How do we perform efficient inference while retaining high translation quality?
Explicitly modeling emotions in dialogue generation has important applications, such as building empathetic personal companions.
Multi-label emotion classification is an important task in NLP and is essential to many applications.
In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers.
In this paper, we propose a simple yet powerful Boundary-Aware Segmentation Network (BASNet), which comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation.
This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension.
In order to generate more meaningful explanations, we propose UNION, a unified end-to-end framework, to utilize several existing commonsense datasets so that it allows a model to learn more dynamics under the scope of commonsense reasoning.
In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD).
Ranked #1 on Salient Object Detection on HKU-IS
Most of the existing methods treat this task as a problem of single-label multi-class text classification.
This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext.
Ranked #3 on Emotion Recognition in Conversation on EC
Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances.
Despite myriad efforts in the literature designing neural dialogue generation systems in recent years, very few consider putting restrictions on the response itself.