DAPIE: Interactive Step-by-Step Explanatory Dialogues to Answer Children’s Why and How Questions
Open accessAbstract
Children acquire an understanding of the world by asking “why” and “how” questions. Conversational agents (CAs) like smart speakers or voice assistants can be promising respondents to children’s questions as they are more readily available than parents or teachers. However, CAs’ answers to “why” and “how” questions are not designed for children, as they can be difficult to understand and provide little interactivity to engage the child. In this work, we propose design guidelines for creating interactive dialogues that promote children’s engagement and help them understand explanations. Applying these guidelines, we propose DAPIE, a system that answers children’s questions through interactive dialogue by employing an AI-based pipeline that automatically transforms existing long-form answers from online sources into such dialogues. A user study (N=16) showed that, with DAPIE, children performed better in an immediate understanding assessment while also reporting higher enjoyment than when explanations were presented sentence-by-sentence.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Materials occupy an essential presence in expressive arts therapy as they afford different expressive properties that can evoke specific experiences during art-creating.
“Materials occupy an essential presence in expressive arts therapy as they afford different expressive properties that can evoke specific experiences during the act of art-creating [56, 92].”
From "When He Feels Cold, He Goes to the Seahorse"—Blending Generative AI into Multimaterial Storymaking for Family Expressive Arts Therapy (2024) · cites this paper as [56]
In family members' art-making, therapists can provide a comfortable and safe space where parents and children utilize physical expressive materials as languages for social, physical, and spiritual wellness.
“In the process of family members' art-making, therapists can provide a comfortable and safe space where parents and children utilize physical expressive materials as languages for social, physical, and spiritual wellness [56];”
From "When He Feels Cold, He Goes to the Seahorse"—Blending Generative AI into Multimaterial Storymaking for Family Expressive Arts Therapy (2024) · cites this paper as [56]
Material interaction plays a pivotal role in expressive arts therapy, facilitating self-expression and therapeutic healing via creative expression.
“Material interaction plays a pivotal role in expressive arts therapy, facilitating self-expression and therapeutic healing via creative expression [38, 56].”
From "When He Feels Cold, He Goes to the Seahorse"—Blending Generative AI into Multimaterial Storymaking for Family Expressive Arts Therapy (2024) · cites this paper as [56]
Material interactions play a pivotal role in the therapeutic outcomes and the overall experience of the clients.
“Material interactions play a pivotal role in the therapeutic outcomes and the overall experience of the clients [56].”
From "When He Feels Cold, He Goes to the Seahorse"—Blending Generative AI into Multimaterial Storymaking for Family Expressive Arts Therapy (2024) · cites this paper as [56]
Therapists value the importance of affording a safe space where participants feel comfortable while engaging in the creative process and expressing their thoughts.
“therapists value the importance of affording a safe space where participants feel comfortable while engaging in the creative process and expressing their thoughts [56].”
From "When He Feels Cold, He Goes to the Seahorse"—Blending Generative AI into Multimaterial Storymaking for Family Expressive Arts Therapy (2024) · cites this paper as [56]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
(1978) · Medical Entomology and Zoology · Cited by 53,488 · see citing works
Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · see citing works
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208 · see citing works
Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575 · see citing works
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.11401 · Cited by 3,101 · see citing works
The ICAP framework: Linking cognitive engagement to active learning outcomes
Chi, M. T. H., & Wylie, R. (2014) · Educational Psychologist · doi:10.1080/00461520.2014.965823 · Cited by 2,911 · see citing works
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · see citing works
Self-explanations: How students study and use examples in learning to solve problems
Chi, M. (1989) · Cognitive Science · doi:10.1016/0364-0213(89)90002-5 · Cited by 2,287 · see citing works
Natural Questions: A benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00276 · Cited by 2,069 · see citing works
The Acquisition and Retention of Knowledge: A Cognitive View
Ausubel, D. P. (2000) · Springer eBooks · doi:10.1007/978-94-015-9454-7 · Cited by 1,341 · see citing works
On Faithfulness and Factuality in Abstractive Summarization
Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.173 · Cited by 1,185 · see citing works
CoQA: A Conversational Question Answering Challenge
Reddy, S., Chen, D., & Manning, C. D. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00266 · Cited by 975 · see citing works
QuAC: Question Answering in Context
Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-T., Choi, Y., Liang, P., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1241 · Cited by 714 · cited 3× · see citing works
“For questioning, we trained a dialogue inpainting model following Dai et al. [16] using four datasets: QuAC [12], QReCC [1], DailyDialog [55], and Taskmaster [6].”
