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Palomino-Ochoa at SemEval-2020 Task 9: Robust System based on Transformer for Code-Mixed Sentiment Classification

2020-11-18 · SEMEVAL 2020 · Daniel Palomino, Jose Ochoa-Luna

We present a transfer learning system to perform a mixed Spanish-English sentiment classification task. Our proposal uses the state-of-the-art language model BERT and embed it within a ULMFiT transfer learning pipeline. This combination allows us to predict the polarity detection of code-mixed (English-Spanish) tweets. Thus, among 29 submitted systems, our approach (referred to as dplominop) is ranked 4th on the Sentimix Spanglish test set of SemEval 2020 Task 9. In fact, our system yields the weighted-F1 score value of 0.755 which can be easily reproduced -- the source code and implementation details are made available.

📄 PDF Abstract BibTeX arXiv:2011.09448

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Tasks

Language ModelingLanguage ModellingSentiment AnalysisSentiment ClassificationTransfer Learning

Methods 이 논문이 사용한 방법론

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Tanh Activation 설명 없음
Variational Dropout Variational Dropout is a regularization technique based on dropout, but uses a variational inference grounded approach. In…
Activation Regularization Activation Regularization (AR), or $L\_{2}$ activation regularization, is regularization performed on activations as opposed to weights. It is usually used in conjunction with…
Sigmoid Activation 설명 없음
DropConnect DropConnect generalizes Dropout by randomly dropping the weights rather than the activations with probability $1-p$. DropConnect…
Weight Tying Weight Tying improves the performance of language models by tying (sharing) the weights of the embedding and softmax layers. This…
Embedding Dropout Embedding Dropout is equivalent to performing dropout on the embedding matrix at a word level, where the dropout is broadcast…

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