MTL-QA : A dataset and multi-task learning approach for knowledge graph and natural language question answering

The dataset used for this project is created by enhancing the publicly available MetaQA (Movie Text Audio QA), which is primarily a KGQA dataset pertaining to movies, an extension of WikiMovies. This involves questions requiring 1, 2, and 3 hops which can be answered by using a MetaQA Knowledge Graph. The questions are available in text and audio format. The text has vanilla (original) and its paraphrased version, and is called ntm. 

In order to develop a dataset to support NLQA, a series of dataset augmentation steps has been performed.

The dataset consists of natural language questions and a tagged topic entity as ground truth. This topic entity is used to retrieve textual information related to the question from Wikipedia. The introduction section of the entity's page is used as the context that is required for NLQA. Hence, this dataset has information related to both KGQA and NLQA. Certain preliminary checks and validations are done to only retain those data samples whose context can be used to answer a given question.