MethodChildren's Understanding: The Development of Mental Models
Halford, G. S. (1993) · Cited by 647 · see citing works
Roscoe, R. D., & Chi, M. T. H. (2007) · Review of Educational Research · doi:10.3102/0034654307309920 · Cited by 647 · see citing works
The development of induction within natural kind and artifact categories
Gelman, S. A. (1988) · Cognitive Psychology · doi:10.1016/0010-0285(88)90025-4 · Cited by 551 · see citing works
Trusting what you're told: how children learn from others
Stawiarska, K. (2012) · Choice Reviews Online · doi:10.5860/choice.50-2197 · Cited by 482 · see citing works
Russ, R. S., Scherr, R. E., Hammer, D., et al. (2008) · Science Education · doi:10.1002/sce.20264 · Cited by 424 · see citing works
Analogical Reasoning in Children
Goswami, U. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/1251.003.0018 · Cited by 345 · see citing works
"Hey Google is it OK if I eat you?": Initial Explorations in Child-Agent Interaction
Druga, S., Williams, R., Breazeal, C., & Resnick, M. (2017) · Proceedings of the 2017 Conference on Interaction Design and Children · doi:10.1145/3078072.3084330 · Cited by 330 · see citing works
Preschoolers' Search for Explanatory Information Within Adult-Child Conversation
Frazier, B. N., Gelman, S., & Wellman, H. (2009) · Child Development · doi:10.1111/j.1467-8624.2009.01356.x · Cited by 329 · see citing works
"Hey Alexa, What's Up?": A Mixed-Methods Studies of In-Home Conversational Agent Usage
Sciuto, A., Saini, A., Forlizzi, J., & Hong, J. I. (2018) · Proceedings of the 2018 Designing Interactive Systems Conference · doi:10.1145/3196709.3196772 · Cited by 327 · see citing works
Cabell, S. Q., Justice, L. M., McGinty, A. S., DeCoster, J., & Forston, L. D. (2014) · Early Childhood Research Quarterly · doi:10.1016/j.ecresq.2014.09.004 · Cited by 290 · see citing works
Scaffolding: A powerful tool in social constructivist classrooms.
Roehler, L. R., & Cantlon, D. (1997) · Cited by 189 · see citing works
Seeing the Complexity of Standing to the Side: Instructional Dialogues
Leinhardt, G., & Steele, M. D. (2005) · Cognition and Instruction · doi:10.1207/s1532690xci2301_4 · Cited by 189 · see citing works
Young Children's Spontaneous Personification as Analogy
Inagaki, K., & Hatano, G. (1987) · Child Development · doi:10.2307/1130542 · Cited by 183 · see citing works
Child Speech Recognition in Human-Robot Interaction
Kennedy, J., Lemaignan, S., Montassier, C., Lavalade, P., Irfan, B., Papadopoulos, F., Senft, E., & Belpaeme, T. (2017) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/2909824.3020229 · Cited by 178 · see citing works
Winkler, R., Hobert, S., Salovaara, A., Söllner, M., & Leimeister, J. M. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376781 · Cited by 177 · see citing works
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
Byrne, B., Krishnamoorthi, K., Sankar, C., Neelakantan, A., Goodrich, B., Duckworth, D., Yavuz, S., Dubey, A., Kim, K.-Y., & Cedilnik, A. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1459 · Cited by 174 · see citing works
Selective effects of explanation on learning during early childhood
Legare, C. H., & Lombrozo, T. (2014) · Journal of Experimental Child Psychology · doi:10.1016/j.jecp.2014.03.001 · Cited by 173 · see citing works
Identifying Essential Epistemic Heuristics for Guiding Mechanistic Reasoning in Science Learning
Krist, C., Schwarz, C. V., & Reiser, B. J. (2018) · Journal of the Learning Sciences · doi:10.1080/10508406.2018.1510404 · Cited by 156 · see citing works
Ruan, S., Jiang, L., Xu, J., Tham, B. J.-K., Qiu, Z., Zhu, Y., Murnane, E. L., Brunskill, E., & Landay, J. A. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300587 · Cited by 156 · see citing works
Whitehead, M. R. (2014) · Early Years Journal of International Research and Development · doi:10.1080/09575146.2014.906093 · Cited by 155 · see citing works
“Why Does Rain Fall?”: Children Prefer to Learn From an Informant Who Uses Noncircular Explanations
Corriveau, K. H., & Kurkul, K. E. (2014) · Child Development · doi:10.1111/cdev.12240 · Cited by 148 · see citing works
A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
Dasigi, P., Lo, K., Beltagy, I., Cohan, A., Smith, N. A., & Gardner, M. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.365 · Cited by 138 · see citing works
Hey Google, Do Unicorns Exist?
