Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification

Heba Elfardy, Manisha Srivastava, Wei Xiao, Jared Kramer, Tarun Agarwal


Abstract
The ability to automatically and accurately process customer feedback is a necessity in the private sector. Unfortunately, customer feedback can be one of the most difficult types of data to work with due to the sheer volume and variety of services, products, languages, and cultures that comprise the customer experience. In order to address this issue, our team built a suite of classifiers trained on a four-language, multi-label corpus released as part of the shared task on “Customer Feedback Analysis” at IJCNLP 2017. In addition to standard text preprocessing, we translated each dataset into each other language to increase the size of the training datasets. Additionally, we also used word embeddings in our feature engineering step. Ultimately, we trained classifiers using Logistic Regression, Random Forest, and Long Short-Term Memory (LSTM) Recurrent Neural Networks. Overall, we achieved a Macro-Average F-score between 48.7% and 56.0% for the four languages and ranked 3/12 for English, 3/7 for Spanish, 1/8 for French, and 2/7 for Japanese.
Anthology ID:
I17-4009
Volume:
Proceedings of the IJCNLP 2017, Shared Tasks
Month:
December
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
59–66
Language:
URL:
https://aclanthology.org/I17-4009
DOI:
Bibkey:
Cite (ACL):
Heba Elfardy, Manisha Srivastava, Wei Xiao, Jared Kramer, and Tarun Agarwal. 2017. Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 59–66, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification (Elfardy et al., IJCNLP 2017)
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PDF:
https://aclanthology.org/I17-4009.pdf