Abstract: A question answering system includes: a neural network tokenizer module configured to determine a token representation of a question to be answered and token representations of candidate paragraphs of a present reasoning path for the question, respectively; a neural network module configured to: transform the token representation of the question and the token representations of the candidate paragraphs of the present reasoning path into vector representations; and append a first variable to the vector representations to produce a second variable; a search module configured to: select the candidate paragraphs from a knowledge database to extend a present reasoning path based on lexical relevance of the candidate paragraphs to the question; and selectively add ones of the candidate paragraphs to the present reasoning path; and an answer inference network module configured to selectively determine an answer to the question based on multiple different portions of the present reasoning path.

Abstract: A training system includes: a neural network configured to, using trained parameters, generate a first encoding based on an input query and second encodings based on candidate responses for the input query; and a training module configured to: train the trained parameters using hyperparameters; and jointly optimize the hyperparameters using coordinate descent and line searching, the hyperparameters including: a first hyperparameter indicative of a first weight value to apply based on positive interactions of entries of a distance matrix based on encodings; a second hyperparameter indicative of a second weight value to apply based on negative interactions of entries of the distance matrix generated based on the first and second encodings; and a third hyperparameter corresponding to a dimension of the distance matrix generated based on the first and second encodings.

Abstract: A computer implemented method for multi-document question answering is performed on a server communicating with a client device over a network. The method includes receiving a runtime question from a client device and retrieving runtime documents concerning the runtime question using a search engine. Runtime answer samples are identified in the retrieved runtime documents. A neural network model, trained using distant supervision and distance based ranking loss, is used to compute runtime scores from runtime question data representing the runtime question and from a runtime answer sample representing a first portion of text from the corpus of documents, where each runtime score represents a probability an answer to the runtime question is present in the runtime answer samples. A runtime answer is selected from the runtime answer samples corresponding to the highest runtime score sent to the client device.

Abstract: A question answering system includes: a neural network tokenizer module configured to determine a token representation of a question to be answered and token representations of candidate paragraphs of a present reasoning path for the question, respectively; a neural network module configured to: transform the token representation of the question and the token representations of the candidate paragraphs of the present reasoning path into vector representations; and append a first variable to the vector representations to produce a second variable; a search module configured to: select the candidate paragraphs from a knowledge database to extend a present reasoning path based on lexical relevance of the candidate paragraphs to the question; and selectively add ones of the candidate paragraphs to the present reasoning path; and an answer inference network module configured to selectively determine an answer to the question based on multiple different portions of the present reasoning path.

Abstract: A computer implemented method for multi-document question answering is performed on a server communicating with a client device over a network. The method includes receiving a runtime question from a client device and retrieving runtime documents concerning the runtime question using a search engine. Runtime answer samples are identified in the retrieved runtime documents. A neural network model, trained using distant supervision and distance based ranking loss, is used to compute runtime scores from runtime question data representing the runtime question and from a runtime answer sample representing a first portion of text from the corpus of documents, where each runtime score represents a probability an answer to the runtime question is present in the runtime answer samples. A runtime answer is selected from the runtime answer samples corresponding to the highest runtime score sent to the client device.