Feature Structures in the Wild: A Case Study in Mixing Traditional Linguistic Knowledge Representation with Neural Language Models
Published in Proceedings of the ACL-21 Workshop on Computing Semantics with Types, Frames and Related Structures, 2021
This paper briefly presents an evaluation of three models: a domain-specific one based upon typed feature structures, a neural language model, and a mixture of the two, on an unseen but in-domain corpus of user queries in the context of a dialogue classification task. We find that the mixture performs the best, which opens the door to a potentially new application of neural language models. A further examination of the domain- We also consider the inner workings of the domainspecific model in more detail, as well as how it came into being, from an ethnographic perspective. This has changed our perspective on the potential role of structured representations in the future of dialogue systems, and suggests that formal research in this area may have a new role to play in validating and coordinating ad hoc dialogue systems development.
Recommended citation: Penn, Gerald and Shi, Ken. (2021). "Feature Structures in the Wild: A Case Study in Mixing Traditional Linguistic Knowledge Representation with Neural Language Models." Proceedings of the ACL-21 Workshop on Computing Semantics with Types, Frames and Related Structures. 53(7). https://aclanthology.org/2021.cstfrs-1.6.pdf