Online platforms and communities establish their own norms that govern what behavior is acceptable within the community. Substantial effort in NLP has focused on identifying unacceptable behaviors and, recently, on forecasting them before they occur. However, these efforts have largely focused on toxicity as the sole form of community norm violation. Such focus has overlooked the much larger set of rules that moderators enforce. Here, we introduce a new dataset focusing on a more complete spectrum of community norms and their violations in the local conversational and global community contexts. We introduce a series of models that use this data to develop context-and community-sensitive norm violation detection, showing that these changes give high performance. 1

Translating natural language utterances to executable queries is a helpful technique in making the vast amount of data stored in relational databases accessible to a wider range of nontech-savvy end users. Prior work in this area has largely focused on textual input that is linguistically correct and semantically unambiguous. However, real-world user queries are often succinct, colloquial, and noisy, resembling the input of a search engine. In this work, we introduce data augmentation techniques and a sampling-based content-aware BERT model (COLLOQL) to achieve robust text-to-SQL modeling over natural language search (NLS) questions. Due to the lack of evaluation data, we curate a new dataset of NLS questions and demonstrate the efficacy of our approach. COLLOQL's superior performance extends to well-formed text, achieving 84.9% (logical) and 90.7% (execution) accuracy on the WikiSQL dataset, making it, to the best of our knowledge, the highest performing model that does not use execution guided decoding.

In academic publications, citations are used to build context for a concept by highlighting relevant aspects from reference papers. Automatically identifying referenced snippets can help researchers swiftly isolate principal contributions of scientific works. In this paper, we exploit the underlying structure of scientific articles to predict reference paper spans and facets corresponding to a citation. We propose two methods to detect citation spanskeyphrase overlap, BERT along with structural priors. We fine-tune FastText embeddings and leverage textual, positional features to predict citation facets.

The rise in the usage of social media has placed it in a central position for news dissemination and consumption. This greatly increases the potential for proliferation of rumours and misinformation. In an effort to mitigate the spread of rumours, we tackle the related task of identifying the stance (Support, Deny, Query, Comment) of a social media post. Unlike previous works (Fajcik et al., 2019;Yang et al., 2019), we impose inductive biases that capture platform specific user behavior. These biases, coupled with social media finetuning of BERT allow for better language understanding, thus yielding an F 1 score of 58.7 on the SemEval 2019 task on rumour stance detection.