It is widely known that hallucination is a critical issue in Simultaneous
Machine Translation (SiMT) due to the absence of source-side information. While
many efforts have been made to enhance performance for SiMT, few of them
attempt to understand and analyze hallucination in SiMT. Therefore, we conduct
a comprehensive analysis of hallucination in SiMT from two perspectives:
understanding the distribution of hallucination words and the target-side
context usage of them. Intensive experiments demonstrate some valuable findings
and particularly show that it is possible to alleviate hallucination by
decreasing the over usage of target-side information for SiMT.

通过对 SiMT 中幻觉的两个角度进行综合分析，即对幻觉词的分布和目标端上下文使用进行理解，研究发现通过减少 SiMT 中目标端信息的过度使用，可以减轻幻觉现象。

关于同时机器翻译中的虚构问题

On the Hallucination in Simultaneous Machine Translation

Simultaneous Machine Translation (SiMT) generates target outputs while
receiving stream source inputs and requires a read/write policy to decide
whether to wait for the next source token or generate a new target token, whose
decisions form a \textit{decision path}. Existing SiMT methods, which learn the
policy by exploring various decision paths in training, face inherent
limitations. These methods not only fail to precisely optimize the policy due
to the inability to accurately assess the individual impact of each decision on
SiMT performance, but also cannot sufficiently explore all potential paths
because of their vast number. Besides, building decision paths requires
unidirectional encoders to simulate streaming source inputs, which impairs the
translation quality of SiMT models. To solve these issues, we propose
\textbf{S}elf-\textbf{M}odifying \textbf{S}tate \textbf{M}odeling (SM$^2$), a
novel training paradigm for SiMT task. Without building decision paths, SM$^2$
individually optimizes decisions at each state during training. To precisely
optimize the policy, SM$^2$ introduces Self-Modifying process to independently
assess and adjust decisions at each state. For sufficient exploration, SM$^2$
proposes Prefix Sampling to efficiently traverse all potential states.
Moreover, SM$^2$ ensures compatibility with bidirectional encoders, thus
achieving higher translation quality. Experiments show that SM$^2$ outperforms
strong baselines. Furthermore, SM$^2$ allows offline machine translation models
to acquire SiMT ability with fine-tuning.

利用自我修改状态建模的同时机器翻译（SM^2）训练范式，通过优化每个状态的决策，确保准确优化政策，并通过前缀采样实现充分探索潜在状态，同时保证与双向编码器兼容，从而提高翻译质量。

自调整状态建模的同时机器翻译

Self-Modifying State Modeling for Simultaneous Machine Translation

Simultaneous machine translation (SiMT) is usually done via sequence-level
knowledge distillation (Seq-KD) from a full-sentence neural machine translation
(NMT) model. However, there is still a significant performance gap between NMT
and SiMT. In this work, we propose to leverage monolingual data to improve
SiMT, which trains a SiMT student on the combination of bilingual data and
external monolingual data distilled by Seq-KD. Preliminary experiments on En-Zh
and En-Ja news domain corpora demonstrate that monolingual data can
significantly improve translation quality (e.g., +3.15 BLEU on En-Zh). Inspired
by the behavior of human simultaneous interpreters, we propose a novel
monolingual sampling strategy for SiMT, considering both chunk length and
monotonicity. Experimental results show that our sampling strategy consistently
outperforms the random sampling strategy (and other conventional typical NMT
monolingual sampling strategies) by avoiding the key problem of SiMT --
hallucination, and has better scalability. We achieve +0.72 BLEU improvements
on average against random sampling on En-Zh and En-Ja. Data and codes can be
found at this https URL.

该研究提出并探索了使用单语数据来提高同时机器翻译的质量，并提出了新的单语抽样策略，以避免 SiMT 中的幻觉问题，实验表明新策略可以显著提高翻译质量。