Lijunyi at SemEval-2020 Task 4: An ALBERT Model Based Maximum Ensemble with Different Training Sizes and Depths for Commonsense Validation and Explanation

2020-12-01 · SEMEVAL 2020 · Junyi Li, Bin Wang, Haiyan Ding

This article describes the system submitted to SemEval 2020 Task 4: Commonsense Validation and Explanation. We only participated in the subtask A, which is mainly to distinguish whether the sentence has meaning. To solve this task, we mainly used ALBERT model-based maximum ensemble with different training sizes and depths. To prove the validity of the model to the task, we also used some other neural network models for comparison. Our model achieved the accuracy score of 0.938(ranked 10/41) in subtask A.

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Multi-Head Attention 설명 없음

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

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

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 $x$…

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

Attention 설명 없음

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Lee at SemEval-2020 Task 5: ALBERT Model Based on the Maximum Ensemble Strategy and Different Data Sampling Methods for Detecting Counterfactual Statements

2020-12-01 · SEMEVAL 2020 · Junyi Li, Yuhang Wu, Bin Wang, Haiyan Ding

This article describes the system submitted to SemEval 2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals. In this task, we only participate in the subtask A which is detecting counterfactual …

counterfactual

Lijunyi at SemEval-2019 Task 9: An attention-based LSTM and ensemble of different models for suggestion mining from online reviews and forums

2019-06-01 · SEMEVAL 2019 6 · Junyi Li

In this paper, we describe a suggestion mining system that participated in SemEval 2019 Task 9, SubTask A - Suggestion Mining from Online Reviews and Forums. Given some suggestions from online reviews and forums that can…

Suggestion mining

ReCAM@IITK at SemEval-2021 Task 4: BERT and ALBERT based Ensemble for Abstract Word Prediction

2021-04-04 · SEMEVAL 2021 · Abhishek Mittal, Ashutosh Modi

This paper describes our system for Task 4 of SemEval-2021: Reading Comprehension of Abstract Meaning (ReCAM). We participated in all subtasks where the main goal was to predict an abstract word missing from a statement.…

Language Modeling Language Modelling Masked Language Modeling Reading Comprehension

Deep Learning Brasil - NLP at SemEval-2020 Task 9: Sentiment Analysis of Code-Mixed Tweets Using Ensemble of Language Models

2020-12-01 · SEMEVAL 2020 · Manoel Ver{\'\i}ssimo dos Santos Neto, Ayrton Amaral, N{\'a}dia Silva, Anderson da Silva Soares

In this paper, we describe a methodology to predict sentiment in code-mixed tweets (hindi-english). Our team called verissimo.manoel in CodaLab developed an approach based on an ensemble of four models (MultiFiT, BERT, A…

Sentiment Analysis

YMAI at SemEval-2022 Task 5: Detecting Misogyny in Memes using VisualBERT and MMBT MultiModal Pre-trained Models

2022-07-01 · SemEval (NAACL) 2022 7 · Mohammad Habash, Yahya Daqour, Malak Abdullah, Mahmoud Al-Ayyoub

This paper presents a deep learning system that contends at SemEval-2022 Task 5. The goal is to detect the existence of misogynous memes in sub-task A. At the same time, the advanced multi-label sub-task B categorizes th…

Deep Learning