SNU_IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification

03/06/2019
by   Sanghwan Bae, et al.
0

We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending the existing methods for class imbalance problem. Reducing the distance between the distribution of prediction and ground truth, they consistently show positive effects on the performance. Also we propose a novel neural architecture which utilizes representation of overall context as well as of each utterance. The combination of the methods and the models achieved micro F1 score of about 0.766 on the final evaluation.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/30/2019

ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT

This paper describes the system submitted by ANA Team for the SemEval-20...
research
10/28/2020

Handling Class Imbalance in Low-Resource Dialogue Systems by Combining Few-Shot Classification and Interpolation

Utterance classification performance in low-resource dialogue systems is...
research
08/29/2020

SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda using Sentence-Level Emotional Salience Features

This paper describes a system developed for detecting propaganda techniq...
research
09/28/2019

GLA-Net: An Attention Network with Guided Loss for Mismatch Removal

Mismatch removal is a critical prerequisite in many feature-based tasks....
research
02/05/2022

SEED: Sound Event Early Detection via Evidential Uncertainty

Sound Event Early Detection (SEED) is an essential task in recognizing t...
research
08/22/2020

UTMN at SemEval-2020 Task 11: A Kitchen Solution to Automatic Propaganda Detection

The article describes a fast solution to propaganda detection at SemEval...

Please sign up or login with your details

Forgot password? Click here to reset