Hang Yuan


YNU-HPCC at Semeval-2018 Task 11: Using an Attention-based CNN-LSTM for Machine Comprehension using Commonsense Knowledge
Hang Yuan | Jin Wang | Xuejie Zhang
Proceedings of the 12th International Workshop on Semantic Evaluation

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 reading comprehension. Typically, there are several questions for each text that correspond to it. And for each question, there are two candidate answers (and only one of them is correct). To solve this problem, the usual approach is to use convolutional neural networks (CNN) and recurrent neural network (RNN) or their improved models (such as long short-term memory (LSTM)). In this paper, an attention-based CNN-LSTM model is proposed for this task. By adding an attention mechanism and combining the two models, this experimental result has been significantly improved.


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.