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Chapter and Conference Paper
Improving Anti-money Laundering via Fourier-Based Contrastive Learning
Anti-money laundering (AML) aims to detect money laundering from daily transactions, which is the key frontier of combating financial crimes. Previous deep-learning AML methods are not robust enough. To addres...
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Chapter and Conference Paper
Improving Low-Resource Chinese Event Detection with Multi-task Learning
Chinese Event Detection (CED) aims to detect events from unstructured sentences. Due to the difficulty of labeling event detection datasets, previous approaches suffer from severe data sparsity problem. To add...
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Chapter and Conference Paper
Unsupervised Cross-Lingual Sentence Representation Learning via Linguistic Isomorphism
Recently, many researches on learning cross-lingual word embeddings...
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Chapter and Conference Paper
Leveraging Multi-head Attention Mechanism to Improve Event Detection
Event detection (ED) task aims to automatically identify trigger words from unstructured text. In recent years, neural models with attention mechanism have achieved great success on this task. However, existin...
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Chapter and Conference Paper
Learning Multilingual Sentence Embeddings from Monolingual Corpus
Learning multi-lingual sentence embeddings usually requires large scale of parallel sentences which are difficult to obtain. We propose a novel self-learning approach which is capable of learning multi-lingual...
