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As a key component in a dialogue system, dialogue state tracking plays an important role. It is very important for dialogue state tracking to deal with the problem of unknown slot values. As far as we known, almost all existing approaches depend on pointer network to solve the unknown slot value problem. These pointer network-based methods usually have a hidden assumption that there is at most one out-of-vocabulary word in an unknown slot value because of the character of a pointer network. However, often, there are multiple out-of-vocabulary words in an unknown slot value, and it makes the existing methods perform bad. To tackle the problem, in this paper, we propose a novel Context-Sensitive Generation network (CSG) which can facilitate the representation of out-of-vocabulary words when generating the unknown slot value. Extensive experiments show that our proposed method performs better than the state-of-the-art baselines.
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data. In this paper, we propose a slot description enhanced generative approach for zero-sh
The goal of dialogue state tracking (DST) is to predict the current dialogue state given all previous dialogue contexts. Existing approaches generally predict the dialogue state at every turn from scratch. However, the overwhelming majority of the sl
Scalability for handling unknown slot values is a important problem in dialogue state tracking (DST). As far as we know, previous scalable DST approaches generally rely on either the candidate generation from slot tagging output or the span extractio
Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set. In the practical application, a reliable dialogue system should know what it does not know. In this paper, we introduce a new task, Novel Slot D
Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is a major obstacle due to increased numbers of state candidates and dialogue lengths. T