You Zhang


MA-BERT: Learning Representation by Incorporating Multi-Attribute Knowledge in Transformers
You Zhang | Jin Wang | Liang-Chih Yu | Xuejie Zhang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


YNU-HPCC at SemEval-2018 Task 1: BiLSTM with Attention based Sentiment Analysis for Affect in Tweets
You Zhang | Jin Wang | Xuejie Zhang
Proceedings of the 12th International Workshop on Semantic Evaluation

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-labels classification). The useful BiLSTM (Bidirectional Long-Short Term Memory) model with attention mechanism was mainly applied for our system. We use BiLSTM in order to get word information extracted from both directions. The attention mechanism was used to find the contribution of each word for improving the scores. Furthermore, based on BiLSTMATT (BiLSTM with attention mechanism) a few deep-learning algorithms were employed for different subtasks. For regression and ordinal classification tasks we used domain adaptation and ensemble learning methods to leverage base model. While a single base model was used for multi-labels task.


YNU-HPCC at IJCNLP-2017 Task 5: Multi-choice Question Answering in Exams Using an Attention-based LSTM Model
Hang Yuan | You Zhang | Jin Wang | Xuejie Zhang
Proceedings of the IJCNLP 2017, Shared Tasks

A shared task is a typical question answering task that aims to test how accurately the participants can answer the questions in exams. Typically, for each question, there are four candidate answers, and only one of the answers is correct. The existing methods for such a task usually implement a recurrent neural network (RNN) or long short-term memory (LSTM). However, both RNN and LSTM are biased models in which the words in the tail of a sentence are more dominant than the words in the header. In this paper, we propose the use of an attention-based LSTM (AT-LSTM) model for these tasks. By adding an attention mechanism to the standard LSTM, this model can more easily capture long contextual information.

YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction
You Zhang | Hang Yuan | Jin Wang | Xuejie Zhang
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

In this paper, we present a system that uses a convolutional neural network with long short-term memory (CNN-LSTM) model to complete the task. The CNN-LSTM model has two combined parts: CNN extracts local n-gram features within tweets and LSTM composes the features to capture long-distance dependency across tweets. Additionally, we used other three models (CNN, LSTM, BiLSTM) as baseline algorithms. Our introduced model showed good performance in the experimental results.