Question Generation based on Lexico-Syntactic Patterns Learned from the Web

THE MENTOR automatically generates multiple-choice tests from a given text. This tool aims at supporting the dialogue system of the FalaComigo project, as one of FalaComigo's goals is the interaction with tourists through questions/answers and quizzes about their visit. In a minimally supervised learning process and by leveraging the redundancy and linguistic variability of the Web, THE MENTOR learns lexico-syntactic patterns using a set of question/answer seeds. Afterward, these patterns are used to match the sentences from which new questions (and answers) can be generated. Finally, several ï¬lters are applied in order to discard low quality items. In this paper we detail the question generation task as performed by T- Mand evaluate its performance

Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

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.Comment: 13 pages, 1 figure, 7 table