Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting answers to questions about documents. One of the methods includes receiving a document comprising a plurality of document tokens; receiving a question associated with the document, the question comprising a plurality of question tokens; processing the document tokens and the question tokens using a reader neural network to generate a joint numeric representation of the document and the question; and selecting, from the plurality of document tokens, an answer to the question using the joint numeric representation of the document and the question.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving a document comprising a plurality of document tokens; receiving a question associated with the document, the question comprising a plurality of question tokens; processing the document tokens and the question tokens using a reader neural network to generate a joint numeric representation of the document and the question; and selecting, from the plurality of document tokens, an answer to the question using the joint numeric representation of the document and the question. 2. The method of claim 1, wherein the joint numeric representation of the document and the question is a vector of numeric values. 3. The method of claim 1, wherein selecting the answer to the question using the joint numeric representation of the document and the question comprises: processing the joint numeric representation using a selection neural network to generate a respective token score for each of a plurality of token types; and selecting a token that is of a highest-scoring token type as the answer to the question. 4. The method of claim 1, wherein the reader neural network is a deep long short-term memory (LSTM) neural network, and wherein processing the document tokens and the question tokens using the reader neural network comprises: generating a sequence that includes the document tokens and the question tokens; and processing each token in the sequence using the deep LSTM neural network to generate the joint numeric representation. 5. The method of claim 4, wherein the sequence comprises the document tokens followed by a delimiter token followed by the question tokens. 6. The method of claim 4, wherein the sequence comprises the question tokens followed by a delimiter token followed by the document tokens. 7. The method of claim 4, wherein the deep LSTM neural network has skip connections from an input to the deep LSTM neural network to each hidden LSTM layer in the deep LSTM neural network and from each hidden LSTM layer to an output layer of the deep LSTM neural network. 8. The method of claim 1, wherein the reader neural network comprises a document forward long short-term memory (LSTM) neural network and a document backward LSTM neural network, and wherein processing the document tokens and the question tokens using the reader neural network further comprises: processing each document token in a forward order using the document forward LSTM neural network to generate a respective forward representation for each document token; processing each document token in a backward order using the document backward LSTM neural network to generate a respective backward representation of the document; and for each document token, combining the forward representation of the document token and the backward representation of the document token to generate a composite representation of the document token. 9. The method of claim 8, wherein the reader neural network comprises a question forward LSTM neural network and a question backward LSTM neural network, and wherein processing the document tokens and the question tokens using the reader neural network further comprises: processing each question token in a forward order using the question forward LSTM neural network to generate a respective forward representation for each question token; processing each question token in a backward order using the question backward LSTM neural network to generate a respective backward representation of each question token; for each question token, combining the forward representation of the question token and the backward representation of the question token to generate a composite representation of the question token; and combining the forward representation of the last question token and the backward representation of the first question token to generate a composite question representation of the question. 10. The method of claim 9, wherein processing the document tokens and the question tokens using the reader neural network further comprises: identifying an initial document representation for the document; for each question token: processing the composite representation of the question token, the composite representations of the document tokens, and a current document representation for the document through one or more neural network layers to update a respective weight for each document token, and combining the composite representations of the document tokens in accordance with the updated weights for the document tokens to generate an updated document representation; and generating the joint representation by combining the document representation after the last question token and the composite question representation. 11. The method of claim 1, wherein the reader neural network comprises a question forward long short-term memory (LSTM) neural network and a question backward LSTM neural network, and wherein processing the document tokens and the question tokens using the reader neural network comprises: processing each question token in a forward order using the question forward LSTM neural network to generate a forward representation of the question; processing each question token in a backward order using the question backward LSTM neural network to generate a backward representation of the question; and combining the forward representation of the question and the backward representation of the question to generate a composite question representation. 12. The method of claim 11, wherein the reader neural network further comprises a document forward LSTM neural network and a document backward LSTM neural network, and wherein processing the document tokens and the question tokens using the reader neural network further comprises: processing each document token in a forward order using the document forward LSTM neural network to generate a respective forward representation for each document token; processing each document token in a backward order using the document backward LSTM neural network to generate a respective backward representation of the document; and for each document token, combining the forward representation of the document and the backward representation of the document to generate a composite representation of the document token. 13. The method of claim 12, wherein processing the document tokens and the question tokens using the reader neural network further comprises: determining a respective weight for each document token; determining, for each document token, a weighted composite representation by computing the product of the weight for the document token and the composite representation of the document token; determining a composite document representation by combining the weighted composite representations of the document tokens; and generating the joint representation by combining the composite document representation and the composite question representation. 