Question Generation is the task of automatically creating questions from textual input. In this work we present a new Attentional Encoder--Decoder Recurrent Neural Network model for automatic question generation. Our model incorporates linguistic features and an additional sentence embedding to capture meaning at both sentence and word levels. The linguistic features are designed to capture information related to named entity recognition, word case, and entity coreference resolution. In addition our model uses a copying mechanism and a special answer signal that enables generation of numerous diverse questions on a given sentence. Our model achieves state of the art results of 19.98 Bleu_4 on a benchmark Question Generation dataset, outperforming all previously published results by a significant margin. A human evaluation also shows that these added features improve the quality of the generated questions.
Automatic question generation has the potential to be useful in the areas of automatic Question Answering (QA) and Machine Comprehension of text. Recently, large datasets such as SQuAD @cite_10 , and MS MARCO @cite_11 have facilitated advances in both areas. These datasets are expensive to create and consist of human authored (document, question, answer) triples with questions and answers either being collected from the web or created by crowd workers. Automatic Question Generation methods can be used to cheaply supplement resources available to QA models, further assisting in advancing QA capabilities. Indeed, have recently shown that a joint QA-QG model is able to achieve state-of-the art results on a variety of different QA related tasks.
@cite_10 @cite_11
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Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage. Traditional methods mainly use rigid heuristic rules to transform a sentence into related questions. In this work, we propose to apply the neural encoder-decoder model to generate meaningful and diverse questions from natural language sentences. The encoder reads the input text and the answer position, to produce an answer-aware input representation, which is fed to the decoder to generate an answer focused question. We conduct a preliminary study on neural question generation from text with the SQuAD dataset, and the experiment results show that our method can produce fluent and diverse questions. In this paper, we present a novel approach to machine reading comprehension for the MS-MARCO dataset. Unlike the SQuAD dataset that aims to answer a question with exact text spans in a passage, the MS-MARCO dataset defines the task as answering a question from multiple passages and the words in the answer are not necessary in the passages. We therefore develop an extraction-then-synthesis framework to synthesize answers from extraction results. Specifically, the answer extraction model is first employed to predict the most important sub-spans from the passage as evidence, and the answer synthesis model takes the evidence as additional features along with the question and passage to further elaborate the final answers. We build the answer extraction model with state-of-the-art neural networks for single passage reading comprehension, and propose an additional task of passage ranking to help answer extraction in multiple passages. The answer synthesis model is based on the sequence-to-sequence neural networks with extracted evidences as features. Experiments show that our extraction-then-synthesis method outperforms state-of-the-art methods. Asking intelligent and relevant questions is an important capability of conversational systems such as chatbots. Neural network-based approaches represent the state-of-the-art in automatic question generation (QG). In this work, we attempt to strengthen them significantly by adopting a holistic and novel generator-evaluator framework that directly optimizes objectives that reward semantics and structure. In this paper, we present a novel deep reinforcement learning based framework for automatic question generation. The generator of the framework is a sequence-to-sequence model, whereas the evaluator model of the framework evaluates and assigns a reward to each predicted question. The overall model is trained by learning the parameters of the generator network which maximizes the reward.Our framework allows us to directly optimize any task-specific score including evaluation measures such as BLEU, GLEU, ROUGE-L,etc., suitable for sequence to sequence tasks such as QG. Our evaluation shows that our approach significantly outperforms state-of-the-art systems on the widely-used SQuAD benchmark in both automatic and human evaluation. Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. Existing datasets either focus exclusively on answerable questions, or use automatically generated unanswerable questions that are easy to identify. To address these weaknesses, we present SQuAD 2.0, the latest version of the Stanford Question Answering Dataset (SQuAD). SQuAD 2.0 combines existing SQuAD data with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD 2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQuAD 2.0 is a challenging natural language understanding task for existing models: a strong neural system that gets 86 F1 on SQuAD 1.1 achieves only 66 F1 on SQuAD 2.0.