Context-Aware Sarcasm Detection Using BERT

2020-07-01 · WS 2020 7 · Arup Baruah, Kaushik Das, Ferdous Barbhuiya, Kuntal Dey

In this paper, we present the results obtained by BERT, BiLSTM and SVM classifiers on the shared task on Sarcasm Detection held as part of The Second Workshop on Figurative Language Processing. The shared task required the use of conversational context to detect sarcasm. We experimented by varying the amount of context used along with the response (response is the text to be classified). The amount of context used includes (i) zero context, (ii) last one, two or three utterances, and (iii) all utterances. It was found that including the last utterance in the dialogue along with the response improved the performance of the classifier for the Twitter data set. On the other hand, the best performance for the Reddit data set was obtained when using only the response without any contextual information. The BERT classifier obtained F-score of 0.743 and 0.658 for the Twitter and Reddit data set respectively.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Sarcasm Detection

Methods 이 논문이 사용한 방법론

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

Sigmoid Activation 설명 없음

Weight Decay 설명 없음

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 $x$…

Tanh Activation 설명 없음

Adam 설명 없음

LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

Multi-Head Attention 설명 없음

Transformers on Sarcasm Detection with Context

2020-07-01 · ACL 2020 7

Sarcasm Detection with Context, a shared task of Second Workshop on Figurative Language Processing (co-located with ACL 2020), is study of effect of context on Sarcasm detection in conversations of Social media. We prese…

Classification General Classification Sarcasm Detection Sentence+2

iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic

2021-04-01 · EACL (WANLP) 2021 4 · Malek Naski, Abir Messaoudi, Hatem Haddad, Moez BenHajhmida 외

We describe our submitted system to the 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic (Abu Farha et al., 2021). We tackled both subtasks, namely Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subta…

Sarcasm Detection Sentiment Analysis

Affective and Contextual Embedding for Sarcasm Detection

2020-12-01 · COLING 2020 8 · Nastaran Babanejad, Heidar Davoudi, Aijun An, Manos Papagelis

Automatic sarcasm detection from text is an important classification task that can help identify the actual sentiment in user-generated data, such as reviews or tweets. Despite its usefulness, sarcasm detection remains a…

Sarcasm Detection Word Embeddings

KoCoSa: Korean Context-aware Sarcasm Detection Dataset

2024-02-22 · Yumin Kim, Heejae Suh, Mingi Kim, Dongyeon Won 외

Sarcasm is a way of verbal irony where someone says the opposite of what they mean, often to ridicule a person, situation, or idea. It is often difficult to detect sarcasm in the dialogue since detecting sarcasm should r…

Dataset Generation Sarcasm Detection

A Transformer Approach to Contextual Sarcasm Detection in Twitter

2020-07-01 · WS 2020 7 · Hunter Gregory, Steven Li, Pouya Mohammadi, Natalie Tarn 외

Understanding tone in Twitter posts will be increasingly important as more and more communication moves online. One of the most difficult, yet important tones to detect is sarcasm. In the past, LSTM and transformer archi…