Adaptive Policy with Wait-k Model for Simultaneous Translation (2023)

Abstract: Simultaneous machine translation (SiMT) requires a robust read/write policy in conjunction with a high-quality translation model. Traditional methods rely on either a fixed wait-k policy coupled with a standalone wait-k translation model, or an adaptive policy jointly trained with the translation model. In this study, we propose a more flexible approach by decoupling the adaptive policy model from the translation model. Our motivation stems from the observation that a standalone multi-path wait-k model performs competitively with adaptive policies utilized in state-of-the-art SiMT approaches. Specifically, we introduce DaP, a divergence-based adaptive policy, that makes read/write decisions for any translation model based on the potential divergence in translation distributions resulting from future information. DaP extends a frozen wait-k model with lightweight parameters, and is both memory and computation efficient. Experimental results across various benchmarks demonstrate that our approach offers an improved trade-off between translation accuracy and latency, outperforming strong baselines.

## introduction simultaneous machine translation (simt) @xcite poses a unique challenge as it generates target tokens in real-time while consuming streaming source tokens, mostly applying to the scenario of speech translation @xcite @xcite . unlike traditional machine translation (mt) @xcite where the entire source is available, simt requires a read/write (r/w) policy to determine whether to generate target tokens or wait for additional source tokens, along with the ability to trans- * work was done during libo zhao's research internship at damo academy, alibaba group. on the other hand, the multi-path wait-k approach proposed by @xcite introduces an effective method for training prefix-toprefix translation models by randomly sampling dif-ferent k values between batches. intuitively, as depicted in figure 1 , any read/write path determined by an adaptive policy can be composed of subpaths from various wait-k paths with different k values, which can be effectively translated by a welltrained multi-path wait-k model. although the performance of the multi-path wait-k approach falls behind the adaptive counterparts, we argue that this discrepancy should be attributed to the wait-k policy rather than the translation model. notably, our observations indicate that multi-path wait-k models can achieve competitive results when combined with the adaptive policy proposed in @xcite . this suggests a decoupled modular approach where the adaptive read/write policy can be modeled and optimized separately from the translation model, offering increased flexibility and the potential for improved performance. a key aspect of this approach lies in acquiring high-quality signals to effectively supervise the learning of the read/write policy model. we draw inspiration from human simultaneous translation @xcite , where interpreters make a switch from listening to translating once they have gathered enough source context @xmath0. in other words, they anticipate that seeing additional future words would not impact their current decisions. this behavior implies a small discrepancy between the interpreters' modeling of translation distribution given the partial source context p(y t |@xmath1, @xmath2, and the translation distribution given the full source context p(y t |@xmath3, x). conversely, interpreters would wait for more source words if the discrepancy becomes significant. this observation motivates the utilization of statistical divergence @xcite between the two conditional distributions for any prefix-to-prefix pair given a translation model as an informative criterion for making read/write decisions. in light of this, we propose dap-simt, a novel divergencebased adaptive policy for simultaneous translation, to enable adaptive simultaneous translation using estimated divergence values, considering that the full source context is unavailable during the translation process. while there are various options of neural architectures for the policy model and the translation model, we choose to build upon a well-trained multi-path wait-k translation model with frozen parameters, and introduce additional lightweight parameters for the adaptive policy model. this design choice minimizes the memory and computation overhead introduced by the policy model, while providing an effective mechanism to achieve an adaptive read/write policy within an existing simt model. our main contributions can be summarized as follows. ## related works existing simt policies are mainly classified into fixed and adaptive categories. fixed policies @xcite @xcite determine read/write operations based on predefined rules. for example, the wait-k policy @xcite first reads k source tokens and then alternates between writing and reading one token. on the other hand, adaptive policies predict read/write operations based on the current source and target prefix, achieving a better balance between latency and translation quality. reinforcement learning has been used by @xcite to learn the policy within a neural machine translation (nmt) model. @xcite designed an incremental decoding that outputs a varying number of target tokens. meanwhile, @xcite and @xcite presented approaches to learning the adaptive policy through attention mechanisms. recent advancements like the waitinfo policy @xcite and itst @xcite have quantified the waiting latency and information weight respectively to devise adaptive policies. to the best of our knowledge, itst is currently the state-of-the-art method in simt. one of the most relevant works to ours is the meaningful unit (mu) for simultaneous translation @xcite . this approach detects whether the translation of a sequence of source multi-path wait-k model (frozen) encoder layer x n decoder layer x @xmath4. this method was further generalized to speech translation in mu-st @xcite . while mu-st inspects the target prefix in the vocabulary domain, our work advances further by examining the distribution of target tokens. ## conclusion in this paper, we introduce a divergence-based adaptive policy for simt, which makes read/write decisions based on the potential divergence in translation distributions resulting from future information. our approach extends a frozen multi-path wait-k translation model with lightweight parameters for the policy model, making it memory and computation efficient. experimental results across various benchmarks demonstrate that our approach provides an improved trade-off between translation accuracy and latency compared to strong baselines. we hope that our approach can inspire a novel perspective on simultaneous translation.