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.

This paper describes the utility of semantic resources such as the Web, WordNet and gazetteers in the answer selection process for a question-answering system. In contrast with previous work using individual semantic resources to support answer selection, our work combines multiple resources to boost the confidence scores assigned to correct answers and evaluates different combination strategies based on unweighted sums, weighted linear combinations, and logistic regression. We apply our approach to select answers from candidates produced by three different extraction techniques of varying quality, focusing on TREC questions whose answers represent locations or proper-names. Our experimental results demonstrate that the combination of semantic resources is more effective than individual resources for all three extraction techniques, improving answer selection accuracy by as much as 32.35% for location questions and 72% for proper-name questions. Of the combination strategies tested, logistic regression models produced the best results for both location and proper-name questions.

This paper presents an evaluation of a spoken dialog system for automotive environments. Our overall goal was to measure the impact of user-system interaction on the user’s driving performance, and to determine whether adding context-awareness to the dialog system might reduce the degree of user distraction during driving. To address this issue, we incorporated context-awareness into a spoken dialog system, and implemented three system features using user context, network context and dialog context. A series of experiments were conducted under three different configurations: driving without a dialog system, driving while using a context-aware dialog system, and driving while using a context-unaware dialog system. We measured the differences between the three configurations by comparing the average car speed, the frequency of speed changes and the angle between the car’s direction and the centerline on the road. These results indicate that context-awareness could reduce the degree of user distraction when using a dialog system during driving.

This paper presents an overview of the tools provided by KANTOO MT system for controlled source language checking, source text analysis, and terminology management. The steps in each process are described, and screen images are provided to illustrate the system architecture and example tool interfaces.

We will present the KANTOO machine translation environment, a set of software servers and tools for multilingual document production. KANTOO includes modules for source language analysis, target language generation, source terminology management, target terminology management, and knowledge source development (see Figure 1).