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Papers

DeftPunk at SemEval-2020 Task 6: Using RNN-ensemble for the Sentence Classification.

2020-12-01 · SEMEVAL 2020 · Jekaterina Kaparina, Anna Soboleva

This paper describes participation in DeftEval 2020 (part of SemEval sharing task competition), and is focused on the sentence classification. Our approach to the task was to create an ensemble of several RNNs combined with fasttext and ELMo embeddings. Results show that various types of models in an ensemble give a performance boost in comparison to standard models. Our model achieved F1-score of 78{\%} for a positive class on the DeftEval dataset.

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SentenceSentence Classification

Methods 이 논문이 사용한 방법론

Tanh Activation 설명 없음
Sigmoid Activation 설명 없음
LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…
fastText fastText embeddings exploit subword information to construct word embeddings. Representations are learnt of character $n$-grams, and words represented as the sum of the…
BiLSTM A Bidirectional LSTM, or biLSTM, is a sequence processing model that consists of two LSTMs: one taking the input in a forward direction, and the other in a backwards…
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 $x$…
ELMo Embeddings from Language Models, or ELMo, is a type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and…

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