Lovato, S. B., Piper, A. M., & Wartella, E. A. (2019) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3311927.3323150 · Cited by 137 · see citing works
Open-Domain Question Answering Goes Conversational via Question Rewriting
Anantha, R., Vakulenko, S., Tu, Z., Longpre, S., Pulman, S., & Chappidi, S. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.44 · Cited by 130 · see citing works
Text simplification for children
Belder, J. D., & Moens, M. (2010) · Lirias · Cited by 126 · see citing works
Young Children Prefer and Remember Satisfying Explanations
Frazier, B. N., Gelman, S., & Wellman, H. (2016) · Journal of Cognition and Development · doi:10.1080/15248372.2015.1098649 · Cited by 113 · see citing works
Parent-Child Interaction in Three Conversational Contexts: Variations in Style and Strategy
Crain-Thoreson, C., Dahlin, M. P., & Powell, T. A. (2001) · New Directions for Child and Adolescent Development · doi:10.1002/cd.13 · Cited by 110 · see citing works
The Contributions of Explanation and Exploration to Children's Scientific Reasoning
Legare, C. H. (2014) · Child Development Perspectives · doi:10.1111/cdep.12070 · Cited by 108 · see citing works
Building Islands of Expertise in Everyday Family Activity
Crowley, K., & Jacobs, M. (2003) · Routledge eBooks · doi:10.4324/9781410606624-17 · Cited by 106 · see citing works
Levine, D., Pace, A. E., Luo, R., Hirsh‐Pasek, K., Golinkoff, R. M., de Villiers, J., Iglesias, A., & Wilson, M. S. (2018) · Early Childhood Research Quarterly · doi:10.1016/j.ecresq.2018.11.006 · Cited by 89 · see citing works
Kelemen, D. A. (2019) · Perspectives on Psychological Science · doi:10.1177/1745691619827011 · Cited by 87 · see citing works
Exploring young children's engagement in joint reading with a conversational agent
Xu, Y., & Warschauer, M. (2020) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3392063.3394417 · Cited by 82 · see citing works
The Last Decade of HCI Research on Children and Voice-based Conversational Agents
Garg, R., Cui, H., Seligson, S., Zhang, B., Porcheron, M., Clark, L., Cowan, B. R., & Beneteau, E. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502016 · Cited by 75 · see citing works
Why doesn't it work?: voice-driven interfaces and young children's communication repair strategies
Cheng, Y., Yen, K., Chen, Y., Chen, S., & Hiniker, A. (2018) · Proceedings of the 17th ACM Conference on Interaction Design and Children · doi:10.1145/3202185.3202749 · Cited by 69 · see citing works
Design Patterns for an Interactive Storytelling Robot to Support Children’s Engagement and Agency
Ligthart, M., Neerincx, M. A., & Hindriks, K. (2020) · IEEE/ACM International Conference on Human-Robot Interaction · doi:10.1145/3319502.3374826 · Cited by 67 · see citing works
Towards a framework for effective instructional explanations in science teaching
Kulgemeyer, C. (2018) · Studies in Science Education · doi:10.1080/03057267.2018.1598054 · Cited by 61 · see citing works
Similarity Comparisons and Relational Analogies in Parent-Child Conversations About Science Topics
Valle, A., & Callanan, M. A. (2006) · Merrill-palmer Quarterly · doi:10.1353/mpq.2006.0009 · Cited by 57 · see citing works
Conceptualizing instructional explanation
Duffy, G. G., Roehler, L. R., Meloth, M. S., & Vavrus, L. G. (1986) · Teaching and Teacher Education · doi:10.1016/s0742-051x(86)80002-6 · Cited by 52 · see citing works
