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Found 9 papers, 1 papers with code
LearnedWMP: Workload Memory Prediction Using Distribution of Query Templates
In this research, we are interested in query performance optimization under concurrent execution of a batch of queries (a workload).
Predicting Evoked Emotions in Conversations
In this work, we introduce the novel problem of Predicting Emotions in Conversations (PEC) for the next turn (n+1), given combinations of textual and/or emotion input up to turn n. We systematically approach the problem by modeling three dimensions inherently connected to evoked emotions in dialogues, including (i) sequence modeling, (ii) self-dependency modeling, and (iii) recency modeling.
Conversation Derailment Forecasting with Graph Convolutional Networks
Online conversations are particularly susceptible to derailment, which can manifest itself in the form of toxic communication patterns like disrespectful comments or verbal abuse.
Industry and Academic Research in Computer Vision
The results indicate that the distribution of the research topics is similar in industry and academic papers.
Affective and Contextual Embedding for Sarcasm Detection
To the best of our knowledge, this is the first attempt to directly alter BERT{'}s architecture and train it from scratch to build a sarcasm classifier.
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks
Affective tasks such as sentiment analysis, emotion classification, and sarcasm detection have been popular in recent years due to an abundance of user-generated data, accurate computational linguistic models, and a broad range of relevant applications in various domains.
Elastic Bulk Synchronous Parallel Model for Distributed Deep Learning
To ameliorate this shortcoming of classic BSP, we propose ELASTICBSP a model that aims to relax its strict synchronization requirement.
Scene Classification in Indoor Environments for Robots using Context Based Word Embeddings
Scene Classification has been addressed with numerous techniques in computer vision literature.
Learning Emotion-enriched Word Representations
As a consequence, emotionally dissimilar words, such as {``}happy{''} and {``}sad{''} occurring in similar contexts would purport more similar meaning than emotionally similar words, such as {``}happy{''} and {``}joy{''}.
