Socratic Question Generation: A Novel Dataset, Models, and Evaluation
暂无分享,去创建一个
[1] Ming Zhou,et al. A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models , 2022, ACM Comput. Surv..
[2] D. Strunk,et al. Using Socratic Questioning to promote cognitive change and achieve depressive symptom reduction: Evidence of cognitive change as a mediator. , 2022, Behaviour research and therapy.
[3] Weizhe Yuan,et al. BARTScore: Evaluating Generated Text as Text Generation , 2021, NeurIPS.
[4] Mark Hasegawa-Johnson,et al. Worldly Wise (WoW) - Cross-Lingual Knowledge Fusion for Fact-based Visual Spoken-Question Answering , 2021, NAACL.
[5] D. Strunk,et al. Cognitive Bias and Medication Use Moderate the Relation of Socratic Questioning and Symptom Change in Cognitive Behavioral Therapy of Depression , 2021, Cognitive Therapy and Research.
[6] Brian Lester,et al. The Power of Scale for Parameter-Efficient Prompt Tuning , 2021, EMNLP.
[7] L. Azzopardi. Cognitive Biases in Search: A Review and Reflection of Cognitive Biases in Information Retrieval , 2021, CHIIR.
[8] Bill Byrne,et al. TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems , 2020, ACL.
[9] T. Zhao,et al. Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach , 2020, NAACL.
[10] Vishrav Chaudhary,et al. Self-training Improves Pre-training for Natural Language Understanding , 2020, NAACL.
[11] Junyi Jessy Li,et al. Inquisitive Question Generation for High Level Text Comprehension , 2020, EMNLP.
[12] Roberto Basili,et al. GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples , 2020, ACL.
[13] Subhabrata Mukherjee,et al. Uncertainty-aware Self-training for Few-shot Text Classification , 2020, NeurIPS.
[14] Thibault Sellam,et al. BLEURT: Learning Robust Metrics for Text Generation , 2020, ACL.
[15] Yue Zhang,et al. MuTual: A Dataset for Multi-Turn Dialogue Reasoning , 2020, ACL.
[16] Isabelle van der Vegt,et al. Measuring Emotions in the COVID-19 Real World Worry Dataset , 2020, NLPCOVID19.
[17] Jeremy Blackburn,et al. The Pushshift Reddit Dataset , 2020, ICWSM.
[18] Ming Zhou,et al. ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training , 2020, FINDINGS.
[19] Colin Raffel,et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..
[20] Lysandre Debut,et al. HuggingFace's Transformers: State-of-the-art Natural Language Processing , 2019, ArXiv.
[21] Iryna Gurevych,et al. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks , 2019, EMNLP.
[22] Benjamin Piwowarski,et al. Self-Attention Architectures for Answer-Agnostic Neural Question Generation , 2019, ACL.
[23] Nian-Shing Chen,et al. Using Socratic Questioning Strategy to Enhance Critical Thinking Skill of Elementary School Students , 2019, 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT).
[24] Tat-Seng Chua,et al. Recent Advances in Neural Question Generation , 2019, ArXiv.
[25] Kilian Q. Weinberger,et al. BERTScore: Evaluating Text Generation with BERT , 2019, ICLR.
[26] Pamela R. Cangelosi,et al. Putting Socrates back in Socratic method: Theory-based debriefing in the nursing classroom. , 2019, Nursing philosophy : an international journal for healthcare professionals.
[27] Noah A. Smith,et al. To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks , 2019, RepL4NLP@ACL.
[28] Yoshua Bengio,et al. HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering , 2018, EMNLP.
[29] Alan W. Black,et al. A Dataset for Document Grounded Conversations , 2018, EMNLP.
[30] B. Inkster,et al. An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study , 2018, JMIR mHealth and uHealth.
[31] Jonathan Sims,et al. Alleviating the Plunging-In Bias, Elevating Strategic Problem-Solving , 2018, Academy of Management Learning & Education.
[32] Mitesh M. Khapra,et al. Towards a Better Metric for Evaluating Question Generation Systems , 2018, EMNLP.
[33] Danqi Chen,et al. CoQA: A Conversational Question Answering Challenge , 2018, TACL.
[34] Eunsol Choi,et al. QuAC: Question Answering in Context , 2018, EMNLP.
[35] Hal Daumé,et al. Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information , 2018, ACL.
[36] Frank Hutter,et al. Decoupled Weight Decay Regularization , 2017, ICLR.
[37] Jonathan Berant,et al. Evaluating Semantic Parsing against a Simple Web-based Question Answering Model , 2017, *SEMEVAL.
[38] Muhammad Abdul-Mageed,et al. EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks , 2017, ACL.
[39] K. Fitzpatrick,et al. Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial , 2017, JMIR mental health.
[40] Ankur Taly,et al. Axiomatic Attribution for Deep Networks , 2017, ICML.
[41] Philip Bachman,et al. NewsQA: A Machine Comprehension Dataset , 2016, Rep4NLP@ACL.
[42] Jian Zhang,et al. SQuAD: 100,000+ Questions for Machine Comprehension of Text , 2016, EMNLP.
[43] Kevin Crowston,et al. Amazon Mechanical Turk: A Research Tool for Organizations and Information Systems Scholars , 2012, Shaping the Future of ICT Research.
[44] Andrew McCallum,et al. Optimizing Semantic Coherence in Topic Models , 2011, EMNLP.
[45] Cristian Danescu-Niculescu-Mizil,et al. Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs , 2011, CMCL@ACL.
[46] M. Neenan. Using Socratic Questioning in Coaching , 2009 .
[47] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[48] Alon Lavie,et al. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments , 2005, IEEvaluation@ACL.
[49] Chin-Yew Lin,et al. ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.
[50] Gustavo E. A. P. A. Batista,et al. A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.
[51] D. Krathwohl. A Revision of Bloom's Taxonomy: An Overview , 2002 .
[52] Dan Roth,et al. Learning Question Classifiers , 2002, COLING.
[53] Salim Roukos,et al. Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.
[54] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[55] A. Tversky,et al. Judgment under Uncertainty: Heuristics and Biases , 1974, Science.
[56] Jacob Cohen. Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.
[57] P. Pu,et al. A Taxonomy of Empathetic Questions in Social Dialogs , 2022, ACL.
[58] Joakim Nivre,et al. Fine-Grained Controllable Text Generation Using Non-Residual Prompting , 2022, ACL.
[59] Chao-Yi Lu,et al. A Survey of Approaches to Automatic Question Generation:from 2019 to Early 2021 , 2021, ROCLING.
[60] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[61] Ilya Sutskever,et al. Language Models are Unsupervised Multitask Learners , 2019 .
[62] Yao-Chung Fan,et al. A Recurrent BERT-based Model for Question Generation , 2019, EMNLP.
[63] Lowell B Bautista,et al. The Socratic Method as a Pedagogical Method in Legal Education , 2014 .
[64] A. Fisher,et al. Critical Thinking : What Every Person Needs to Survive in a Rapidly Changing World , 2007 .
[65] J. R. Mosig,et al. Are They Related , 2006 .
[66] P. Delin,et al. What is an assumption , 1995 .
[67] J. Fleiss. Measuring nominal scale agreement among many raters. , 1971 .