SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification
In this paper, we describe our participation in WASSA 2018 Implicit Emotion Shared Task (IEST 2018). We claim that the use of emotional external knowledge may enhance the performance and the capacity of generalization of an emotion classification system based on neural networks. Accordingly, we submitted four deep learning systems grounded in a sequence encoding layer. They mainly differ in the feature vector space and the recurrent neural network used in the sequence encoding layer. The official results show that the systems that used emotional external knowledge have a higher capacity of generalization, hence our claim holds.
Code (0)
등록된 구현이 없습니다.
Tasks
Emotion ClassificationEmotion RecognitionGeneral ClassificationSentiment AnalysisSimilar Papers 제목 키워드 기반
Empathetic Dialogue Generation with Pre-trained RoBERTa-GPT2 and External Knowledge
One challenge for dialogue agents is to recognize feelings of the conversation partner and respond accordingly. In this work, RoBERTa-GPT2 is proposed for empathetic dialogue generation, where the pre-trained auto-encodi…
DecoderDialogue GenerationKnowledge Bridging for Empathetic Dialogue Generation
Lack of external knowledge makes empathetic dialogue systems difficult to perceive implicit emotions and learn emotional interactions from limited dialogue history. To address the above problems, we propose to leverage e…
Dialogue GenerationFine-Tune, Don't Prompt, Your Language Model to Identify Biased Language in Clinical Notes
Clinical documentation can contain emotionally charged language with stigmatizing or privileging valences. We present a framework for detecting and classifying such language as stigmatizing, privileging, or neutral. We c…
Prompt EngineeringBias DetectionMRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures
In this retrospective study, we annotated 44 structures on two datasets: an internal dataset of 1,518 MRI sequences from 843 patients at the Mount Sinai Health System, and an external dataset of 397 MRI sequences from 26…
AnatomyBenchmarkingDeep LearningMRI segmentation+1SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations
Detecting emotions in textual conversation is a challenging problem in absence of nonverbal cues typically associated with emotion, like fa- cial expression or voice modulations. How- ever, more and more users are using …
Emotion ClassificationGeneral Classification