Publications

2019

Qiao, M., & Valiant, G. (2019). A Theory of Selective Prediction. Proceedings of the 32nd Conference on Learning Theory (PMLR), 99, 2580–2594. https://proceedings.mlr.press/v99/qiao19a.html
Liu, T., Ding, W., Wang, Z., Tang, J., Huang, G. Y., & Liu, Z. (2019). Automatic Short Answer Grading via Multiway Attention Networks. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Artificial Intelligence in Education (Vol. 11626, pp. 169–173). Springer International Publishing. https://doi.org/10.1007/978-3-030-23207-8_32
Strobl, Ailhaud, Benetos, Devitt, Kruse, & Proske. (2019). Digital support for academic writing: A review of technologies and pedagogies. Computers & Education, 131, 33–48. https://doi.org/10.1016/j.compedu.2018.12.00
Gere, A. R., Limlamai, N., Wilson, E., MacDougall Saylor, K., & Pugh, R. (2019). Writing and Conceptual Learning in Science: An Analysis of Assignments. Written Communication, 36(1), 99–135. https://doi.org/10.1177/0741088318804820
Liu, T., Ding, W., Wang, Z., Tang, J., Huang, G. Y., & Liu, Z. (2019). Automatic Short Answer Grading via Multiway Attention Networks. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Artificial Intelligence in Education (Vol. 11626, pp. 169–173). Springer International Publishing. https://doi.org/10.1007/978-3-030-23207-8_32
Sung, C., Dhamecha, T., Saha, S., Ma, T., Reddy, V., & Arora, R. (2019). Pre-Training BERT on Domain Resources for Short Answer Grading. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 6070–6074. https://doi.org/10.18653/v1/D19-1628
Wang, T., Inoue, N., Ouchi, H., Mizumoto, T., & Inui, K. (2019). Inject Rubrics into Short Answer Grading System. Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), 175–182. https://doi.org/10.18653/v1/D19-6119
Green, B., & Chen, Y. (2019). The Principles and Limits of Algorithm-in-the-Loop Decision Making. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1–24. https://doi.org/10.1145/3359152
Hellman, S., Rosenstein, M., Gorman, A., Murray, W., Becker, L., Baikadi, A., Budden, J., & Foltz, P. W. (2019). Scaling Up Writing in the Curriculum: Batch Mode Active Learning for Automated Essay Scoring. Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale, 1–10. https://doi.org/10.1145/3330430.3333629
Pandya, R., Huang, S. H., Hadfield-Menell, D., & Dragan, A. D. (2019). Human-AI Learning Performance in Multi-Armed Bandits. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 369–375. https://doi.org/10.1145/3306618.3314245
Gao, Y., Sun, C., & Passonneau, R. J. (2019). Automated Pyramid Summarization Evaluation. Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), 404–418. https://doi.org/10.18653/v1/K19-1038
Gao, Y., Driban, A., Xavier McManus, B., Musi, E., Davies, P., Muresan, S., & Passonneau, R. J. (2019). Rubric Reliability and Annotation of Content and Argument in Source-Based Argument Essays. Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, 507–518. https://doi.org/10.18653/v1/W19-4452
