SLING: A framework for frame semantic parsing
This work addresses the need for efficient semantic parsing in natural language processing, though it appears incremental as it builds on existing transition-based and neural network methods.
The authors tackled the problem of parsing natural language into semantic frames by introducing SLING, a framework that uses a transition-based neural network model trained end-to-end, achieving efficient and scalable parsing with fast inference.
We describe SLING, a framework for parsing natural language into semantic frames. SLING supports general transition-based, neural-network parsing with bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. The parsing model is trained end-to-end using only the text tokens as input. The transition system has been designed to output frame graphs directly without any intervening symbolic representation. The SLING framework includes an efficient and scalable frame store implementation as well as a neural network JIT compiler for fast inference during parsing. SLING is implemented in C++ and it is available for download on GitHub.