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BERT at SemEval-2020 Task 8: Using BERT to Analyse Meme Emotions
Our system built using state-of-the-art Transformer-based pre-trained Bidirectional Encoder Representations from Transformers (BERT) performed better compared to baseline models for the two tasks A and C and performed close to the baseline model for task B.
Detecting Sarcasm in Conversation Context Using Transformer-Based Models
Sarcasm detection, regarded as one of the sub-problems of sentiment analysis, is a very typical task because the introduction of sarcastic words can flip the sentiment of the sentence itself.
CodeForTheChange at SemEval-2019 Task 8: Skip-Thoughts for Fact Checking in Community Question Answering
The strengths of the scalable gradient tree boosting algorithm, XGBoost and distributed sentence encoder, Skip-Thought Vectors are not explored yet by the cQA research community.
Affect in Tweets Using Experts Model
We compared the results of our Experts Model with both baseline results and top five performers of SemEval-2018 Task-1, Affect in Tweets (AIT).
Mixture of Regression Experts in fMRI Encoding
We argue that each expert learns a certain region of brain activations corresponding to its category of words, which solves the problem of identifying the regions with a simple encoding model.
