Slot Filling with Data Augmentation That Allows the Use of Keyword and Context Information for Handling Unknown Slot Values

Abstract

This paper proposes a new method for slot filling of unknown slot values (i.e., those are not included in the training data) in spoken dialogue systems. Slot filling detects slot values from user utterances and handles named entities such as product and restaurant names. In the real world, there is a steady stream of new named entities and it would be infeasible to add all of them as training data. Accordingly, it is inevitable that users will input utterances with unknown slot values and spoken dialogue systems must correctly estimate them. We provide a value detector that detects keywords representing slot values ignoring slots and a slot estimator that estimates slots for detected keywords. Context information can be an important clue for estimating slot values because the values in a given slot tend to appear in similar contexts. The value detector is trained with positive samples, which have keywords corresponding to slot values replaced with random words, thereby enabling the use of context information. However, any approach that can detect unknown slot values may produce false alarms because the features of unknown slot values are unseen and it is difficult to distinguish keywords of unknown slot values from non-keywords, which do not correspond to slot values. Therefore, we introduce a negative sample method that replaces keywords with nonkeywords randomly, which allows the slot estimator to learn to reject non-keywords. Experimental results show that the proposed method achieves an 6,15 and 78% relative improvement in F1 score compared with an existing model on three datasets, respectively.