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

Dushyanta Dhyani


Abstract
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% for both) and French (70% and 73.17% respectively) task, and outperformed all the other models on comment (87.28%) and meaningless (51.85%) tags using Micro Average F1 by Tags metric for the French task.
Anthology ID:
I17-4028
Volume:
Proceedings of the IJCNLP 2017, Shared Tasks
Month:
December
Year:
2017
Address:
Taipei, Taiwan
Editors:
Chao-Hong Liu, Preslav Nakov, Nianwen Xue
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
170–173
Language:
URL:
https://aclanthology.org/I17-4028
DOI:
Bibkey:
Cite (ACL):
Dushyanta Dhyani. 2017. OhioState at IJCNLP-2017 Task 4: Exploring Neural Architectures for Multilingual Customer Feedback Analysis. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 170–173, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
OhioState at IJCNLP-2017 Task 4: Exploring Neural Architectures for Multilingual Customer Feedback Analysis (Dhyani, IJCNLP 2017)
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PDF:
https://aclanthology.org/I17-4028.pdf