Constituency 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.
Ranked #1 on Machine Translation on IWSLT2015 English-German
We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions.
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.
Ranked #8 on 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.