Taniguchi, T., Mochihashi, D., Nagai, T., Uchida, S., Inoue, N., Kobayashi, I., Nakamura, T., Hagiwara, Y., Iwahashi, N., & Inamura, T. (2019). Survey on frontiers of language and robotics. Advanced Robotics, 33(15–16), 700–730. https://doi.org/10.1080/01691864.2019.1632223
Magyar, J., Kobayashi, M., Nishio, S., Sincak, P., & Ishiguro, H. (2019). Autonomous Robotic Dialogue System with Reinforcement Learning for Elderlies with Dementia. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 3416–3421. https://doi.org/10.1109/SMC.2019.8914248
Sierra, C., & International Joint Conferences on Artificial Intelligence, Inc (Eds.). (2019). International Joint Conferences on Artificial Intelligence (IJCAI 2017): Melbourne, Australia, 19-25 August 2017. Curran Associates, Inc.
Addlesee, A., Eshghi, A., & Konstas, I. (2019, September 14). Current Challenges in Spoken Dialogue Systems and Why They Are Critical for Those Living with Dementia. ArXiv:1909.06644 [Cs]. Dialog for Good (DiGo) 2019, Workshop on Speech and Language Technology Serving Society, Stockholm, Sweden. http://arxiv.org/abs/1909.06644
Addlesee, A., Eshghi, A., & Konstas, I. (2019). Current Challenges in Spoken Dialogue Systems and Why They Are Critical for Those Living with Dementia. ArXiv:1909.06644 [Cs]. http://arxiv.org/abs/1909.06644
Ritter, F. E., Tehranchi, F., & Oury, J. D. (2019). ACT-R: A cognitive architecture for modeling cognition. WIREs Cognitive Science, 10(3), e1488. https://doi.org/https://doi.org/10.1002/wcs.1488
Miyazawa, K., Horii, T., Aoki, T., & Nagai, T. (2019). Integrated Cognitive Architecture for Robot Learning of Action and Language. Frontiers in Robotics and AI, 6, 131. https://doi.org/10.3389/frobt.2019.00131
Kulkarni, A., Zha, Y., Chakkraborti, T., Vadlamudi, S. G., Zhang, Y., & Kambhampati, S. (2019). Explicable Planning as Minimizing Distance from Expected Behavior. Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS ’19), Richland, CS, 2075–2077.
Chakraborti, T., Sreedharan, S., & Kambhampati, S. (2019). Balancing Explicability and Explanations for Human-Aware Planning. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19). https://www.ijcai.org/Proceedings/2019/185
Huerta, M., & Garza, T. (2019). Writing in Science: Why, How, and for Whom? A Systematic Literature Review of 20 Years of Intervention Research (1996–2016). Educational Psychology Review, 31(3), 533–570. https://doi.org/10.1007/s10648-019-09477-1
Zare, M., Ayub, A., Wagner, A. R., & Passonneau, R. J. (2019). Show Me How to Win: A Robot that Uses Dialog Management to Learn from Demonstrations. Fourth Games and Natural Language Processing Workshop (GAMNLP-19). https://dl.acm.org/doi/10.1145/3337722.3341866
Gao, Y., Driban, A., McManus, B., Musi, E., Davies, P., & Passonneau. (2019). Rubric Reliability and Annotation of Content and Argument in Source-Based Argument Essays. Proceedings of the Fourteenth on Innovative Use of NLP for Building Educational Applications (BEA), 507–518. https://www.aclweb.org/anthology/W19-4452/

