Question Answering Corpora Sample Clauses

Question Answering Corpora. ‌ With the current growth of question answering, several corpora have been published and can be used to evaluate the robustness of question answering systems. Corpus generation has been pioneered by the QASent dataset that consists of 277 questions. It has been widely used for benchmarking the answer sentence selection task [149]. While the corpus has been popular, its extremely small scale makes it impossible to be used in advanced neural ar- chitectures. Over the past few years, several more corpora have been released focusing on different aspects of the text-based question answering tasks. In- suranceQA is an automatically generated dataset containing 16K+ ques- tions selected from the insurance library2 [50]. 2xxxx://xxx.xxxxxxxxxxxxxxxx.xxx/ WikiQA is a dataset com- prising almost two thousand cleaned Bing3 search engine queries [161]. These queries have been manually associated with their corresponding Wikipedia pages’ abstracts. Xxxxxxx et al. [93] extracted information boxes from var- ious Wikipedia pages and released the InfoboxQA dataset that consists of over 15K questions with the associated infoboxes. More recently, the SQuAD dataset has been published [106]. It is a massive corpus of over 100K questions designed for the answer extraction task. Majority of these corpora have been created using crowdsourcing methods thus are relevant to this dissertation. Unfortunately, creating a diverse and realistic dataset is still a difficult task for researchers. In this thesis, a new multi-stage anno- tation scheme is presented that addresses several challenges when building a corpus for open-domain question answering.
Sample 1
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