A context-aware attention-based neural network is provided for answering an input question given a set of purportedly supporting statements for the input question. The neural network includes a processing element. The processing element is configured to calculate a question representation for the input question, based on word annotations and word-level attentions calculated for the input question. The processing element is further configured to calculate a sentence representation for each of the purportedly supporting statements, based on word annotations and word-level attentions calculated for each of the purportedly supporting statements. The processing element is also configured to calculate a context representation for the set of purportedly supporting statements with respect to the sentence representation for each of the purportedly supporting statements. The processing element is additionally configured to generate an answer to the input question based on the question representation and the context representation.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A context-aware attention-based neural network for answering an input question given a set of purportedly supporting statements for the input question, the neural network comprising: a processing element configured to calculate a question representation for the input question, based on word annotations and word-level attentions calculated for the input question; calculate a sentence representation for each of the purportedly supporting statements, based on word annotations and word-level attentions calculated for each of the purportedly supporting statements; calculate a context representation for the set of purportedly supporting statements with respect to the sentence representation for each of the purportedly supporting statements; and generate an answer to the input question based on the question representation and the context representation. 2. The context-aware attention-based neural network of claim 1, wherein the processing element is further configured to: determine whether sufficient information exists to generate the answer based on the question representation and the context representation; and invoke, responsive to a determination that sufficient information is lacking to generate the answer based on the question representation and the context representation, an interactive process that (i) poses a supplemental question to a user, (ii) obtains feedback for the supplement question from the user, and (iii) generates an answer prediction for the input question based on the feedback. 3. The context-aware attention-based neural network of claim 2, wherein the processing element is configured to generate the answer to the input question based on the question representation and the context representation and bypass the interactive process, responsive to a determination that sufficient information exists to generate the answer based on the question representation and the context representation. 4. The context-aware attention based neural network of claim 2, wherein the processing element is configured to dynamically update the context vector responsive to, and using, the answer prediction. 5. The context-aware attention-based neural network of claim 1, wherein the processing element is further configured to employ a binary decision process that selectively (i) outputs the answer to the input question based on the question representation and the context representation and bypasses an interactive process, and (ii) invokes the interactive process that uses a supplementary question and user feedback to generate an answer prediction for the input question. 6. The context-aware attention-based neural network of claim 1, wherein the question representation, the sentence representation, and the context representation are calculated as a respective vector. 7. The context-aware attention-based neural network of claim 1, wherein the processing element is further configured to influence an attention over each word in the set of purportedly supporting statements using context information extracted from the set of purportedly supporting statements. 8. The context-aware attention-based neural network of claim 1, wherein the processing element is further configured to affect a word semantic meaning contributing to the sentence representation of one or more of the purportedly supporting statements using context information extracted from the set of purportedly supporting statements. 9. The context-aware attention-based neural network of claim 1, wherein the context-aware attention-based neural network is embodied in an Application Specific Integrated Circuit. 10. The context-aware attention-based neural network of claim 1, wherein the word-level attentions calculated for the input question are embodied as importance weights placed on each of the words in the input sentence to indicate a relative importance of contribution to the question representation. 11. The context-aware attention-based neural network of claim 10, wherein each of the importance weights are measured as respective vector similarities of word-based vectors derived from words in the input question. 12. The context-aware attention-based neural network of claim 1, wherein the processing element uses one or more Gated Recurrent Units to calculate the question representation, the sentence representation, and the context representation. 13. A computer program product for implementing a context-aware attention-based neural network for answering an input question given a set of purportedly supporting statements for the input question, the 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 comprising: calculating, by a processing element of the computer, a question representation for the input question, based on word annotations and word-level attentions calculated for the input question; calculating, by the processing element, a sentence representation for each of the purportedly supporting statements, based on word annotations and word-level attentions calculated for each of the purportedly supporting statements; calculating, by the processing element, a context representation for the set of purportedly supporting statements with respect to the sentence representation for each of the purportedly supporting statements; and generating, by the processing element, an answer to the input question based on the question representation and the context representation. 14. The computer program product of claim 13, wherein the method further comprises: determine whether sufficient information exists to generate the answer based on the question representation and the context representation; and invoke, responsive to a determination that sufficient information is lacking to generate the answer based on the question representation and the context representation, an interactive process that (i) poses a supplemental question to a user, (ii) obtains feedback for the supplement question from the user, and (iii) generates an answer prediction for the input question based on the feedback. 15. The computer program product of claim 14, wherein the processing element is configured to generate the answer to the input question based on the question representation and the context representation and bypass the interactive process, responsive to a determination that sufficient information exists to generate the answer based on the question representation and the context representation. 16. The computer program product of claim 14, wherein the context vector is dynamically updated responsive to, and using, the answer prediction. 17. The computer program product of claim 13, wherein the method further comprises employing a binary decision process that selectively (i) outputs the answer to the input question based on the question representation and the context representation and bypasses an interactive process, and (ii) invokes the interactive process that uses a supplementary question and user feedback to generate an answer prediction for the input question. 18. The computer program product of claim 13, wherein the method further comprises influencing an attention over each word in the set of purportedly supporting statements using context information extracted from the set of purportedly supporting statements. 19. The computer program product of claim 13, wherein the method further comprises affecting a word semantic meaning contributing to the sentence representation of one or more of the purportedly supporting statements using context information extracted from the set of purportedly supporting statements. 20. A method for implementing a context-aware attention-based neural network for answering an input question given a set of purportedly supporting statements for the input question, the method comprising: calculating, by a processing element, a question representation for the input question, based on word annotations and word-level attentions calculated for the input question; calculating, by the processing element, a sentence representation for each of the purportedly supporting statements, based on word annotations and word-level attentions calculated for each of the purportedly supporting statements; calculating, by the processing element, a context representation for the set of purportedly supporting statements with respect to the sentence representation for each of the purportedly supporting statements; and generating, by the processing element, an answer to the input question based on the question representation and the context representation.