paper-with-me

홈 › Papers

YNU-HPCC at SemEval-2019 Task 8: Using A LSTM-Attention Model for Fact-Checking in Community Forums

2019-06-01 · SEMEVAL 2019 6 · Peng Liu, Jin Wang, Xue-jie Zhang

We propose a system that uses a long short-term memory with attention mechanism (LSTM-Attention) model to complete the task. The LSTM-Attention model uses two LSTM to extract the features of the question and answer pair. Then, each of the features is sequentially composed using the attention mechanism, concatenating the two vectors into one. Finally, the concatenated vector is used as input for the MLP and the MLP{'}s output layer uses the softmax function to classify the provided answers into three categories. This model is capable of extracting the features of the question and answer pair well. The results show that the proposed system outperforms the baseline algorithm.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Fact Checking

Methods 이 논문이 사용한 방법론

Sigmoid Activation 설명 없음
Tanh Activation 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 $x$…
LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

Similar Papers 제목 키워드 기반

YNU-HPCC at SemEval-2019 Task 6: Identifying and Categorising Offensive Language on Twitter

2019-06-01 · SEMEVAL 2019 6 · Chengjin Zhou, Jin Wang, Xue-jie Zhang

This document describes the submission of team YNU-HPCC to SemEval-2019 for three Sub-tasks of Task 6: Sub-task A, Sub-task B, and Sub-task C. We have submitted four systems to identify and categorise offensive language.…

Deep Learning

YNU-HPCC at SemEval-2018 Task 1: BiLSTM with Attention based Sentiment Analysis for Affect in Tweets

2018-06-01 · SEMEVAL 2018 6 · You Zhang, Jin Wang, Xue-jie Zhang

We implemented the sentiment system in all five subtasks for English and Spanish. All subtasks involve emotion or sentiment intensity prediction (regression and ordinal classification) and emotions determining (multi-lab…

ClassificationDomain AdaptationEnsemble LearningGeneral Classification+4

YNU-HPCC at Semeval-2018 Task 11: Using an Attention-based CNN-LSTM for Machine Comprehension using Commonsense Knowledge

2018-06-01 · SEMEVAL 2018 6 · Hang Yuan, Jin Wang, Xue-jie Zhang

This shared task is a typical question answering task. Compared with the normal question and answer system, it needs to give the answer to the question based on the text provided. The essence of the problem is actually r…

Question AnsweringReading Comprehension

YNU-HPCC at SemEval-2018 Task 12: The Argument Reasoning Comprehension Task Using a Bi-directional LSTM with Attention Model

2018-06-01 · SEMEVAL 2018 6 · Quanlei Liao, Xutao Yang, Jin Wang, Xue-jie Zhang

An argument is divided into two parts, the claim and the reason. To obtain a clearer conclusion, some additional explanation is required. In this task, the explanations are called warrants. This paper introduces a bi-dir…

Ensemble LearningQuestion Answering

YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles

2020-08-24 · SEMEVAL 2020 · Jiaxu Dao, Jin Wang, Xue-jie Zhang

This paper summarizes our studies on propaganda detection techniques for news articles in the SemEval-2020 task 11. This task is divided into the SI and TC subtasks. We implemented the GloVe word representation, the BERT…

ArticlesPropaganda detection