Hu, J., Gordon, C., Yang, N., et al. (2020) · Early Education and Development · doi:10.1080/10409289.2020.1759011 · Cited by 50 · see citing works
Parent Scaffolding in Children's Oral Reading
Evans, M. A., Moretti, S., Shaw, D., & Fox, M. (2003) · Early Education and Development · doi:10.1207/s15566935eed1403_5 · Cited by 44 · see citing works
“I Want to Know More!”: Children Are Sensitive to Explanation Quality When Exploring New Information
Mills, C. M., Sands, K. R., Rowles, S. P., & Chandler-Campbell, I. L. (2019) · Cognitive Science · doi:10.1111/cogs.12706 · Cited by 43 · see citing works
Xu, Y., Vigil, V., Bustamante, A. S., & Warschauer, M. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502050 · Cited by 40 · see citing works
Mills, C. M., Danovitch, J. H., Mugambi, V. N., Sands, K. R., & Fox, C. P. (2021) · Child Development · doi:10.1111/cdev.13681 · Cited by 37 · see citing works
Read before Generate! Faithful Long Form Question Answering with Machine Reading
Su, D., Li, X., Zhang, J., Shang, L., Jiang, X., Liu, Q., & Fung, P. (2022) · arXiv · doi:10.48550/arxiv.2203.00343 · Cited by 37 · see citing works
Richards, M. N., & Calvert, S. L. (2017) · Journal of Children and Media · doi:10.1080/17482798.2017.1304969 · Cited by 33 · see citing works
Lee, Y.-J., Chung, J. J. Y., Kim, T. S., Song, J. Y., & Kim, J. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502087 · Cited by 32 · see citing works
Self-citationMind the gap: How incomplete explanations influence children’s interest and learning behaviors
Danovitch, J. H., Mills, C. M., Sands, K. R., & Williams, A. J. (2021) · Cognitive Psychology · doi:10.1016/j.cogpsych.2021.101421 · Cited by 28 · see citing works
How does a switch work? The relation between adult mechanistic language and children's learning
Kurkul, K. E., Castine, E., Leech, K. A., & Corriveau, K. H. (2020) · Journal of Applied Developmental Psychology · doi:10.1016/j.appdev.2020.101221 · Cited by 22 · see citing works
A content analysis of voice-based apps on the market for early literacy development
Xu, Y., & Warschauer, M. (2020) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3392063.3394418 · Cited by 21 · see citing works
Validity of the children's orientation to book reading rating scale
Kaderavek, J. N., Guo, Y. J., & Justice, L. M. (2014) · Journal of Research in Reading · doi:10.1111/j.1467-9817.2012.01528.x · Cited by 19 · see citing works
Tewari, A., & Canny, J. (2014) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/2556288.2557205 · Cited by 17 · see citing works
Giggle gauge: a self-report instrument for evaluating children's engagement with technology
Dietz, G., Pease, Z., McNally, B., et al. (2020) · Interaction Design and Children · doi:10.1145/3392063.3394393 · Cited by 12 · see citing works
Flagged first, then by citations
Research works printed but not linked: 20
- Elizabeth Baraf Bonawitz and Tania Lombrozo. 2012. Occam's rattle: children's use of simplicity and probability to constrain inference. Developmental psychology 48, 4 (2012), 1156.
- Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jefrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. https://doi.org/10.48550/ARXIV.2005.14165 · DOI · Paper page
- Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Zhao, Aida Amini, Mike Green, Qazi Rashid, and Kelvin Guu. 2022. Dialog Inpainting: Turning Documents to Dialogs. In International Conference on Machine Learning (ICML). PMLR.
