A method and system are provided. The method includes obtaining, by a hardware processor, candidate data representing a plurality of candidates. The method further includes calculating, by the hardware processor, for each of the candidates, a temporal next state of a Recurrent Neural Network (RNN) by inputting a corresponding one of the candidates to the RNN at a current state. The method also includes merging, by the hardware processor, the temporal next state for each of the candidates to obtain the temporal next state of the RNN.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: obtaining, by a hardware processor, candidate data representing a plurality of candidates; calculating, by the hardware processor, for each of the candidates, a temporal next state of a Recurrent Neural Network (RNN) by inputting a corresponding one of the candidates to the RNN at a current state; and merging, by the hardware processor, the temporal next state for each of the candidates to obtain the temporal next state of the RNN. 2. The method of claim 1, further comprising updating the current state of the RNN according to the temporal next state of the RNN. 3. The method of claim 1, wherein the obtaining step includes obtaining a plurality of occurrence probabilities for each of the candidates, each of the occurrence probabilities indicating a probability of occurrence for each of the candidates represented by the candidate data, and wherein the merging step includes calculating a mean value of temporal next states of the candidates, each of the temporal next states weighted by a respective one of the occurrence probabilities for each of the candidates. 4. The method of claim 1, wherein the RNN includes a Long Short-Term Memory (LSTM), and each of the current states and the next states includes a hidden state and a cell state. 5. The method of claim 1, wherein the candidate data has a directed graph structure, wherein each edge in the directed graph structure corresponds to one of the candidates from among the plurality of candidates. 6. The method of claim 1, wherein the candidate data has a confusion network structure, and wherein each link in the confusion network structure corresponds to one of the candidates from among the plurality of candidates. 7. The method of claim 6, wherein each of the candidates corresponds to a word or a phrase in a text. 8. The method of claim 7, wherein the obtaining candidate data further comprises generating, by speech recognition, speech-to-text data. 9. The method of claim 8, further comprising: obtaining training data including a candidate data set corresponding to a correct output, wherein the candidate data set includes the candidate data representing the plurality of candidates; and training the RNN based on the training data. 10. The method of claim 7, further comprising calculating output data by processing an output layer of the RNN based on at least the next state of the RNN, wherein the next state of the RNN corresponds to a recurrent layer of the RNN. 11. The method of claim 10, wherein the output data further comprises at least one selected from the group consisting of an answer of a slot filling problem, an answer of key word spotting, and translated text. 12. An apparatus comprising: a processor, configured to: obtain candidate data representing a plurality of candidates; calculate, for each of the candidates, a temporal next state of a Recurrent Neural Network (RNN) by inputting a corresponding one of the candidates to the RNN at a current state; and merge the temporal next state for each of the candidates to obtain the temporal next state of the RNN. 13. The apparatus of claim 12, wherein the processor is further configured to update the current state of the RNN according to the temporal next state of the RNN. 14. The apparatus of claim 12, wherein the processor obtains the candidate data by obtaining a plurality of occurrence probabilities, each of the occurrence probabilities indicating a probability of occurrence for each of the candidates represented by the candidate data, and the processor merges the temporal next state by calculating a mean value of temporal next states of the candidates, each of the temporal next states weighted by a respective one of the occurrence probabilities for each of the candidates. 15. The apparatus of claim 12, wherein the RNN is a Long Short-Term Memory (LSTM), and each of the current states and the next states includes a hidden state and a cell state. 16. The apparatus of claim 12, wherein the candidate data has a directed graph structure, and wherein each edge in the directed graph structure corresponds to one of the candidates among the plurality of candidates. 17. A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method, the method comprising: obtaining, by a hardware processor, candidate data representing a plurality of candidates; calculating, by the hardware processor, for each of the candidates, a temporal next state of a Recurrent Neural Network (RNN) by inputting a corresponding one of the candidates to the RNN at a current state; and merging, by the hardware processor, the temporal next state for each of the candidates to obtain the temporal next state of the RNN. 18. The computer program product of claim 17, wherein the method further comprises updating the current state of the RNN according to the next state of the RNN. 19. The computer program product of claim 17, wherein the obtaining step includes obtaining a plurality of occurrence probabilities, wherein each occurrence probability indicates a probability of occurrence for each of the candidates represented by the candidate data, and the merging step includes calculating a mean value of the temporal next states of the candidates, wherein each of the temporal next states is weighted by the occurrence probability for each of the candidates. 20. The computer program product of claim 17, wherein the RNN is a Long Short-Term Memory (LSTM), and each of the current states and the next states includes a hidden state and a cell state.