{"citation":{"apa":"Wachsmuth, H., &#38; Alshomary, M. (2022). “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations. In N. Calzolari, C.-R. Huang, H. Kim, J. Pustejovsky, L. Wanner, K.-S. Choi, P.-M. Ryu, H.-H. Chen, L. Donatelli, H. Ji, S. Kurohashi, P. Paggio, N. Xue, S. Kim, Y. Hahm, Z. He, T. K. Lee, E. Santus, F. Bond, &#38; S.-H. Na (Eds.), <i>Proceedings of the 29th International Conference on Computational Linguistics</i> (pp. 344–354). International Committee on Computational Linguistics.","mla":"Wachsmuth, Henning, and Milad Alshomary. “‘Mama Always Had a Way of Explaining Things So I Could Understand’: A Dialogue Corpus for Learning to Construct Explanations.” <i>Proceedings of the 29th International Conference on Computational Linguistics</i>, edited by Nicoletta Calzolari et al., International Committee on Computational Linguistics, 2022, pp. 344–354.","ieee":"H. Wachsmuth and M. Alshomary, “‘Mama Always Had a Way of Explaining Things So I Could Understand’: A Dialogue Corpus for Learning to Construct Explanations,” in <i>Proceedings of the 29th International Conference on Computational Linguistics</i>, 2022, pp. 344–354.","bibtex":"@inproceedings{Wachsmuth_Alshomary_2022, place={Gyeongju, Republic of Korea}, title={“Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations}, booktitle={Proceedings of the 29th International Conference on Computational Linguistics}, publisher={International Committee on Computational Linguistics}, author={Wachsmuth, Henning and Alshomary, Milad}, editor={Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and et al.}, year={2022}, pages={344–354} }","chicago":"Wachsmuth, Henning, and Milad Alshomary. “‘Mama Always Had a Way of Explaining Things So I Could Understand’: A Dialogue Corpus for Learning to Construct Explanations.” In <i>Proceedings of the 29th International Conference on Computational Linguistics</i>, edited by Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, et al., 344–354. Gyeongju, Republic of Korea: International Committee on Computational Linguistics, 2022.","ama":"Wachsmuth H, Alshomary M. “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations. In: Calzolari N, Huang C-R, Kim H, et al., eds. <i>Proceedings of the 29th International Conference on Computational Linguistics</i>. International Committee on Computational Linguistics; 2022:344–354.","short":"H. Wachsmuth, M. Alshomary, in: N. Calzolari, C.-R. Huang, H. Kim, J. Pustejovsky, L. Wanner, K.-S. Choi, P.-M. Ryu, H.-H. Chen, L. Donatelli, H. Ji, S. Kurohashi, P. Paggio, N. Xue, S. Kim, Y. Hahm, Z. He, T.K. Lee, E. Santus, F. Bond, S.-H. Na (Eds.), Proceedings of the 29th International Conference on Computational Linguistics, International Committee on Computational Linguistics, Gyeongju, Republic of Korea, 2022, pp. 344–354."},"publication":"Proceedings of the 29th International Conference on Computational Linguistics","editor":[{"full_name":"Calzolari, Nicoletta","last_name":"Calzolari","first_name":"Nicoletta"},{"last_name":"Huang","full_name":"Huang, Chu-Ren","first_name":"Chu-Ren"},{"first_name":"Hansaem","last_name":"Kim","full_name":"Kim, Hansaem"},{"full_name":"Pustejovsky, James","last_name":"Pustejovsky","first_name":"James"},{"full_name":"Wanner, Leo","last_name":"Wanner","first_name":"Leo"},{"full_name":"Choi, Key-Sun","last_name":"Choi","first_name":"Key-Sun"},{"first_name":"Pum-Mo","full_name":"Ryu, Pum-Mo","last_name":"Ryu"},{"last_name":"Chen","full_name":"Chen, Hsin-Hsi","first_name":"Hsin-Hsi"},{"full_name":"Donatelli, Lucia","last_name":"Donatelli","first_name":"Lucia"},{"first_name":"Heng","last_name":"Ji","full_name":"Ji, Heng"},{"first_name":"Sadao","full_name":"Kurohashi, Sadao","last_name":"Kurohashi"},{"first_name":"Patrizia","full_name":"Paggio, Patrizia","last_name":"Paggio"},{"last_name":"Xue","full_name":"Xue, Nianwen","first_name":"Nianwen"},{"last_name":"Kim","full_name":"Kim, Seokhwan","first_name":"Seokhwan"},{"full_name":"Hahm, Younggyun","last_name":"Hahm","first_name":"Younggyun"},{"last_name":"He","full_name":"He, Zhong","first_name":"Zhong"},{"full_name":"Lee, Tony Kyungil","last_name":"Lee","first_name":"Tony Kyungil"},{"full_name":"Santus, Enrico","last_name":"Santus","first_name":"Enrico"},{"first_name":"Francis","full_name":"Bond, Francis","last_name":"Bond"},{"first_name":"Seung-Hoon","full_name":"Na, Seung-Hoon","last_name":"Na"}],"year":"2022","type":"conference","date_created":"2024-07-22T13:05:42Z","department":[{"_id":"600"}],"user_id":"3900","title":"“Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations","publisher":"International Committee on Computational Linguistics","abstract":[{"lang":"eng","text":"As AI is more and more pervasive in everyday life, humans have an increasing demand to understand its behavior and decisions. Most research on explainable AI builds on the premise that there is one ideal explanation to be found. In fact, however, everyday explanations are co-constructed in a dialogue between the person explaining (the explainer) and the specific person being explained to (the explainee). In this paper, we introduce a first corpus of dialogical explanations to enable NLP research on how humans explain as well as on how AI can learn to imitate this process. The corpus consists of 65 transcribed English dialogues from the Wired video series 5 Levels, explaining 13 topics to five explainees of different proficiency. All 1550 dialogue turns have been manually labeled by five independent professionals for the topic discussed as well as for the dialogue act and the explanation move performed. We analyze linguistic patterns of explainers and explainees, and we explore differences across proficiency levels. BERT-based baseline results indicate that sequence information helps predicting topics, acts, and moves effectively."}],"status":"public","author":[{"full_name":"Wachsmuth, Henning","last_name":"Wachsmuth","id":"3900","first_name":"Henning"},{"first_name":"Milad","id":"73059","full_name":"Alshomary, Milad","last_name":"Alshomary"}],"place":"Gyeongju, Republic of Korea","language":[{"iso":"eng"}],"date_updated":"2024-07-22T13:06:47Z","page":"344–354","_id":"55337"}