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Victor O.K. Li

5 accepted papers

2025

An LLM-based Temporal-spatial Data Generation and Fusion Approach for Early Detection of Late Onset Alzheimer’s Disease (LOAD) Stagings Especially in Chinese and English-speaking Populations

EMNLP 2025

Alzheimer’s Disease (AD), the 7th leading cause of death globally, demands scalable methods for early detection. While speech-based diagnostics offer promise, existing approaches struggle with temporal-spatial (T-S) challenges in capturing subtle linguistic shifts across different disease stages (te

Cited by 0SourcePDFScholar
2021

Lexically Constrained Neural Machine Translation with Explicit Alignment Guidance

AAAI 2021technical

Lexically constrained neural machine translation (NMT), which leverages pre-specified translation to constrain NMT, has practical significance in interactive translation and NMT domain adaption. Previous work either modify the decoding algorithm or train the model on augmented dataset. These methods…

2021

Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet

AAAI 2021technical

Lexically constrained sentence generation allows the incorporation of prior knowledge such as lexical constraints into the output. This technique has been applied to machine translation, and dialog response generation. Previous work usually used Markov Chain Monte Carlo (MCMC) sampling to generate l…

2020

Lexical-Constraint-Aware Neural Machine Translation via Data Augmentation

IJCAI 2020poster

Leveraging lexical constraint is extremely significant in domain-specific machine translation and interactive machine translation. Previous studies mainly focus on extending beam search algorithm or augmenting the training corpus by replacing source phrases with the corresponding target translation.…

2018

Non-Autoregressive Neural Machine Translation

ICLR 2018poster

Existing approaches to neural machine translation condition each output word on previously generated outputs. We introduce a model that avoids this autoregressive property and produces its outputs in parallel, allowing an order of magnitude lower latency during inference. Through knowledge distillat…