Examples of the present disclosure provide systems and methods relating to a machine comprehension test with a learning-based approach, harnessing neural networks arranged in a parallel hierarchy. This parallel hierarchy enables the model to compare the passage, question, and answer from a variety of perspectives, as opposed to using a manually designed set of features. Perspectives may range from the word level to sentence fragments to sequences of sentences, and networks operate on word-embedding representations of text. A training methodology for small data is also provided.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: at least one processor; and memory soring instructions that, when executed by the at least one processor, perform a set of operations comprising: receiving text input, wherein the text input; converting the text input into a distributed representation; performing parallel-hierarchical machine comprehension on the text input by analyzing the distributed representation of the text input by two or more processes in parallel; combining the results of the two or more processes using a neural network in order to determine a top result; and providing the top result. 2. The system of claim 1, wherein the text input comprises: text data; question data; and answer data. 3. The system of claim 2, wherein the text input comprises natural language text. 4. The system of claim 1, wherein the two or more processes comprise at least a semantic process and a word-by-word process. 5. The system of claim 4, wherein the semantic process compares a hypothesis to sentences in the text data. 6. The system of claim 5, wherein the hypothesis is generated by combing at least a portion of the question data with at least a portion of the answer data. 7. The system of claim 4, wherein the word-by-word process comprises at least one of: a sentential process; a sliding window sequential process; and a dependency sliding window dependency process. 8. The system of claim 7, wherein the sliding window sequential process scans over words of the text data as one continuous sequence. 9. The system of claim 7, wherein the sliding window dependency process comprises: constructing dependency graph for a sentence in the text data; reordering words in the sentence based at least in part on the dependency graph to generate a reordered sentence; and scans over words of the reordered sentence. 10. The system of claim 1, wherein the neural network comprises a multilayer perceptron. 11. A method comprising: receiving text data; converting the text input into a first distributed representation using a multilayer perceptron neural network; converting the text input into a second distributed representation using a multilayer perceptron plus sum neural network; performing parallel-hierarchical machine comprehension on the text input by analyzing the distributed representation of the text input by two or more processes in parallel, wherein performing parallel-hierarchical machine comprehension comprises: analyzing the first distributed representation using one or more word-by-word processes; and analyzing the second distributed representation using a semantic processes; combining the results from the one or more word-by-word processes and the semantic process using a neural network in order to determine a top result; and providing the top result. 12. The method of claim 11, wherein the text input comprises: text data; question data; and answer data. 13. The method of claim 12, wherein the semantic process compares a hypothesis to sentences in the text data. 14. The method of claim 13, wherein the hypothesis is generated by combing at least a portion of the question data with at least a portion of the answer data. 15. The method of claim 14, wherein the hypothesis is compared to text data using cosine similarity. 16. The method of claim 11, wherein the one or more word-by-word process comprises at least one of: a sentential process; a sliding window sequential process; and a dependency sliding window dependency process. 17. The method of claim 16, wherein the sliding window sequential process scans over words of the text data as one continuous sequence. 19. The method of claim 16, wherein the sliding window dependency process comprises: constructing dependency graph for a sentence in the text data; reordering words in the sentence based at least in part on the dependency graph to generate a reordered sentence; and scans over words of the reordered sentence. 19. A computer storage medium comprising computer executable instructions that, when executed by at least one processor, perform a set of operations comprising: receiving text data; converting the text input into a first distributed representation using a multilayer perceptron neural network; converting the text input into a second distributed representation using a multilayer perceptron plus sum neural network; performing parallel-hierarchical machine comprehension on the text input by analyzing the distributed representation of the text input by two or more processes in parallel, wherein performing parallel-hierarchical machine comprehension comprises: analyzing the first distributed representation using one or more word-by-word processes; and analyzing the second distributed representation using a semantic processes; combining the results from the one or more word-by-word processes and the semantic process using a neural network in order to determine a top result; and providing the top result. 20. The computer storage medium of claim 19, wherein the text input comprises: text data; question data; and answer data.