2018

Kalman, C. S. (2018). Successful Science and Engineering Teaching: Theoretical and Learning Perspectives. Springer, Cham. https://doi.org/10.1007/978-3-319-66140-7
Gao, Y., Warner, A., & Passonneau, R. J. (2018, May 7). PyrEval: An Automated Method for Summary Content Analysis. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). http://www.lrec-conf.org/proceedings/lrec2018/pdf/1096.pdf
Krippendorff, K. (2018). Content analysis: an introduction to its methodology (Fourth Edition). SAGE.
National Science & Technology Council. (2018). Charting a course for success: America’s strategy for STEM education. Office of Science and Technology Policy. https://www.energy.gov/sites/default/files/2019/05/f62/STEM-Education-Strategic-Plan-2018.pdf
Balgopal, M. M., Casper, A. M. A., Wallace, A. M., Laybourn, P. J., & Brisch, E. (2018). Writing Matters: Writing-to-Learn Activities Increase Undergraduate Performance in Cell Biology. BioScience, 68(6), 445–454. https://doi.org/10.1093/biosci/biy042
Hand, B., Shelley, M. C., Laugerman, M., Fostvedt, L., & Therrien, W. (2018). Improving critical thinking growth for disadvantaged groups within elementary school science: A randomized controlled trial using the Science Writing Heuristic approach. Science Education, 102(4), 693–710. https://doi.org/10.1002/sce.21341
Cohen, Y., Levi, E., & Ben-Simon, A. (2018). Validating human and automated scoring of essays against “true” scores. Applied Measurement in Education, 31(3), 241–250. https://doi.org/10.1080/08957347.2018.1464450
Saha, S., Dhamecha, T. I., Marvaniya, S., Sindhgatta, R., & Sengupta, B. (2018). Sentence Level or Token Level Features for Automatic Short Answer Grading?: Use Both. In C. Penstein Rosé, R. Martínez-Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, & B. du Boulay (Eds.), Artificial Intelligence in Education (Vol. 10947, pp. 503–517). Springer International Publishing. https://doi.org/10.1007/978-3-319-93843-1_37
Wang, E., Matsumura, L. C., & Correnti, R. (2018). Student Writing Accepted as High-Quality Responses to Analytic Text-Based Writing Tasks. The Elementary School Journal, 118(3), 357–383. https://doi.org/10.1086/696097
Gao, Y., M.Davies, P., & Passonneau, R. J. (2018). Automated Content Analysis: A Case Study of Computer Science Student Summaries. Proceedings of the Thirteenth Workshop on Innovative Use of NLP for          Building Educational Applications, 264–272. https://doi.org/10.18653/v1/W18-0531
Bousmalis, K., Irpan, A., Wohlhart, P., Bai, Y., Kelcey, M., Kalakrishnan, M., Downs, L., Ibarz, J., Pastor, P., Konolige, K., Levine, S., & Vanhoucke, V. (2018). Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping. 2018 IEEE International Conference on Robotics and Automation (ICRA), 4243–4250. https://doi.org/10.1109/ICRA.2018.8460875
Xu, Y., & Reitter, D. (2018). Information density converges in dialogue: Towards an information-theoretic model. Cognition, 170, 147–163. https://doi.org/10.1016/j.cognition.2017.09.018
Shi, W., & Yu, Z. (2018). Sentiment Adaptive End-to-End Dialog Systems. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1509–1519. https://doi.org/10.18653/v1/P18-1140
Moon, A., Gere, A. R., & Shultz, G. V. (2018). Writing in the STEM classroom: Faculty conceptions of writing and its role in the undergraduate classroom. Science Education, 102(5), 1007–1028. https://doi.org/10.1002/sce.21454
Knight, S., Buckingham Shum, S., Ryan, P., Sándor, Á., & Wang, X. (2018). Designing Academic Writing Analytics for Civil Law Student Self-Assessment. International Journal of Artificial Intelligence in Education, 28(1), 1–28. https://doi.org/10.1007/s40593-016-0121-0
Lan, W., & Xu, W. (2018). Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering. Proceedings of the 27th International Conference on Computational Linguistics, 3890–3902. https://www.aclweb.org/anthology/C18-1328
Zafeiroudi, K. D., Eckman, L., & Passonneau, R. J. (2018, October 8). Testing a Knowledge Inquiry System on Question Answering Tasks. Joint Proceedings of ISWC 2018 Workshops SemDeep-4 and NLIWoD-4. Natural Language Interfaces to Web of Data (NLIWoD), Monterey, CA.
Passonneau, R. J., Poddar, A., Gite, G., Krivokapic, A., Yang, Q., & Perin, D. (2018). Wise Crowd Content Assessment and Educational Rubrics. International Journal of Artificial Intelligence in Education, 28(1), 29–55. https://doi.org/https://doi.org/10.1007/s40593-016-0128-6
Gao, J., Radeva, A., Shen, C., Wang, S., Wang, Q., & Passonneau, R. J. (2018). Prediction of a hotspot pattern in keyword search results. Computer Speech & Language, 48, 80–102. https://doi.org/https://doi.org/10.1016/j.csl.2017.10.005
Zafeiroudi, K. D., & Passonneau, R. J. (2018, November 6). Knowledge Inquiry for Information Foraging. 23rd International Command and Control Research and Technology Symposium (ICCRTS), Pensacola, FL.

