TECHNICAL FIELD

Embodiments described herein generally relate to systems and methods for generating questions and, more particularly but not exclusively, to systems and methods for generating questions from free text using deep learning networks.

BACKGROUND

Search engines cannot always satisfy an end user's desire to have more direct access to relevant documents or information. Question answering (QA) systems have attempted to improve this experience by directly retrieving relevant answers to natural language questions.

However, one of the main requirements of a QA system is that it must receive a concise and well-described question as an input to generate the best possible answer as an output. Prior studies have revealed that humans do not always ask succinct questions on a specific topic of interest. For example, variations in expressive abilities among users may impact the ability of QA systems to “understand” the input queries and therefore the QA system may not truly understand the information that a user is seeking. Accordingly, the performance of the QA system may be adversely affected.

Using existing search engines is therefore time consuming and often unsatisfying as they are unable to handle complex queries well. Accordingly, accurate answers may not be returned. While existing QA systems have addressed some of these limitations, they generally do not perform well with questions that are related to many topics of interest and that may be unrelated.

A need exists, therefore, for question generation systems and methods that overcome the disadvantages of existing techniques.

SUMMARY

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description section. This summary is not intended to identify or exclude key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

In one aspect, embodiments relate to a method of generating a question from text. The method includes receiving textual content using an interface, receiving a factual statement associated with the textual content using the interface, and generating, using a processor executing instructions stored on a memory to provide a question generator module, a question from the textual content relating to the factual statement.

In some embodiments, the method further includes receiving a question type related to the textual content using the interface.

In some embodiments, the factual statement consists of a plurality of words, and the method further comprises mapping, using a processor executing instructions stored on a memory to provide a fact embedder module, a sequence of the plurality of words to word embeddings. In some embodiments, the method further includes processing the word embeddings of the fact embedder module using a first gated recurrent unit to result in a set of computed weights. In some embodiments, the method further includes providing the received textual content to a second bidirectional fact-based gated recurrent unit whose weighting is determined by the set of computed weights. In some embodiments, the method further includes providing output of the second bidirectional fact-based gated recurrent unit to at least one attention generator, each attention generator computing normalized weights for all sequences of the second gated recurrent unit. In some embodiments, the method further includes using the computed normalized weights and a third unidirectional fact-based gated recurrent unit to generate a plurality of words forming the question. In some embodiments, the at least one attention generator utilizes the set of computed weights in determining the normalized weights. In some embodiments, the second bidirectional fact-based gated recurrent unit comprises a convolutional neural network feeding forward into a plurality of recurrent neural networks.

According to yet another aspect, embodiments relate to a system for generating a question from text. The system includes an interface for receiving textual content and a factual statement associated with the textual content, and a processor executing instructions stored on a memory to provide a question generator module configured to generate a question from the textual content relating to the factual statement.

In some embodiments, the interface is further configured to receive a question type related to the textual content.

In some embodiments, the factual statement consists of a plurality of words, and the system further includes a processor executing instructions stored on a memory to provide a fact embedder module configured to map a sequence of the plurality of words to word embeddings. In some embodiments, the system further includes a first gated recurrent unit configured to process the word embeddings into a set of computed weights. In some embodiments, the system further includes a second bidirectional fact-based gated recurrent unit configured to receive the textual content whose weighting is determined by the set of computed weights. In some embodiments, the system further includes at least one attention generator configured to compute normalized weights for sequences outputted by the second bidirectional fact-based gated recurrent unit. In some embodiments, the system further includes a third unidirectional fact-based gated recurrent unit configured to generate a plurality of words forming the question using the normalized weights. In some embodiments, the at least one attention generator utilizes the set of computed weights in computing the normalized weights. In some embodiments, the second bidirectional fact-based gated recurrent unit comprises a convolutional neural network feeding forward into a plurality of recurrent neural networks.

In some embodiments, the question generator module receives input that is a concatenation of the factual statement and a paragraph. In some embodiments, the system further includes an attention generator that considers the factual statement and previous hidden states of the question generator module. In some embodiments, the question generator module includes an encoder and a decoder, and the question generator module uses a single representation of the factual statement for the encoder and the decoder.

In some embodiments, the question generator module includes an encoder and a decoder, and an input to the decoder is the element-wise product of an encoder output and an extracted fact representation from the encoder.

According to yet another aspect, embodiments relate to a computer readable medium containing computer-executable instructions for performing a method of generating a question from text. The medium includes computer-executable instructions for receiving textual content using an interface; computer-executable instructions for receiving a factual statement associated with the textual content using the interface, and computer-executable instructions for generating, using a processor executing instructions stored on a memory to provide a question generator module, a question from the textual content relating to the factual statement.

