Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating parse trees for input text segments. One of the methods includes obtaining an input text segment, processing the input text segment using a first long short term memory (LSTM) neural network to convert the input text segment into an alternative representation for the input text segment, and processing the alternative representation for the input text segment using a second LSTM neural network to generate a linearized representation of a parse tree for the input text segment.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method performed by one or more computers, the method comprising: obtaining an input text segment; processing the input text segment using a first long short term memory (LSTM) neural network to convert the input text segment into an alternative representation for the input text segment; and processing the alternative representation for the input text segment using a second LSTM neural network to generate a linearized representation of a parse tree for the input text segment. 2. The method of claim 1, further comprising: generating the parse tree for the input text segment from the linearized representation of the parse tree. 3. The method of claim 1, wherein the linearized representation of the parse tree for the input text segment is a sequence of symbols from a predetermined vocabulary of parse tree symbols. 4. The method of claim 1, wherein the input text segment is a variable length input text segment. 5. The method of claim 1, wherein the alternative representation is a vector of fixed dimensionality. 6. The method of claim 1, wherein processing the input text segment comprises: adding an end-of-sentence token to the end of the input text segment to generate a modified input text segment; and processing the modified input text segment using the first LSTM neural network. 7. The method of claim 1, wherein processing the alternative representation for the input text segment using the second LSTM neural network comprises initializing a hidden state of the second LSTM neural network to the alternative representation for the input text segment. 8. The method of claim 1, wherein processing the alternative representation for the input text segment using the second LSTM neural network comprises: processing the alternative representation for the input text segment using the second LSTM neural network to generate a respective sequence score for each of a plurality of possible linearized representations of parse trees; and selecting a possible linearized representation having a highest sequence score as the linearized representation of the parse tree for the input text segment. 9. The method of claim 8, wherein processing the alternative representation for the input text segment using the second LSTM neural network to generate a respective sequence score for each of a plurality of possible linearized representations of parse trees comprises: processing the alternative representation using the second LSTM neural network using a left to right beam search decoding. 10. The method of claim 8, wherein the set of possible linearized representations comprises possible linearized representations of varying lengths. 11. The method of claim 1, further comprising: training the first LSTM neural network and the second LSTM neural network using Stochastic Gradient Descent. 12. The method of claim 1, wherein one or more of the first LSTM neural network or the second LSTM neural network is a deep LSTM neural network. 13. The method of claim 1, wherein the parse tree is a tree that represents the syntactic structure of the text segment according to a context-free grammar, and wherein a linearized representation of a particular parse tree is generated by traversing the particular parse tree in a depth-first traversal order. 14. A system comprising one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: obtaining an input text segment; processing the input text segment using a first long short term memory (LSTM) neural network to convert the input text segment into an alternative representation for the input text segment; and processing the alternative representation for the input text segment using a second LSTM neural network to generate a linearized representation of a parse tree for the input text segment. 15. The system of claim 14, the operations further comprising: generating the parse tree for the input text segment from the linearized representation of the parse tree. 16. The system of claim 14, wherein the linearized representation of the parse tree for the input text segment is a sequence of symbols from a predetermined vocabulary of parse tree symbols. 17. The system of claim 14, wherein processing the alternative representation for the input text segment using the second LSTM neural network comprises initializing a hidden state of the second LSTM neural network to the alternative representation for the input text segment. 18. The system of claim 17, wherein processing the alternative representation for the input text segment using the second LSTM neural network comprises: processing the alternative representation for the input text segment using the second LSTM neural network to generate a respective sequence score for each of a plurality of possible linearized representations of parse trees; and selecting a possible linearized representation having a highest sequence score as the linearized representation of the parse tree for the input text segment. 19. The system of claim 14, wherein the parse tree is a tree that represents the syntactic structure of the text segment according to a context-free grammar, and wherein a linearized representation of a particular parse tree is generated by traversing the particular parse tree in a depth-first traversal order. 20. A computer program product encoded on one or more non-transitory computer storage media, the computer program product comprising instruction that, when executed by one or more computers, cause the one or more computers to perform operations comprising: obtaining an input text segment; processing the input text segment using a first long short term memory (LSTM) neural network to convert the input text segment into an alternative representation for the input text segment; and processing the alternative representation for the input text segment using a second LSTM neural network to generate a linearized representation of a parse tree for the input text segment.