Combine different word embeddings

How to take advantage of different word embeddings in text classification task? Please check my Kaggle post: https://www.kaggle.com/c/quora-insincere-questions-classification/discussion/71778

A subset of this field is called meta-embedding. Here is a list of papers: https://github.com/Shujian2015/meta-embedding-paper-list

I found that just taking average of different embeddings is already powerful enough.

One thing to try is BERT: https://gluebenchmark.com/leaderboard

Papers to Start with

Some papers I really love:

  • Zhang Zhi, et al. “Bag of Freebies for Training Object Detection Neural Networks.” arXiv preprint arXiv:1902.04103 (2019).
  • Xie, Junyuan, et al. “Bag of Tricks for Image Classification with Convolutional Neural Networks.” arXiv preprint arXiv:1812.01187 (2018).
  • Howard, Jeremy, and Sebastian Ruder. “Universal language model fine-tuning for text classification.” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vol. 1. 2018.
  • Smith, Leslie N. “A disciplined approach to neural network hyper-parameters: Part 1–learning rate, batch size, momentum, and weight decay.” arXiv preprint arXiv:1803.09820 (2018).
  • Chahal, Karanbir, Manraj Singh Grover, and Kuntal Dey. “A Hitchhiker’s Guide On Distributed Training of Deep Neural Networks.” arXiv preprint arXiv:1810.11787 (2018).
  • Neishi, Masato, et al. “A bag of useful tricks for practical neural machine translation: Embedding layer initialization and large batch size.” Proceedings of the 4th Workshop on Asian Translation (WAT2017). 2017.
  • Joulin, Armand, et al. “Bag of tricks for efficient text classification.” arXiv preprint arXiv:1607.01759 (2016).
  • Covington, Paul, Jay Adams, and Emre Sargin. “Deep neural networks for youtube recommendations.” Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.
  • He, Xinran, et al. “Practical lessons from predicting clicks on ads at Facebook.” Proceedings of the Eighth International Workshop on Data Mining for Online Advertising. ACM, 2014.
  • McMahan, H. Brendan, et al. “Ad click prediction: a view from the trenches.” Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2013.