Kenneth Kwok


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SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis
Erik Cambria | Qian Liu | Sergio Decherchi | Frank Xing | Kenneth Kwok
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In recent years, AI research has demonstrated enormous potential for the benefit of humanity and society. While often better than its human counterparts in classification and pattern recognition tasks, however, AI still struggles with complex tasks that require commonsense reasoning such as natural language understanding. In this context, the key limitations of current AI models are: dependency, reproducibility, trustworthiness, interpretability, and explainability. In this work, we propose a commonsense-based neurosymbolic framework that aims to overcome these issues in the context of sentiment analysis. In particular, we employ unsupervised and reproducible subsymbolic techniques such as auto-regressive language models and kernel methods to build trustworthy symbolic representations that convert natural language to a sort of protolanguage and, hence, extract polarity from text in a completely interpretable and explainable manner.


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Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition
Joey Tianyi Zhou | Hao Zhang | Di Jin | Hongyuan Zhu | Meng Fang | Rick Siow Mong Goh | Kenneth Kwok
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are investigated to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD). Additionally, adversarial training is adopted to boost model generalization. In experiments, we examine the effects of different components in DATNet across domains and languages and show that significant improvement can be obtained especially for low-resource data, without augmenting any additional hand-crafted features and pre-trained language model.

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Commonsense inference in human-robot communication
Aliaksandr Huminski | Yan Bin Ng | Kenneth Kwok | Francis Bond
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing

Natural language communication between machines and humans are still constrained. The article addresses a gap in natural language understanding about actions, specifically that of understanding commands. We propose a new method for commonsense inference (grounding) of high-level natural language commands into specific action commands for further execution by a robotic system. The method allows to build a knowledge base that consists of a large set of commonsense inferences. The preliminary results have been presented.