Extracting error productions from a neural network-based LR parser

Abstract It is well-known that traditional rule-based parsers have poor error recovery capability. This has limited their practicality in natural language processing, where robustness and flexibility are of primary concern. In view of this, we propose the neural network LR parser (NNLR) in which the shift-reduce parsing decision of the LR parser is simulated by a feedforward neural network. Being trained with a small set of grammatical sentences only, the NNLR is capable of parsing a significantly large number of erroneous sentences. To explore the knowledge encoded in the neural network that sustains its robust processing capacity, we analyze the NNLR in two ways. First, we show that in the NNLR, erroneous sentences are recovered as if the parser had filled in some of the empty slots in the original LR parsing table. An augmented parsing table is thus constructed. Second, a set of new grammar rules, commonly called error productions, can be extracted from the trained network. When being included in the original grammar, these rules allow certain erroneous sentences to be generated and parsed in addition to grammatical ones. In both analyses, the symbolic knowledge discovered is readily comprehensible, and it can potentially be re-used by the original LR parser to enhance its robustness.