P s F uture: A Pseudo-Future-based Zero-Shot Adaptive Policy for Simultaneous Machine Translation (2024)

Abstract: Simultaneous Machine Translation (SiMT) requires target tokens to be generated in real-time as streaming source tokens are consumed. Traditional approaches to SiMT typically require sophisticated architectures and extensive parameter configurations for training adaptive read/write policies, which in turn demand considerable computational power and memory. We propose PsFuture, the first zero-shot adaptive read/write policy for SiMT, enabling the translation model to independently determine read/write actions without the necessity for additional training. Furthermore, we introduce a novel training strategy, Prefix-to-Full (P2F), specifically tailored to adjust offline translation models for SiMT applications, exploiting the advantages of the bidirectional attention mechanism inherent in offline models. Experiments across multiple benchmarks demonstrate that our zero-shot policy attains performance on par with strong baselines and the P2F method can further enhance performance, achieving an outstanding trade-off between translation quality and latency.

## introduction simultaneous machine translation (simt) @xcite is required to generate target tokens concurrently as it processes incoming source tokens. differing from traditional machine translation (mt) methods @xcite @xcite that access the full source text, simt necessitates a read/write (r/w) policy to decide between emitting target tokens or awaiting more source input, coupled with the ability to translate from source prefixes to target prefixes (p2p) @xcite . typically, the read/write policy is integrated with the translation mechanism: either employing a fixed wait-k policy alongside a corresponding translation model @xcite @xcite , or utilizing an adaptive policy @xcite @xcite @xcite @xcite @xcite that dynamically adjusts read/write decisions based on the context, in conjunction with a model trained to translate policy-defined prefixes. this adaptive method has led to superior performance @xcite ), yet it demands specialized architectural solutions and multitask learning frameworks for concurrent training of the closely linked adaptive policy and translation model, complicating component optimization and increasing computational demands. on the other hand, dap-simt @xcite ) introduces a novel framework that separates the adaptive read/write policies from the translation model, offering greater versatility in simultaneous translation. this approach demonstrates that translation models, when directed by an effective adaptive read/write policy, even if initially trained on fixed policies, can balance quality and latency well, achieving state-of-the-art (sota) outcomes. however, akin to other adaptive policies, it requires intricate designs and a significant parameter set for training the adaptive read/write policy, often demanding substantial computational resources and memory. we introduce psfuture, a zero-shot adaptive read/write policy based on pseudo-future information. this policy utilizes the inherent capabilities of the translation model itself to make read/write decisions without additional training. similar to the policy in dap-simt @xcite , we draw inspiration from human simultaneous translation @xcite , where interpreters shift from listening to translating upon anticipating that further future words would not impact figure 1: an @xmath0. even when provided with additional possible future information, the probability distribution of the predicted next token does not change significantly, remaining dominated by the token "to". therefore, based on the current source prefix "我想吃" and the current target prefix "i want," a write operation can be executed to predict the next token as "to". the proposed psfuture method can be directly applied to most existing simultaneous translation models, such as the multi-path wait-k model, which demonstrates superior performance when directed by effective adaptive policies @xcite . additionally, we investigate the application of the psfuture method to offline translation models. previous simt models @xcite ) conventionally employ a unidirectional attention encoder with tailored maskedcross-attention for prefix-to-prefix training. this approach, while efficient, limits the model's ability to extract features, making it less adept in high-latency scenarios compared to offline models that utilize bidirectional attention mechanisms. to leverage the benefits of bidirectional attention in simt, we introduce a novel and effective training technique, prefix-to-full (p2f), designed to enhance the performance of offline translation models under diverse latency conditions. our main contributions can be summarized as follows. ## related work simt policies are broadly categorized into fixed and adaptive schemes. fixed policies @xcite @xcite execute read/write actions following predefined rules, such as the wait-k policy @xcite , which after reading k source tokens, alternates between reading and writing one token. conversely, adaptive policies dynamically determine read/write actions based on the evolving source and target context, enhancing the balance between translation accuracy and latency. adaptive approaches employ methods like reinforcement learning within a neural machine translation (nmt) framework @xcite , incremental decoding for variable target token output @xcite , and attention-based methods @xcite ., simultaneous translation model simultaneous translation model x 2 x 1 y 1 y 2 y 1 y 2 cosine distance x 2 x 3 x 1 x 4 compared with 𝜆 next token distribution1 next token distribution2 read / write figure 2: an overall schematic of the psfuture policy. based on the current source prefixes (x 1 , x 2 ), target prefixes (y 1 , y 2 ), and pseudo future information (x 3 , x 4 ) (tokens highlighted in red), the simultaneous translation model can directly perform adaptive read/write decisions. ## analysis in this scetion, we aim to provide a detailed examination of the proposed method. unless otherwise noted, the psfuture-related experiments are based on the multi-path wait-k model, and the results stem from the @xmath1. 6.1 effect of the pseudo-future suffix this part investigates the influence of various pseudo-future suffixes ( furthermore, experiments with ground truth suffixes are conducted to ascertain the upper bound of the psfuture method. the results indicate that there remains potential for enhancement. future efforts will focus on incrementally approaching this upper limit by exploring and refining suffixes. ## conclusion in this paper, we propose the first zero-shot adaptive read/write policy for simt, psfuture. it empowers the translation model to autonomously decide on read/write actions without requiring additional training and can attain effectiveness on par with previously meticulously trained adaptive policies. moreover, we introduce a novel training strategy, prefix-to-full (p2f), specifically tailored to adjust offline translation models for simt applications, exploiting the benefits of the bidirectional attention mechanism inherent in offline models.