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Your search keyword '"Zhou, Chenyue"' showing total 9 results
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9 results on '"Zhou, Chenyue"'

1. Plausible-Parrots @ MSP2023: Enhancing Semantic Plausibility Modeling using Entity and Event Knowledge

Author

Shen, Chong and Zhou, Chenyue

Subjects
Computer Science - Computation and Language
Abstract

In this work, we investigate the effectiveness of injecting external knowledge to a large language model (LLM) to identify semantic plausibility of simple events. Specifically, we enhance the LLM with fine-grained entity types, event types and their definitions extracted from an external knowledge base. These knowledge are injected into our system via designed templates. We also augment the data to balance the label distribution and adapt the task setting to real world scenarios in which event mentions are expressed as natural language sentences. The experimental results show the effectiveness of the injected knowledge on modeling semantic plausibility of events. An error analysis further emphasizes the importance of identifying non-trivial entity and event types., Comment: 10 pages, 5 figures, 5 tables

Published
2024

8. Building a Catalan-Chinese parallel corpus from Wikipedia for use in machine translation

Author

Zhou, Chenyue and Melero i Nogués, Maite

Subjects
Chinese, Parallel corpus, Machine translation, Low-resource languages, Corpus quality, Data mining, Catalan
Abstract

Treball de fi de màster en Lingüística Teòrica i Aplicada. Directora: Dra. Maite Melero The lack of parallel corpora is one of the biggest challenges hindering progress in Machine Translation for low-resource languages. In this work, we crawl and filter parallel sentences in Catalan and Chinese from Wikipedia in order to compile a parallel corpus of good quality. This paper describes the processes we follow to build the corpus, including mining the text data, computing sentence embeddings, extracting sentence alignment and filtering for better corpus quality. We manually audit the corpus quality based on an error taxonomy. Results show that the automatic filtering we applied makes a great improvement in the quality of our web-crawled corpus. The corpus is later used as training data to finetune a multilingual Machine Translation (MT) system in both CA→ZH and ZH→CA directions. Results show that finetuning with our corpus successfully managed to improve BLEU score in both directions on the Flores-101 public benchmark test sets, which demonstrates the importance of corpus in MT and the quality of our Catalan-Chinese parallel corpus.

Published
2022

9. Elevating nanomaterial optical sensor arrays through the integration of advanced machine learning techniques for enhancing visual inspection of food quality and safety.

Author

Lin, Yuandong, Cheng, Jun-Hu, Ma, Ji, Zhou, Chenyue, and Sun, Da-Wen

Subjects
*FOOD inspection, *SENSOR arrays, *FOOD quality, *OPTICAL sensors, *MACHINE learning, *PESTICIDE residues in food
Abstract

AbstractFood quality and safety problems caused by inefficient control in the food chain have significant implications for human health, social stability, and economic progress and optical sensor arrays (OSAs) can effectively address these challenges. This review aims to summarize the recent applications of nanomaterials-based OSA for food quality and safety visual monitoring, including colourimetric sensor array (CSA) and fluorescent sensor array (FSA). First, the fundamental properties of various advanced nanomaterials, mainly including metal nanoparticles (MNPs) and nanoclusters (MNCs), quantum dots (QDs), upconversion nanoparticles (UCNPs), and others, were described. Besides, the diverse machine learning (ML) and deep learning (DL) methods of high-dimensional data obtained from the responses between different sensing elements and analytes were presented. Moreover, the recent and representative applications in pesticide residues, heavy metal ions, bacterial contamination, antioxidants, flavor matters, and food freshness detection were comprehensively summarized. Finally, the challenges and future perspectives for nanomaterials-based OSAs are discussed. It is believed that with the advancements in artificial intelligence (AI) techniques and integrated technology, nanomaterials-based OSAs are expected to be an intelligent, effective, and rapid tool for food quality assessment and safety control. [ABSTRACT FROM AUTHOR]

Published
2024
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