Towards automatic argument diagramming of multiparity meetings

Joint Modeling of Content and Discourse Relations in Dialogues

“…For the area of discourse analysis in dialogues, a significant amount of work has been done in predicting local discourse structures, such as recognizing dialogue acts or social acts of adjacent utterances from phone conversations (Stolcke et al, 2000;Kalchbrenner and Blunsom, 2013;Ji et al, 2016), spoken meetings (Dielmann and Renals, 2008), or emails (Cohen et al, 2004). Although discourse information from non-adjacent turns has been studied in the context of online discussion forums (Ghosh et al, 2014) and meetings (Hakkani-Tur, 2009), none of them models the effect of discourse structure on content selection, which is a gap that this work fills in.…”

“…Let t denote the set of links x i , x i in x. Following previous work on discourse analysis in meetings (Rienks et al, 2005;Hakkani-Tur, 2009), we assume that the attachment structure between discourse units are given during both training and testing.…”

“…Let t denote the set of links x i , x i in x. Following previous work on discourse analysis in meetings (Rienks et al, 2005;Hakkani-Tur, 2009), we assume that the attachment structure between discourse units are given during both training and testing. A set of candidate phrases are extracted from each discourse unit x i , from which salient phrases that contain gist information will be identified.…”

Automatic argumentative analysis for interaction mining

“…On a total of 2304 turns, 2247 have received an argumentative automatic classification, with a recall of 97.53%. As can be seen from the table, the A3 algorithm achieves an F-score that is significantly higher than the previous results reported in the literature on the same topic, which are all below 80% Hakkani-Tür 2009). The overall precision is computed as the ratio between the correct argumentative labels and the found argumentative labels and corresponds to 81.26%.…”

Interaction Mining: The New Frontier of Customer Interaction Analytics

“…While it is possible to learn patterns from content-bearing documents, it is nearly impossible to learn pragmatic meaning from non-content bearing words. Approaches that attempted to apply machine learning to Interaction Mining have failed in providing satisfactory results so far (Rienks and Verbree 2006;Hakkani-Tür 2009). This can be explained because the amount of data needed for supervised learning becomes an insurmountable bottleneck.…”