Discourse relation identification has been anactive area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system.Previous work primarily relies on a corpora of formal text which is inherently nondialogic, i.e., news and journals.This data however is not suitable to handle the nuances of informal dialogue nor is it capable of navigating the plethora of valid topics present in open-domain dialogue.In this paper, we designed a novel discourse relation identification pipeline specifically tuned for opendomain dialogue systems.We firstly propose a method to automatically extract the implicit discourse relation argument pairs and labels from a dataset of dialogic turns, resulting in a novel corpus of discourse relation pairs; the first of its kind to attempt to identify the discourse relations connecting the dialogic turns in open-domain discourse.Moreover, we have taken the first steps to leverage the dialogue features unique to our task to further improve the identification of such relations by performing feature ablation and incorporating dialogue features to enhance the state-of-the-art model.

Many genres of natural language text are narratively structured, a testament to our predilection for organizing our experiences as narratives.There is broad consensus that understanding a narrative requires identifying and tracking the goals and desires of the characters and their narrative outcomes.However, to date, there has been limited work on computational models for this problem.We introduce a new dataset, DesireDB, which includes goldstandard labels for identifying statements of desire, textual evidence for desire fulfillment, and annotations for whether the stated desire is fulfilled given the evidence in the narrative context.We report experiments on tracking desire fulfillment using different methods, and show that LSTM Skip-Thought model achieves F-measure of 0.7 on our corpus.