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

By Iulian Serban, Alberto García-Durán, Caglar Gulcehre, Sungjin Ahn, A. P. Sarath Chandar, Aaron C. Courville, Yoshua Bengio. 2016

300 citations recorded by the source 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering (2019, background): “, 2018), Knowledge-Base-based QG (Reddy et al., 2017; Serban et al., 2016), and Image-based QG (Li et al.”
Towards a Better Metric for Evaluating Question Generation Systems (2018, background): “…problem and create even more training data, there is growing interest in developing techniques that can automatically generate questions from a given source, say a document (Du et al., 2017; Du and Cardie, 2017), knowledge base (Reddy et al., 2017; Serban et al., 2016), or image (Li et al., 2017) .”
Visual Dialog (2016, methodology): “Some recent large-scale datasets in this domain include the 30M Factoid Question-Answer corpus [44], 100K SimpleQuestions dataset [6], DeepMind Q&A dataset [15], the 20 artificial tasks in the bAbI dataset [57], and the SQuAD dataset for reading comprehension [38].”
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks (2019, background): “However, obtaining additional, even unlabeled samples for a speciﬁc task can be difﬁcult in NLP. Traditional data augmentation in NLP is typically task-speciﬁc (Wang and Eisner, 2016; Serban et al., 2016) and difﬁcult to extend to other NLP tasks.”
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them (2021, background): “Serban et al. (2016) generate large numbers of questions from Freebase, but do not address how to use them for QA.”
Learning to Ask: Neural Question Generation for Reading Comprehension (2017, background): “Serban et al. (2016) propose generating simple factoid questions from logic triple (subject, relation, object).”
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering (2016, methodology): “, 2016) and the 30M Factoid Question-Answer Corpus (Serban et al., 2016) are artificially synthesized; the TREC datasets (Harman and Voorhees, 2006), Free917 (Cai and Yates, 2013) and WebQuestions (Berant et al.”
Harvesting Paragraph-level Question-Answer Pairs from Wikipedia (2018, background): “Closest to our work is that of Serban et al. (2016).”
Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph (2018, methodology): “…aim is to answer a question from a collection of documents (ii) factoid QA over structured knowledge graphs (Berant et al. 2013; Bordes et al. 2015; Serban et al. 2016b) (iii) reading comprehension style QA (Rajpurkar et al. 2016; Nguyen et al. 2016), (iv) cloze style QA (Mostafazadeh et al. 2016;…”
Cycle-Consistency for Robust Visual Question Answering (2019, background): “Question Generation (QG) as a task has been studied extensively by [3, 20, 37, 43] in NLP. Generating questions conditioned on an image was introduced in [32] and a large-scale VQG dataset was collected by [33] generation capabilities of models.”
G-DAug: Generative Data Augmentation for Commonsense Reasoning (2020, background): “(Serban et al., 2016) and visual question generation (Mostafazadeh et al.”
A Systematic Review of Automatic Question Generation for Educational Purposes (2019, methodology): “• Training question-answering (QA) systems – The 30M factoid question-answer corpus (Serban et al. 2016) is a corpus of questions automatically generated from Freebase.”
Teaching Machines to Ask Questions (2018, methodology): “As the reverse task of question answering, question generation also has the potential for generating large-scale question answer pair corpus[Serban et al. , 2016].”
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs (2017, background): “In recent years KGs have been playing an increasingly crucial role in fields such as question answering [Das et al., 2017], language modeling [Ahn et al., 2016], and text generation [Serban et al., 2016].”
Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text (2018, background): “A recent deep learning approach to question generation (Serban et al., 2016) investigates a simpler task of generating questions only from a triplet of subject, relation and object.”
Learning to Collaborate for Question Answering and Asking (2018, background): “There are several works on generating questions from different resources, including a sentence(Heilman, 2011), a topic (Chali and Hasan, 2015), a fact from knowledge base (Serban et al., 2016), etc.”
Machine Comprehension by Text-to-Text Neural Question Generation (2017, background): “…(Heilman and Smith, 2010; Ali et al., 2010; Lindberg et al., 2013; Labutov et al., 2015; Mazidi and Nielsen, 2015), to reroute queries to community question-answering systems (Zhao et al., 2011), and to enrich training data for question-answering systems (Serban et al., 2016; Yang et al., 2017).”