Abstract
Slot filling is a crucial component in task-oriented dialog systems that is used to parse (user) utterances into semantic concepts called slots. An ontology is defined by the collection of slots and the values that each slot can take. The most widely used practice of treating slot filling as a sequence labeling task suffers from two main drawbacks. First, the ontology is usually pre-defined and fixed and therefore is not able to detect new labels for unseen slots. Second, the one-hot encoding of slot labels ignores the correlations between slots with similar semantics, which makes it difficult to share knowledge learned across different domains. To address these problems, we propose a new model called elastic conditional random field (eCRF), where each slot is represented by the embedding of its natural language description and modeled by a CRF layer. New slot values can be detected by eCRF whenever a language description is available for the slot. In our experiment, we show that eCRFs outperform existing models in both in-domain and cross-domain tasks, especially in predicting unseen slots and values.
Highlights
Slot filling [1,2] is a crucial component in task-oriented dialog systems and parses utterances into semantic concepts in terms of a set of named entities called slots
The results show that elastic conditional random field (eCRF) significantly outperform a Bidirectional long short-term memory (BiLSTM) baseline and the concept tagger (CT) in [12] for both tasks, especially in predictions of unseen slots and values
We propose a novel framework called elastic conditional random field, which consists of three parts
Summary
A label, e.g., B-time, is a combination of the slot name and one of the IOB tags.These labels are used to identify the values for different slots from the utterance In this manner, slot filling is treated as a sequence labeling task, as illustrated, for which the two dominant classes of methods are based on recurrent neural networks (RNNs) [1] and conditional random fields (CRFs) [4], respectively. Slot filling is treated as a sequence labeling task, as illustrated, for which the two dominant classes of methods are based on recurrent neural networks (RNNs) [1] and conditional random fields (CRFs) [4], respectively This practice has been widely employed for slot filling [2,5] and many other similar sequence labeling problems [6].
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