An Efficient Approach to Encoding Context for Spoken Language Understanding
This work addresses efficiency improvements for SLU in dialogue systems, but it appears incremental as it builds on existing context encoding methods.
The paper tackles the trade-off between accuracy and computational efficiency in spoken language understanding (SLU) for task-oriented dialogue systems by proposing an efficient context encoding method using a shared RNN module. It demonstrates effectiveness on dialogues from two domains, though no concrete numbers are provided.
In task-oriented dialogue systems, spoken language understanding, or SLU, refers to the task of parsing natural language user utterances into semantic frames. Making use of context from prior dialogue history holds the key to more effective SLU. State of the art approaches to SLU use memory networks to encode context by processing multiple utterances from the dialogue at each turn, resulting in significant trade-offs between accuracy and computational efficiency. On the other hand, downstream components like the dialogue state tracker (DST) already keep track of the dialogue state, which can serve as a summary of the dialogue history. In this work, we propose an efficient approach to encoding context from prior utterances for SLU. More specifically, our architecture includes a separate recurrent neural network (RNN) based encoding module that accumulates dialogue context to guide the frame parsing sub-tasks and can be shared between SLU and DST. In our experiments, we demonstrate the effectiveness of our approach on dialogues from two domains.