14. The method of claim 13, wherein determining the respective weight for each document token comprises: processing the composite representation of the document token and the composite query representation through one or more neural network layers to generate the weight for the document token. 15. A system comprising one or more computers and one or more storage devices storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: receiving a document comprising a plurality of document tokens; receiving a question associated with the document, the question comprising a plurality of question tokens; processing the document tokens and the question tokens using a reader neural network to generate a joint numeric representation of the document and the question; and selecting, from the plurality of document tokens, an answer to the question using the joint numeric representation of the document and the question. 16. The system of claim 15, wherein selecting the answer to the question using the joint numeric representation of the document and the question comprises: processing the joint numeric representation using a selection neural network to generate a respective token score for each of a plurality of token types; and selecting a token that is of a highest-scoring token type as the answer to the question. 17. The system of claim 15, wherein the reader neural network is a deep long short-term memory (LSTM) neural network, and wherein processing the document tokens and the question tokens using the reader neural network comprises: generating a sequence that includes the document tokens and the question tokens; and processing each token in the sequence using the deep LSTM neural network to generate the joint numeric representation. 18. The system of claim 15, wherein the reader neural network comprises a document forward long short-term memory (LSTM) neural network and a document backward LSTM neural network, and wherein processing the document tokens and the question tokens using the reader neural network further comprises: processing each document token in a forward order using the document forward LSTM neural network to generate a respective forward representation for each document token; processing each document token in a backward order using the document backward LSTM neural network to generate a respective backward representation of the document; and for each document token, combining the forward representation of the document token and the backward representation of the document token to generate a composite representation of the document token. 19. The system of claim 18, wherein the reader neural network comprises a question forward LSTM neural network and a question backward LSTM neural network, and wherein processing the document tokens and the question tokens using the reader neural network further comprises: processing each question token in a forward order using the question forward LSTM neural network to generate a respective forward representation for each question token; processing each question token in a backward order using the question backward LSTM neural network to generate a respective backward representation of each question token; for each question token, combining the forward representation of the question token and the backward representation of the question token to generate a composite representation of the question token; and combining the forward representation of the last question token and the backward representation of the first question token to generate a composite question representation of the question. 20. The system of claim 19, wherein processing the document tokens and the question tokens using the reader neural network further comprises: identifying an initial document representation for the document; for each question token: processing the composite representation of the question token, the composite representations of the document tokens, and a current document representation for the document through one or more neural network layers to update a respective weight for each document token, and combining the composite representations of the document tokens in accordance with the updated weights for the document tokens to generate an updated document representation; and generating the joint representation by combining the document representation after the last question token and the composite question representation. 21. The system of claim 15, wherein the reader neural network comprises a question forward long short-term memory (LSTM) neural network and a question backward LSTM neural network, and wherein processing the document tokens and the question tokens using the reader neural network comprises: processing each question token in a forward order using the question forward LSTM neural network to generate a forward representation of the question; processing each question token in a backward order using the question backward LSTM neural network to generate a backward representation of the question; and combining the forward representation of the question and the backward representation of the question to generate a composite question representation. 22. The system of claim 21, wherein the reader neural network further comprises a document forward LSTM neural network and a document backward LSTM neural network, and wherein processing the document tokens and the question tokens using the reader neural network further comprises: processing each document token in a forward order using the document forward LSTM neural network to generate a respective forward representation for each document token; processing each document token in a backward order using the document backward LSTM neural network to generate a respective backward representation of the document; and for each document token, combining the forward representation of the document and the backward representation of the document to generate a composite representation of the document token. 23. The system of claim 22, wherein processing the document tokens and the question tokens using the reader neural network further comprises: determining a respective weight for each document token; determining, for each document token, a weighted composite representation by computing the product of the weight for the document token and the composite representation of the document token; determining a composite document representation by combining the weighted composite representations of the document tokens; and generating the joint representation by combining the composite document representation and the composite question representation. 24. The system of claim 23, wherein determining the respective weight for each document token comprises: processing the composite representation of the document token and the composite query representation through one or more neural network layers to generate the weight for the document token. 25. One or more non-transitory computer storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: receiving a document comprising a plurality of document tokens; receiving a question associated with the document, the question comprising a plurality of question tokens; processing the document tokens and the question tokens using a reader neural network to generate a joint numeric representation of the document and the question; and selecting, from the plurality of document tokens, an answer to the question using the joint numeric representation of the document and the question.