Search Results for author:
Found 6 papers, 1 papers with code
Index Tracking via Learning to Predict Market Sensitivities
A basic strategy to manage an index fund is replicating the index's constituents and weights identically, which is, however, cost-ineffective and impractical.
Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation
Source-free domain adaptation is an emerging line of work in deep learning research since it is closely related to the real-world environment.
Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation
Applying generative adversarial networks (GANs) to text-related tasks is challenging due to the discrete nature of language.
MILAB at SemEval-2019 Task 3: Multi-View Turn-by-Turn Model for Context-Aware Sentiment Analysis
This paper describes our system for SemEval-2019 Task 3: EmoContext, which aims to predict the emotion of the third utterance considering two preceding utterances in a dialogue.
Improving Neural Question Generation using Answer Separation
Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the generation of unintended questions.
AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification
In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence.
