Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks. We introduce GoEmotions, the largest manually annotated dataset of 58k English Reddit comments, labeled for 27 emotion categories or Neutral. We demonstrate the high quality of the annotations via Principal Preserved Component Analysis. We conduct transfer learning experiments with existing emotion benchmarks to show that our dataset generalizes well to other domains and different emotion taxonomies. Our BERT-based model achieves an average F1-score of .46 across our proposed taxonomy, leaving much room for improvement.

Graph-based algorithms are among the most successful paradigms for solving semi-supervised learning tasks. Recent work on graph convolutional networks and neural graph learning methods has successfully combined the expressiveness of neural networks with graph structures. We propose a technique that, when applied to these methods, achieves state-of-the-art results on semi-supervised learning datasets. Traditional graph-based algorithms, such as label propagation, were designed with the underlying assumption that the label of a node can be imputed from that of the neighboring nodes. However, real-world graphs are either noisy or have edges that do not correspond to label agreement. To address this, we propose Graph Agreement Models (GAM), which introduces an auxiliary model that predicts the probability of two nodes sharing the same label as a learned function of their features. The agreement model is used when training a node classification model by encouraging agreement only for the pairs of nodes it deems likely to have the same label, thus guiding its parameters to better local optima. The classification and agreement models are trained jointly in a co-training fashion. Moreover, GAM can also be applied to any semi-supervised classification problem, by inducing a graph whenever one is not provided. We demonstrate that our method achieves a relative improvement of up to 72% for various node classification models, and obtains state-of-the-art results on multiple established datasets.

To improve software reliability, many rule-based techniques have been proposed to infer programming rules and detect violations of these rules as bugs. These rule-based approaches often rely on the highly frequent appearances of certain patterns in a project to infer rules. It is known that if a pattern does not appear frequently enough, rules are not learned, thus missing many bugs.

Dana Movshovitz-Attias, William W. Cohen. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

As search engines are becoming smarter at interpreting user queries and providing meaningful responses, they rely on ontologies to understand the meaning of entities. Creating ontologies manually is a laborious process, and resulting ontologies may not reflect the way users think about the world, as many concepts used in queries are noisy, and not easily amenable to formal modeling. There has been considerable effort in generating ontologies from Web text and query streams, which may be more reflective of how users query and write content. In this paper, we describe the LATTE system that automatically generates a subconcept--superconcept hierarchy, which is critical for using ontologies to answer queries. LATTE combines signals based on word-vector representations of concepts and dependency parse trees; however, LATTE derives most of its power from an ontology of attributes extracted from the Web that indicates the aspects of concepts that users find important. LATTE achieves an F1 score of 74%, which is comparable to expert agreement on a similar task. We additionally demonstrate the usefulness of LATTE in detecting high quality concepts from an existing resource of IsA links.

Question answering (Q&A) communities have been gaining popularity in the past few years. The success of such sites depends mainly on the contribution of a small number of expert users who provide a significant portion of the helpful answers, and so identifying users that have the potential of becoming strong contributers is an important task for owners of such communities.