We are interested in the problem of understanding personal narratives (PN) - spoken or written - recollections of facts, events, and thoughts. For PNs, we define emotion carriers as the speech or text segments that best explain the emotional state of the narrator. Such segments may span from single to multiple words, containing for example verb or noun phrases. Advanced automatic understanding of PNs requires not only the prediction of the narrator’s emotional state but also to identify which events (e.g. the loss of a relative or the visit of grandpa) or people (e.g. the old group of high school mates) carry the emotion manifested during the personal recollection. This work proposes and evaluates an annotation model for identifying emotion carriers in spoken personal narratives. Compared to other text genres such as news and microblogs, spoken PNs are particularly challenging because a narrative is usually unstructured, involving multiple sub-events and characters as well as thoughts and associated emotions perceived by the narrator. In this work, we experiment with annotating emotion carriers in speech transcriptions from the Ulm State-of-Mind in Speech (USoMS) corpus, a dataset of PNs in German. We believe this resource could be used for experiments in the automatic extraction of emotion carriers from PN, a task that could provide further advancements in narrative understanding.

In this work, we investigate the human perception of coherence in open-domain dialogues. In particular, we address the problem of annotating and modeling the coherence of next-turn candidates while considering the entire history of the dialogue. First, we create the Switchboard Coherence (SWBD-Coh) corpus, a dataset of human-human spoken dialogues annotated with turn coherence ratings, where next-turn candidate utterances ratings are provided considering the full dialogue context. Our statistical analysis of the corpus indicates how turn coherence perception is affected by patterns of distribution of entities previously introduced and the Dialogue Acts used. Second, we experiment with different architectures to model entities, Dialogue Acts and their combination and evaluate their performance in predicting human coherence ratings on SWBD-Coh. We find that models combining both DA and entity information yield the best performances both for response selection and turn coherence rating.