Incivility is not only prevalent on online social media platforms, but also has concrete effects on individual users, online groups, the platforms themselves, and the society at large. Given the prevalence and effects of online incivility, and the challenges involved in humanbased incivility detection, it is urgent to develop validated and versatile automatic approaches to identifying uncivil posts and comments. This project advances both a neural, BERT-based classifier as well as a logistic regression classifier to identify uncivil comments. The classifier is trained on a dataset of Reddit posts, which are annotated for incivility, and further expanded using a combination of labeled data from Reddit and Twitter. Our best performing model achieves an F 1 of 0.802 on our Reddit test set. The final model is not only applicable across social media platforms and their distinct data structures, but also computationally versatile, and -as such -ready to be used on vast volumes of online data. All trained models and annotated data are made available to the research community.
Gunrock is the winner of the 2018 Amazon Alexa Prize, as evaluated by coherence and engagement from both real users and Amazonselected expert conversationalists. We focus on understanding complex sentences and having in-depth conversations in open domains. In this paper, we introduce some innovative system designs and related validation analysis. Overall, we found that users produce longer sentences to Gunrock, which are directly related to users' engagement (e.g., ratings, number of turns). Additionally, users' backstory queries about Gunrock are positively correlated to user satisfaction. Finally, we found dialog flows that interleave facts and personal opinions and stories lead to better user satisfaction.
Dependency parsing of conversational input can play an important role in language understanding for dialog systems by identifying the relationships between entities extracted from user utterances. Additionally, effective dependency parsing can elucidate differences in language structure and usage for discourse analysis of human-human versus human-machine dialogs. However, models trained on datasets based on news articles and web data do not perform well on spoken human-machine dialog, and currently available annotation schemes do not adapt well to dialog data. Therefore, we propose the Spoken Conversation Universal Dependencies (SCUD) annotation scheme that extends the Universal Dependencies (UD) (Nivre et al., 2016) guidelines to spoken human-machine dialogs. We also provide ConvBank, a conversation dataset between humans and an opendomain conversational dialog system with SCUD annotation. Finally, to demonstrate the utility of the dataset, we train a dependency parser on the ConvBank dataset. We demonstrate that by pre-training a dependency parser on a set of larger public datasets and finetuning on ConvBank data, we achieved the best result, 85.05% unlabeled and 77.82% labeled attachment accuracy.
Gunrock 2.0 is built on top of Gunrock with an emphasis on user adaptation. Gunrock 2.0 combines various neural natural language understanding modules, including named entity detection, linking, and dialog act prediction, to improve user understanding. Its dialog management is a hierarchical model that handles various topics, such as movies, music, and sports. The system-level dialog manager can handle question detection, acknowledgment, error handling, and additional functions, making downstream modules much easier to design and implement. The dialog manager also adapts its topic selection to accommodate different users' profile information, such as inferred gender and personality. The generation model is a mix of templates and neural generation models. Gunrock 2.0 is able to achieve an average rating of 3.73 at its latest build from May 29 th to June 4 th .
The initial stages of seamount subduction and associated deformation in an overriding accretionary wedge is rarely documented. Initial subduction of Bennett Knoll seamount and faulting of the overlying strata along the Hikurangi subduction margin, New Zealand, are here studied using multibeam swath bathymetry, subbottom profiles, and regional seismic reflection lines. Our results provide new insights into the earliest stages of seamount collision at sediment-rich margins. Differential shortening along the subduction front induced by seamount subduction is initially accommodated in the accretionary wedge by conjugate strike-slip faults that straddle the buried flanks of the seamount and offset the frontal thrusts by as much as 5 km. The geometries of the strike-slip faults are controlled by the seamount’s dimensions and aspect, the obliquity of plate convergence, pore-fluid pressure, and the thickness and rheology of the incoming sedimentary section. Strike-slip faults in such settings are ephemeral and overprinted by the formation of new structures as seamount subduction advances.
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