Consituency parsing aims to extract a constituency-based parse tree from a sentence that represents its syntactic structure according to a phrase structure grammar.
Sentence (S) | +-------------+------------+ | | Noun (N) Verb Phrase (VP) | | John +-------+--------+ | | Verb (V) Noun (N) | | sees Bill
Recent approaches convert the parse tree into a sequence following a depth-first traversal in order to be able to apply sequence-to-sequence models to it. The linearized version of the above parse tree looks as follows: (S (N) (VP V N)).
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The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades.
#10 best model for Constituency Parsing on Penn Treebank
We extend our previous work on constituency parsing (Kitaev and Klein, 2018) by incorporating pre-training for ten additional languages, and compare the benefits of no pre-training, ELMo (Peters et al., 2018), and BERT (Devlin et al., 2018).
We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead to improvements to a state-of-the-art discriminative constituency parser.
#2 best model for Constituency Parsing on Penn Treebank
Recurrent Neural Networks can be trained to produce sequences of tokens given some input, as exemplified by recent results in machine translation and image captioning.
Parsing accuracy using efficient greedy transition systems has improved dramatically in recent years thanks to neural networks.