Hindi question answering systems suffer from a lack of data. To address the same, this paper presents an approach towards automatic question generation. We present a rule-based system for question generation in Hindi by formalizing question transformation methods based on karaka-dependency theory. We use a Hindi dependency parser to mark the karaka roles and use IndoWordNet a Hindi ontology to detect the semantic category of the karaka role heads to generate the interrogatives. We analyze how one sentence can have multiple generations from the same karaka role's rule. The generations are manually annotated by multiple annotators on a semantic and syntactic scale for evaluation. Further, we constrain our generation with the help of various semantic and syntactic filters so as to improve the generation quality. Using these methods, we are able to generate diverse questions, significantly more than number of sentences fed to the system.
The question-answering system developed by this research matches one-sentence-long user questions to a number of question templates that cover the conceptual model of the database and describe the concepts, their attributes, and the relationships in form of natural language questions. A question template resembles a frequently asked question (FAQ). Unlike a static FAQ, however, a question template may contain entity slots that are replaced by data instances from the underlying database. During the question-answering process, the system retrieves relevant data instances and question templates, and offers one or several interpretations of the original question. The user selects an interpretation to be answered. Self-questioning is an important reading comprehension strategy, so it would be useful for an intelligent tutor to help students apply it to any given text. Our goal is to help children generate questions that make them think about the text in ways that improve their comprehension and retention. However, teaching and scaffolding self-questioning involve analyzing both the text and the students' responses. This requirement poses a tricky challenge to generating such instruction automatically, especially for children too young to respond by typing. This paper describes how to generate self-questioning instruction for an automated reading tutor. Following expert pedagogy, we decompose strategy instruction into describing, modeling, scaffolding, and prompting the strategy. We present a working example to illustrate how we generate each of these four phases of instruction for a given text. We identify some relevant criteria and use them to evaluate the generated instruction on a corpus of 513 children's stories. Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scale question-answer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the question-generation model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions. 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.