOZ 2.0和MultiWOZ 2.0两个数据集上的实验表明,Q2SM相比于之前的最好模型,分别在联合准确率和状态F1值两个评价指标上提升了1.09%和2.38%。此外,模型消融实验验证了,DST-RNN相比于传统的RNN或LSTM,不仅可以提升评价指标值,还可以加快模型的收敛速度。
Previous work on the one-class collaborative filtering （OCCF） problem can be roughly categorized into pointwise methods, pairwise methods, and content-based methods. A fundamental assumption of these approaches is that all missing values in the user-item rating matrix are considered negative. However, this assumption may not hold because the missing values may contain negative and positive examples. For example, a user who fails to give positive feedback about an item may not necessarily dislike it; he may simply be unfamiliar with it. Meanwhile, content-based methods, e.g. collaborative topic regression （CTR）, usually require textual content information of the items, and thus their applicability is largely limited when the text information is not available. In this paper, we propose to apply the latent Dirichlet allocation （LDA） model on OCCF to address the above-mentioned problems. The basic idea of this approach is that items are regarded as words, users are considered as documents, and the user-item feedback matrix constitutes the corpus. Our model drops the strong assumption that missing values are all negative and only utilizes the observed data to predict a user＇s interest. Additionally, the proposed model does not need content information of the items. Experimental results indicate that the proposed method outperforms previous methods on various ranking-oriented evaluation metrics.We further combine this method with a matrix factorizationbased method to tackle the multi-class collaborative filtering （MCCF） problem, which also achieves better performance on predicting user ratings.
