Ajda Gokcen


Madly Ambiguous: A Game for Learning about Structural Ambiguity and Why It’s Hard for Computers
Ajda Gokcen | Ethan Hill | Michael White
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

Madly Ambiguous is an open source, online game aimed at teaching audiences of all ages about structural ambiguity and why it’s hard for computers. After a brief introduction to structural ambiguity, users are challenged to complete a sentence in a way that tricks the computer into guessing an incorrect interpretation. Behind the scenes are two different NLP-based methods for classifying the user’s input, one representative of classic rule-based approaches to disambiguation and the other representative of recent neural network approaches. Qualitative feedback from the system’s use in online, classroom, and science museum settings indicates that it is engaging and successful in conveying the intended take home messages. A demo of Madly Ambiguous can be played at http://madlyambiguous.osu.edu.


A Corpus of Word-Aligned Asked and Anticipated Questions in a Virtual Patient Dialogue System
Ajda Gokcen | Evan Jaffe | Johnsey Erdmann | Michael White | Douglas Danforth
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present a corpus of virtual patient dialogues to which we have added manually annotated gold standard word alignments. Since each question asked by a medical student in the dialogues is mapped to a canonical, anticipated version of the question, the corpus implicitly defines a large set of paraphrase (and non-paraphrase) pairs. We also present a novel process for selecting the most useful data to annotate with word alignments and for ensuring consistent paraphrase status decisions. In support of this process, we have enhanced the earlier Edinburgh alignment tool (Cohn et al., 2008) and revised and extended the Edinburgh guidelines, in particular adding guidance intended to ensure that the word alignments are consistent with the overall paraphrase status decision. The finished corpus and the enhanced alignment tool are made freely available.


I do not disagree: leveraging monolingual alignment to detect disagreement in dialogue
Ajda Gokcen | Marie-Catherine de Marneffe
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)