Besting the Quiz Master: Crowdsourcing Incremental Classification Games

By Jordan L. Boyd-Graber, Brianna Satinoff, He He. 2012

74 citations recorded by the source corpus.

Subfields: Natural Language Processing, Question Answering & Reading Comprehension

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Mohit Bansal
2. Jianfeng Gao
3. Jason Aaron Edward Weston
4. Christopher D. Manning
5. Yoav Goldberg
6. Richard Socher
7. Douwe Kiela
8. Yejin Choi

In Question Answering & Reading Comprehension

1. Kellie Webster
2. Maharshi Gor
3. Mohit Iyyer
4. Pedro Rodriguez
5. Jordan L. Boyd-Graber
6. Hal Daumé Iii
7. Richard Socher
8. Sebastian Riedel

Citations in the corpus

• A Neural Network for Factoid Question Answering over Paragraphs (2014, methodology): “This simple discriminative model is an improvement over the generative quiz bowl answering model of Boyd-Graber et al. (2012).”
• Improving alignment of dialogue agents via targeted human judgements (2022, methodology): “, 2019), Quiz Bowl (Boyd-Graber et al., 2012) and TriviaQA (Joshi et al.”
• Constructing Datasets for Multi-hop Reading Comprehension Across Documents (2017, background): “, 2017), and trivia (Boyd-Graber et al., 2012; Dunn et al., 2017).”
• The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems (2015, background): “Several datasets of question-answer pairs are available [3], however these interactions are much shorter than what we seek to study.”
• Pathologies of Neural Models Make Interpretations Difficult (2018, methodology): “We also apply input reduction to single-input tasks including sentiment analysis (Maas et al., 2011) and Quizbowl (Boyd-Graber et al., 2012), achieving similar results.”
• A dataset and baselines for sequential open-domain question answering (2018, unknown): “Unlike previous work that only uses the starter (or tossup) questions (Boyd-Graber et al., 2012), bonus questions are not interruptable (players always hear the complete question) and have greater variability in difficulty.”
• Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy (2021, methodology): “We analyze four QA tasks: NQ,1 SQuAD (Rajpurkar et al., 2016), QB (Boyd-Graber et al., 2012) and TriviaQA (Joshi et al., 2017).”
• What Question Answering can Learn from Trivia Nerds (2019, background): “While this is true in many contexts, examples are rife in format called QB (Boyd-Graber et al., 2012), whose very long questions 6 showcase trivia writers’ tactics.”