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). · arXiv
- Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019. ELI5: Long Form Question Answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 3558-3567. https://doi.org/10. 18653/v1/P19-1346 · DOI · Paper page
- Yea-Seul Kim, Jessica Hullman, Matthew Burgess, and Eytan Adar. 2016. Sim- pleScience: Lexical Simplifcation of Scientifc Terminology. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. As- sociation for Computational Linguistics, Austin, Texas, 1066-1071. https: //doi.org/10.18653/v1/D16-1114 · DOI
- Alison King. 1994. Guiding knowledge construction in the classroom: Efects of teaching children how to question and how to explain. American educational research journal 31, 2 (1994), 338-368.
- Katelyn E Kurkul and Kathleen H Corriveau. 2018. Question, explanation, follow- up: A mechanism for learning from others? Child Development 89, 1 (2018), 280-294.
- Cristine H Legare. 2014. The contributions of explanation and exploration to children's scientifc reasoning. Child Development Perspectives 8, 2 (2014), 101-106.
- Margaret Li, Jason Weston, and Stephen Roller. 2019. ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons. https://doi.org/10.48550/ARXIV.1909.03087 · DOI
- Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017. Dailydialog: A manually labelled multi-turn dialogue dataset. arXiv preprint arXiv:1710.03957 (2017). · arXiv
- Paul Light and George Butterworth. 2017. Chapter 7. Desituating cognition through the construction of conceptual knowledge. Routledge.
- Louis Martin, Angela Fan, Éric de la Clergerie, Antoine Bordes, and Benoît Sagot. 2021. MUSS: Multilingual Unsupervised Sentence Simplifcation by Mining Paraphrases. arXiv preprint arXiv:2005.00352 (2021). · arXiv
- Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jef Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman. 2021. WebGPT: Browser- assisted question-answering with human feedback. CoRR abs/2112.09332 (2021). arXiv:2112.09332 https://arxiv.org/abs/2112.09332 · arXiv
- Darrel A Regier, William E Narrow, Diana E Clarke, Helena C Kraemer, S Janet Kuramoto, Emily A Kuhl, and David J Kupfer. 2013. DSM-5 feld trials in the United States and Canada, Part II: test-retest reliability of selected categorical diagnoses. American journal of psychiatry 170, 1 (2013), 59-70.
- Catherine Snow. 1983. Literacy and language: Relationships during the preschool years. Harvard educational review 53, 2 (1983), 165-189.
- Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang. 2019. Mitigating Gender Bias in Natural Language Processing: Literature Review. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 1630-1640. https://doi.org/10.18653/v1/P19-1159 · DOI · Paper page
- Shufan Wang, Fangyuan Xu, Laure Thompson, Eunsol Choi, and Mohit Iyyer. 2022. Modeling Exemplifcation in Long-form Question Answering via Retrieval. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Seattle, United States, 2079-2092. https://doi.org/10. 18653/v1/2022.naacl-main.151 · DOI
- Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, and Arnold Overwijk. 2020. Approximate nearest neighbor nega- tive contrastive learning for dense text retrieval. arXiv preprint arXiv:2007.00808 (2020). · arXiv
- Fangyuan Xu, Junyi Jessy Li, and Eunsol Choi. 2022. How Do We Answer Complex Questions: Discourse Structure of Long-form Answers. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Dublin, Ireland, 3556-3572. https://doi.org/10.18653/v1/2022.acl-long.249 · DOI
Cited by
Sorted by citations
Zhang, C., Liu, X., Ziska, K., Jeon, S., Yu, C.-L., & Xu, Y. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642647 · Cited by 96 · see citing works
Liu, D., Zhou, H., & An, P. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642852 · Cited by 38 · see citing works
Lee, Y., Son, K., Kim, T. S., Kim, J., Chung, J. J. Y., Adar, E., & Kim, J. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3662681 · Cited by 36 · see citing works
Self-citation
- Authors
- Lee, Y., Kim, T. S., Kim, S., Yun, Y., & Kim, J.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- NAVER Cloud (South Korea) (KR)
- Venue
- Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2023-04-19
- Pages
- 1-22
- OpenAlex
- W4366550149
- Cited by
- 35
- References
- 67
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 17
- FWCI
- 6.20
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- KAIST-NAVER Hypercreative AI Center
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- children's questions, chatbots, interactive dialogue, design guidelines, child engagement, voice assistants
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
