Tree-structured neural networks exploit valuable syntactic parse information as they interpret the meanings of sentences. However, they suffer from two key technical problems that make them slow and unwieldy for large-scale NLP tasks: they usually operate on parsed sentences and they do not directly support batched computation. We address these issues by introducing the Stackaugmented Parser-Interpreter Neural Network (SPINN), which combines parsing and interpretation within a single treesequence hybrid model by integrating treestructured sentence interpretation into the linear sequential structure of a shift-reduce parser. Our model supports batched computation for a speedup of up to 25× over other tree-structured models, and its integrated parser can operate on unparsed data with little loss in accuracy. We evaluate it on the Stanford NLI entailment task and show that it significantly outperforms other sentence-encoding models.
Bowman et al. REF introduced the Stack-augmented Parser-Interpreter Neural Network (or SPINN in short) to combine parsing and interpretation within a single tree-sequence hybrid model by integrating tree-structured sentence interpretation into the linear sequential structure of a shift-reduce parser.
A Fast Unified Model for Parsing and Sentence Understanding
ArXiv
ArXiv
Computer Science