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 paper introduces a dependency annotation effort which aims to fully annotate a million word Hindi corpus. It is the first attempt of its kind to develop a large scale tree-bank for an Indian language. In this paper we provide the motivation for following the Paninian framework as the annotation scheme and argue that the Paninian framework is better suited to model the various linguistic phenomena manifest in Indian languages. We present the basic annotation scheme. We also show how the scheme handles some phenomenon such as complex verbs, ellipses, etc. Empirical results of some experiments done on the currently annotated sentences are also reported.