Papers by Zhengshan Xue

3 papers
On the Hallucination in Simultaneous Machine Translation (2024.acl-short)

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Challenge: Currently, there are no studies which systematically analyze hallucination in SiMT.
Approach: They conduct a comprehensive analysis of hallucination in simultaneous machine translation (SiMT) they find that halluciation is extremely severe, especially as latency increases .
Outcome: The results show that it is possible to alleviate hallucination by decreasing the over usage of target-side information for SiMT.
CKDST: Comprehensively and Effectively Distill Knowledge from Machine Translation to End-to-End Speech Translation (2023.findings-acl)

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Challenge: End-to-end speech-totext translation (ST) data are limited due to the limited resources.
Approach: They propose a knowledge distillation framework for speech translation that integrates knowledge from machine translation and decouples knowledge from non-target class knowledge.
Outcome: The proposed framework outperforms state-of-the-art models on a benchmark dataset.
InImageTrans: Multimodal LLM-based Text Image Machine Translation (2025.findings-acl)

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Challenge: Existing multimodal large language models suffer from repetition and omission hallucinations when transferred to text image machine translation task.
Approach: They propose an efficient MLLM named InImageTrans for TiMT and a method for advancing it.
Outcome: The proposed method outperforms existing open-source MLLMs on the MCiT benchmark.

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