Huizhi Liang

Also published as: HuiZhi Liang


2020

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UoR at SemEval-2020 Task 4: Pre-trained Sentence Transformer Models for Commonsense Validation and Explanation
Thanet Markchom | Bhuvana Dhruva | Chandresh Pravin | Huizhi Liang
Proceedings of the Fourteenth Workshop on Semantic Evaluation

SemEval Task 4 Commonsense Validation and Explanation Challenge is to validate whether a system can differentiate natural language statements that make sense from those that do not make sense. Two subtasks, A and B, are focused in this work, i.e., detecting against-common-sense statements and selecting explanations of why they are false from the given options. Intuitively, commonsense validation requires additional knowledge beyond the given statements. Therefore, we propose a system utilising pre-trained sentence transformer models based on BERT, RoBERTa and DistillBERT architectures to embed the statements before classification. According to the results, these embeddings can improve the performance of the typical MLP and LSTM classifiers as downstream models of both subtasks compared to regular tokenised statements. These embedded statements are shown to comprise additional information from external resources which help validate common sense in natural language.

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UoR at SemEval-2020 Task 8: Gaussian Mixture Modelling (GMM) Based Sampling Approach for Multi-modal Memotion Analysis
Zehao Liu | Emmanuel Osei-Brefo | Siyuan Chen | Huizhi Liang
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Memes are widely used on social media. They usually contain multi-modal information such as images and texts, serving as valuable data sources to analyse opinions and sentiment orientations of online communities. The provided memes data often face an imbalanced data problem, that is, some classes or labelled sentiment categories significantly outnumber other classes. This often results in difficulty in applying machine learning techniques where balanced labelled input data are required. In this paper, a Gaussian Mixture Model sampling method is proposed to tackle the problem of class imbalance for the memes sentiment classification task. To utilise both text and image data, a multi-modal CNN-LSTM model is proposed to jointly learn latent features for positive, negative and neutral category predictions. The experiments show that the re-sampling model can slightly improve the accuracy on the trial data of sub-task A of Task 8. The multi-modal CNN-LSTM model can achieve macro F1 score 0.329 on the test set.

2016

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UNIMELB at SemEval-2016 Tasks 4A and 4B: An Ensemble of Neural Networks and a Word2Vec Based Model for Sentiment Classification
Steven Xu | HuiZhi Liang | Timothy Baldwin
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures
Timothy Baldwin | Huizhi Liang | Bahar Salehi | Doris Hoogeveen | Yitong Li | Long Duong
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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RoseMerry: A Baseline Message-level Sentiment Classification System
Huizhi Liang | Richard Fothergill | Timothy Baldwin
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)