Practical Semantic Parsing for Spoken Language Understanding
This work addresses the challenge of building efficient semantic parsers for real-world applications like voice assistants, though it is incremental in its approach.
The authors tackled the problem of executable semantic parsing for spoken language understanding by developing a transfer learning framework that improves performance across multiple domains, including Question Answering and SLU, as demonstrated on public datasets like Overnight and NLmaps and commercial data.
Executable semantic parsing is the task of converting natural language utterances into logical forms that can be directly used as queries to get a response. We build a transfer learning framework for executable semantic parsing. We show that the framework is effective for Question Answering (Q&A) as well as for Spoken Language Understanding (SLU). We further investigate the case where a parser on a new domain can be learned by exploiting data on other domains, either via multi-task learning between the target domain and an auxiliary domain or via pre-training on the auxiliary domain and fine-tuning on the target domain. With either flavor of transfer learning, we are able to improve performance on most domains; we experiment with public data sets such as Overnight and NLmaps as well as with commercial SLU data. The experiments carried out on data sets that are different in nature show how executable semantic parsing can unify different areas of NLP such as Q&A and SLU.