A wide array of natural dialogue discourse can be found on the internet. Previous attempts to automatically determine disagreement between interlocutors in such dialogue have mostly relied on n-gram and grammatical dependency features taken from respondent text. Agreement-disagreement classifiers built upon these baseline features tend to do poorly, yet have proven difficult to improve upon. Using the Internet Argument Corpus, which comprises quote and response post pairs taken from an online debate forum with human-annotated agreement scoring, we introduce semantic environment features derived by comparing quote and response sentences which align well. We show that this method improves classifier accuracy relative to the baseline method namely in the retrieval of disagreeing pairs, which improves from 69% to 77%.

Neural text normalization systems can achieve low error rates; however, the errors they make include not only ones from which the hearer can recover (such as reading $3 as three dollar) but also unrecoverable errors, such as reading $3 as three euros. FST decoding constraints have proven effective at reducing unrecoverable errors. In this paper we explore an alternative approach to error mitigation: using dual encoder classifiers trained with both positive and negative examples to implement soft constraints on acceptability. Since the error rates are very low, it is difficult to determine when improvement is significant, but qualitative analysis suggests that soft dual encoder constraints can help reduce the number of unrecoverable errors.

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.