CAUnLP at NLP4IF 2019 Shared Task: Context-Dependent BERT for Sentence-Level Propaganda Detection

2019-11-01 · WS 2019 11 · Wenjun Hou, Ying Chen

The goal of fine-grained propaganda detection is to determine whether a given sentence uses propaganda techniques (sentence-level) or to recognize which techniques are used (fragment-level). This paper presents the sys- tem of our participation in the sentence-level subtask of the propaganda detection shared task. In order to better utilize the document information, we construct context-dependent input pairs (sentence-title pair and sentence- context pair) to fine-tune the pretrained BERT, and we also use the undersampling method to tackle the problem of imbalanced data.

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Propaganda detectionSentence

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Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Weight Decay 설명 없음

Residual Connection 설명 없음

Adam 설명 없음

Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Attention 설명 없음

Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

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