Good Question! Statistical Ranking for Question Generation

By Michael Heilman, Noah A. Smith. 2010

515 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering (2019, background): “Question Generation Early QG studies focused on using rule-based methods to transform statements to questions (Heilman and Smith, 2010; Lindberg et al., 2013; Labutov et al., 2015).”
FEVER: a Large-scale Dataset for Fact Extraction and VERification (2018, background): “A different approach could be to consider a combination of question generation (Heilman and Smith, 2010) followed by question answering such as BiDAF (Seo et al.”
Just Ask: Learning to Answer Questions from Millions of Narrated Videos (2020, neutral): “Table 1 confirms the importance of punctuation and demonstrates the superior performance of our generation method compared to [29].”
Crowdsourcing Multiple Choice Science Questions (2017, background): “Some approaches rely on syntactic transformation templates (Mitkov and Ha, 2003; Heilman and Smith, 2010), while most others generate cloze-style questions.”
Learning to Ask: Neural Question Generation for Reading Comprehension (2017, methodology): “Human evaluations also rated our generated questions as more grammatical, ﬂuent, and challenging (in terms of syntactic divergence from the original reading passage and reasoning needed to answer) than the state-of-the-art Heilman and Smith (2010) system.”
Synthetic QA Corpora Generation with Roundtrip Consistency (2019, background): “Question generation is a well-studied task in its own right (Heilman and Smith, 2010; Du et al., 2017; Du and Cardie, 2018).”
Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks (2018, methodology): “Education is another key application where QG can help with reading comprehension (Heilman and Smith, 2010).”
Harvesting Paragraph-level Question-Answer Pairs from Wikipedia (2018, background): “Compared to models that only take into account sentence-level information (Heilman and Smith, 2010; Du et al., 2017; Zhou et al., 2017), we find that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the cur”
Identifying Well-formed Natural Language Questions (2018, background): “Automatic question generation is the task of generating questions that ask about the information or facts present in either a given sentence or para-graph (Vanderwende, 2008; Heilman and Smith, 2010).”
Unsupervised Question Answering by Cloze Translation (2019, methodology): “While there exist sophisticated rule-based systems (Heilman and Smith, 2010) to transform statements into questions (for English), we ﬁnd their performance to be empirically weak for QA (see Section 3).”
G-DAug: Generative Data Augmentation for Commonsense Reasoning (2020, methodology): “Finally, previous work has also presented techniques to generate questions for reading comprehension (Heilman and Smith, 2010; Rus et al., 2011), online tutoring (Lindberg et al.”
Equity Beyond Bias in Language Technologies for Education (2019, background): “After basic literacy skills are acquired, NLP tools for language understanding are widely used to generate reading comprehension questions (Heilman and Smith, 2010).”
Evaluating Rewards for Question Generation Models (2019, background): “…about a document in natural language is a crucial aspect of the effort to automatically process natural language data, enabling machines to ask clariﬁcation questions (Saeidi et al., 2018), become more robust to queries (Yu et al., 2018), and to act as automatic tutors (Heilman and Smith, 2010).”
Answer-focused and Position-aware Neural Question Generation (2018, background): “(4) Rank the over generated questions by well-designed features (Heilman and Smith, 2009, 2010; Chali and Hasan, 2015).”
Open-Ended Video Question Answering via Multi-Modal Conditional Adversarial Networks (2020, methodology): “Following the state-of-the-art question generation method [26], we generate the question-answer pairs from the video descriptions from the forward two datasets.”
Identifying Where to Focus in Reading Comprehension for Neural Question Generation (2017, background): “In particular, prior work focuses almost exclusively on sentence-level question generation: given a text passage, assume that all sentences contain a question-worthy concept and generate one or more questions for each (Heilman and Smith, 2010; Du et al., 2017; Zhou et al., 2017).”