SlowGuess/NAACL_2021_OCR huggingface dataset:
of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pages 812-817. ACL.
Alexander Koller, Konstantina Garoufi, Maria Staudte, and Matthew Crocker. 2012. Enhancing referential success by tracking hearer gaze. In Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 30-39. ACL.
Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, and Stefan Lee. 2020. 12-in-1: Multi-task vision and language representation learning. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pages 10434-10443. IEEE.
Manuel Portela and Carlos Granell-Canut. 2017. A new friend in our smartphone?: observing interactions with chatbots in the search of emotional engagement. In Proceedings of the XVIII International Conference on Human Computer Interaction, Interaction, pages 48:1-48:7. ACM.
Matthew Purver. 2004. The Theory and Use of Clarification Requests in Dialogue. Ph.D. thesis, King's College, University of London. Supervised by Jonathan Ginzburg.
Matthew Purver. 2006. CLARIE: Handling clarification requests in a dialogue system. Research on Language and Computation, 4(2-3):259-288.
Matthew Purver, Jonathan Ginzburg, and Patrick Healey. 2003. On the means for clarification in dialogue. In Current and New Directions in Discourse and Dialogue, pages 235-255. Kluwer Academic Publishers.
Matthew Purver, Julian Hough, and Christine Howes. 2018. Computational models of miscommunication phenomena. Topics in Cognitive Science, 10(2):425-451.
Sudha Rao and Hal Daumé III. 2018. Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2737-2746, Melbourne, Australia. Association for Computational Linguistics.
Verena Rieser and Johanna Moore. 2005. Implications for generating clarification requests in task-oriented dialogues. In Proc of ACL, pages 239-246.
Kepa Rodríguez and David Schlangen. 2004. Form, intonation and function of clarification requests in german task oriented spoken dialogues. In Proc of SEMDIAL, pages 101-108.
Emanuel Schegloff. 1987. Some sources of misunderstanding in talk-in-interaction. Linguistics, 8:201-218.
David Schlangen and Gabriel Skantze. 2009. A general, abstract model of incremental dialogue processing. In Proceedings of the 12th Conference of the European Chapter of the ACL, pages 710-718. ACL.
Ethan O. Selfridge, Iker Arizmendi, Peter A. Heeman, and Jason D. Williams. 2012. Integrating incremental speech recognition and POMDP-based dialogue systems. In Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 275-279, Seoul, South Korea. ACL.
Gabriel Skantze. 2007. Error Handling in Spoken Dialogue Systems. Ph.D. thesis, KTH - Royal Institute of Technology, Sweden. Supervised by R. Carlson.
Laura Stoia. 2007. Noun Phrase Generation for Situated Dialogs. Ph.D. thesis, Ohio State University, USA. Supervised by Donna Byron.
Svetlana Stoyanchev, Alex Liu, and Julia Hirschberg. 2013. Modelling human clarification strategies. In Proceedings of the SIGDIAL 2013 Conference, pages 137-141, Metz, France. ACL.
Hao Tan and Mohit Bansal. 2019. LXMERT: Learning cross-modality encoder representations from transformers. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). ACL.
Xiang Zhi Tan, Sean Andrist, Dan Bohus, and Eric Horvitz. 2020. Now, over here: Leveraging extended attentional capabilities in human-robot interaction. In Companion of the International Conference on Human-Robot Interaction, HRI '20, page 468-470, New York, NY, USA. ACM.
Jesse Thomason, Michael Murray, Maya Cakmak, and Luke Zettlemoyer. 2019. Vision-and-dialog navigation. In 3rd Annual Conference on Robot Learning, volume 100 of Proceedings of Machine Learning Research, pages 394-406. PMLR.
Michael Tomasello, Malinda Carpenter, Josep Call, Tanya Behne, and Henrik Moll. 2005. Understanding and sharing intentions: The origins of cultural cognition. Behavioral and Brain Sciences, 28(5):675-691.
David Traum. 2003. Semantics and pragmatics of questions and answers for dialogue agents. In Proceedings of the International Workshop on Computational Semantics (IWCS), pages 380-394.
Martín Villalba, Christoph Teichmann, and Alexander Koller. 2017. Generating contrastive referring expressions. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 678-687, Vancouver, Canada. ACL.
A Annotation decision procedure
In this section we formalize the classification methodology for clarifications. Our examples here are from the SCARE corpus (Stoia, 2007). The SCARE corpus consists of fifteen English spontaneous dialogues situated in an instruction giving task. The dialogues vary in length, with a minimum of 400 turns and a maximum of 1500.
The annotation was performed by two independent annotators with an initial interannotator agreement of .84 kappa. Disagreements were discussed until agreement, a single annotation was obtained. We excluded from the annotation dialogue 1 which has almost no feedback from the DF because he thought that he was not supposed to speak.
The decision graph used in our "quasi-systematic" annotation is depicted in Figure 2. We call our procedure "quasi-systematic" because, while its tasks (depicted in rectangles) are readily automated, its decision points are not as they require subjective human judgments. Decision points D1 and D2 decide whether the turn is a CR or not; new tasks and digressions from the current task answer "no" to both decision points and just stack their evidence of proposal in T3. If the turn is a CR of a proposal X, T4 unstacks all proposals over X as a result of applying the downward evidence property of conversations (discussed in the paper). Intuitively, the turn is taken as an implicit uptake in level 4 of all the proposals over proposal X (which must be completed before X can be completed).<sup>9</sup> Decision points D3 to D6 decide whether the CRs belong to (Clark, 1996)'s levels 1 to 4 respectively, with the help of (Rodriguez and Schlangen, 2004). If a turn can be preceded by positive evidence in level 4 but it still is negative evidence of some proposal made earlier in the dialogue we annotate the CR as other. An example of this in dialogue 2 in the SCARE dataset THERE'S ISN'T REALLY SHORT FOR THERE ARE, IS IT? BUT PEOPLE DO IT ANYWAY where the DF follower is correcting the DG who said THERE'S THREE DOORS earlier. The negative evidence is not related to the modalities relevant for the task at hand but to the language itself.
<sup>8</sup>The corpus is available in http://slate.cse.ohio-state.edu/quake-corpora/scare/>.
9This intuition is in line with Geurts's preliminary analysis of non-declaratives: if the speaker did not negotiate the proposals over X, then we can assume that he did not have problems up-taking them (Geurts, in press).
Figure 2: Decision graph for our recipe. Decision points D1 and D2 decide whether the turn is a CR or not. Decision points D3 to D6 decide whether the CRs belong to (Clark, 1996)'s levels 1 to 4 respectively. T9 indicates that the CR is grounded in a modality not represented by levels 1 to 4.