2017

Hendrycks, D., & Gimpel, D. (2017). A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. 5th International Conference on Learning Representations (ICLR 2017). https://openreview.net/forum?id=Hkg4TI9xl
Geifman, Y., & El-Yaniv, R. (2017). Selective Classification for Deep Neural Networks. Advances in Neural Information Processing Systems 30 (NIPS 2017), 30. ttps://proceedings.neurips.cc/paper/2017/file/4a8423d5e91fda00bb7e46540e2b0cf1-Paper.pdf
Rahimi, Z., Litman, D., Correnti, R., Wang, E., & Matsumura, L. C. (2017). Assessing Students’ Use of Evidence and Organization in Response-to-Text Writing: Using Natural Language Processing for Rubric-Based Automated Scoring. International Journal of Artificial Intelligence in Education, 27(4), 694–728. https://doi.org/10.1007/s40593-017-0143-2
Park, J., & Cho, K. (2017). Toward the Integration of Peer Reviewing and Computational Linguistics Approaches. Journal of Educational Computing Research, 55(1), 123–144. https://doi.org/10.1177/0735633116656454
United States Chamber of Commerce. (2017). Bridging the soft skills gap: How the business and education sectors are partnering to prepare students for the 21 st century workforce. Center for Education and Workforce. https://www.uschamberfoundation.org/sites/default/files/Closing%20the%20Soft%20Skills%20Gap.pdf
Belland, B. R., Walker, A. E., Kim, N. J., & Lefler, M. (2017). Synthesizing Results From Empirical Research on Computer-Based Scaffolding in STEM Education: A Meta-Analysis. Review of Educational Research, 87(2), 309–344. https://doi.org/10.3102/0034654316670999
Li, H., Gobert, J., & Dickler, R. (2017). Automated assessment for scientific explanations in on-line science inquiry. 10th International Conference on Educational Data Mining. https://files.eric.ed.gov/fulltext/ED596581.pdf
Riordan, B., Horbach, A., Cahill, A., Zesch, T., & Lee, C. M. (2017). Investigating neural architectures for short answer scoring. Proceedings of the 12th Workshop on Innovative Use of NLP for Building          Educational Applications, 159–168. https://doi.org/10.18653/v1/W17-5017
Easterday, M. W., Rees Lewis, D., & Gerber, E. M. (2017). Designing Crowdcritique Systems for Formative Feedback. International Journal of Artificial Intelligence in Education, 27(3), 623–663. https://doi.org/10.1007/s40593-016-0125-9
Liang, C., Yang, X., Wham, D., Pursel, B., Passonneau, R., & Giles, C. L. (2017). Distractor Generation with Generative Adversarial Nets for Automatically Creating Fill-in-the-blank Questions. Proceedings of the Knowledge Capture Conference, 1–4. https://doi.org/10.1145/3148011.3154463
Chinaei, H., Currie, L. C., Danks, A., Lin, H., Mehta, T., & Rudzicz, F. (2017). Identifying and Avoiding Confusion in Dialogue with People with Alzheimer’s Disease. Computational Linguistics, 43(2), 377–406. https://doi.org/10.1162/COLI_a_00290
Yu, Z., Ramanarayanan, V., Mundkowsky, R., Lange, P., Ivanov, A., Black, A. W., & Suendermann-Oeft, D. (2017). Multimodal HALEF: An Open-Source Modular Web-Based Multimodal Dialog Framework. In K. Jokinen & G. Wilcock (Eds.), Dialogues with Social Robots (Vol. 427, pp. 233–244). Springer Singapore. https://doi.org/10.1007/978-981-10-2585-3_18
Zhou Yu, & Alan W. Black. (2017). Learning conversational systems that interleave task and non-task content. Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI’17), 4214–4220.

2016
2015
2014