Question Answer Driven Zero-shot Slot Filling with Weak Supervision Pretraining

Slot-filling is an essential component for building task-oriented dialog systems. In this work, we focus on the zero-shot slot-filling (ZSSF) problem, where the model needs to predict slots and their values given utterances from new domains with zero training data. Prior methods for ZSSF directly learn representations for slots descriptions and utterances for extracting slot fillers. However, there are ambiguity and loss of information in encoding the raw slot description, which can hurt the models' zero-shot capacity. To address this problem, we introduce QA-driven slot filling (QASF), which extracts slot-filler spans from utterances with a span-based QA model. We use a linguistically motivated questioning strategy for turning the descriptions into questions, allowing the model to generalize to unseen slot types. Furthermore, our QASF model better utilizes weak supervision signals from QA pairs synthetically generated from conversations.

• Machine Intelligence

• Natural Language Processing