OhioState at IJCNLP-2017 Task 4: Exploring Neural Architectures for Multilingual Customer Feedback Analysis

10/18/2017
by   Dushyanta Dhyani, et al.
0

This paper describes our systems for IJCNLP 2017 Shared Task on Customer Feedback Analysis. We experimented with simple neural architectures that gave competitive performance on certain tasks. This includes shallow CNN and Bi-Directional LSTM architectures with Facebook's Fasttext as a baseline model. Our best performing model was in the Top 5 systems using the Exact-Accuracy and Micro-Average-F1 metrics for the Spanish (85.28 73.17 (87.28 for the French task.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/05/2018

Understanding Meanings in Multilingual Customer Feedback

Understanding and being able to react to customer feedback is the most f...
research
10/12/2017

Auto Analysis of Customer Feedback using CNN and GRU Network

Analyzing customer feedback is the best way to channelize the data into ...
research
08/22/2020

CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection

This paper describes our participation in the SemEval-2020 task Detectio...
research
11/03/2022

Exploring the State-of-the-Art Language Modeling Methods and Data Augmentation Techniques for Multilingual Clause-Level Morphology

This paper describes the KUIS-AI NLP team's submission for the 1^st Shar...
research
04/05/2022

Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER

We investigate the task of complex NER for the English language. The tas...
research
01/29/2019

Universal Dependency Parsing from Scratch

This paper describes Stanford's system at the CoNLL 2018 UD Shared Task....

Please sign up or login with your details

Forgot password? Click here to reset