Anticipation-Free Training for Simultaneous Machine Translation
This addresses a key challenge in simultaneous machine translation for real-time applications, offering an incremental improvement over existing methods.
The paper tackles the problem of long-distance reordering causing aggressive anticipation and hallucinations in simultaneous machine translation by proposing a framework that decomposes translation into monotonic and reordering steps, using an auxiliary sorting network, and reports outperforming previous methods with lower latency.
Simultaneous machine translation (SimulMT) speeds up the translation process by starting to translate before the source sentence is completely available. It is difficult due to limited context and word order difference between languages. Existing methods increase latency or introduce adaptive read-write policies for SimulMT models to handle local reordering and improve translation quality. However, the long-distance reordering would make the SimulMT models learn translation mistakenly. Specifically, the model may be forced to predict target tokens when the corresponding source tokens have not been read. This leads to aggressive anticipation during inference, resulting in the hallucination phenomenon. To mitigate this problem, we propose a new framework that decompose the translation process into the monotonic translation step and the reordering step, and we model the latter by the auxiliary sorting network (ASN). The ASN rearranges the hidden states to match the order in the target language, so that the SimulMT model could learn to translate more reasonably. The entire model is optimized end-to-end and does not rely on external aligners or data. During inference, ASN is removed to achieve streaming. Experiments show the proposed framework could outperform previous methods with less latency.