Learning Adaptive Segmentation Policy for Simultaneous Translation (2020)

Abstract: Balancing accuracy and latency is a great challenge for simultaneous translation. To achieve high accuracy, the model usually needs to wait for more streaming text before translation, which results in increased latency. However, keeping low latency would probably hurt accuracy. Therefore, it is essential to segment the ASR output into appropriate units for translation. Inspired by human interpreters, we propose a novel adaptive segmentation policy for simultaneous translation. The policy learns to segment the source text by considering possible translations produced by the translation model, maintaining consistency between the segmentation and translation. Experimental results on Chinese-English and German-English translation show that our method achieves a better accuracy-latency trade-off over recently proposed state-of-the-art methods.

## introduction in recent years, simultaneous translation has attracted increasing interest both in research and industry community. it aims at a real-time translation that demands high translation quality and an as-short-as-possible delay between speech and translation output. a typical simultaneous translation system consists of an auto-speech-recognition (asr) system that transcribes the source speech into source streaming text, and a machine translation (mt) system that performs the translation from the source into the target text. however, there is a gap between the output of asr and the input of mt. the mt system takes sentences as input, while the streaming asr output has no segmentation boundaries. therefore, exploring a policy to split asr output into appropriate segments becomes a vital issue for simultaneous translation. if translation starts before adequate source content is delivered, the translation quality degrades. however, waiting for too much source text increases latency. the policies of recent work generally falls into two classes: in this paper, we propose a novel adaptive segmentation policy for simultaneous translation. our method is motivated by two widely used strategies in simultaneous interpretation: source: shàngwǔ diǎn wǒ qùle tàng gōngyuán 上午 10 点 我 去了 趟 公园 morning 10 o'clock i go to park text translation: i went to the park at 10 a.m. source with mu: 上午 10 点 || 我 去了 趟 || 公园 simul. interpretation: at 10 a.m. || i went to || the park. see in our approach, we model the policy as an mu segmentation model, which dynamically splits the streaming text into meaning units. once a meaning unit is detected 1 , it is fed to the mt model to generate translation. the mu segmentation is implemented by a classification model under the pre-training & fine-tuning framework @xcite . as there are no standard training corpora to train the mu segmentation classifier, we propose a novel translation-prefix based method to generate training data. basically, the method detects whether the translation of a sequence of words is a prefix of the full sentence's translation. if so, the sequence is considered as an mu. this makes the segmentation model consistent with the translation model. we further propose a refined method to extract fine-grained mus to reduce latency. experimental results on nist chinese-english and wmt 2015 german-english datasets show that our method outperforms the previous state-ofthe-art methods in balancing translation accuracy and latency. the contributions of this paper can be summarized as follows: ## adaptive segmentation policy our idea is inspired by human interpreters who start translating as soon as they recognize an mu. in this paper, we aim to split the streaming text into mus to get a trade-off between translation quality and latency. see figure 1 for illustration. we model the mu segmentation as a classification problem and train a classifier, which receives a streaming text from asr output and detects whether it constitutes an mu (figure 1 (a) and (b) ). once an mu is detected, it is sent to the mt model to produce translation (figure 1 (c) ). meanwhile, the mu segmentation model keeps receiving source words. to build an mu segmentation model, there are three key issues: to further generate fine-grained mu, we then propose a refined method (section 2.3 ). the model reads more source words and the probability of class 1 is greater than δ now. (c). as soon as an mu is detected, it is sent to the translation model to generate translation. once an mu is translated, the translation will not be changed by the incoming source text. we thus use a force decoding to ensure the monotonic translation. we train the classifier under a pre-training & fine-tuning framework (section 2.4). finally, the mu segmentation model is integrated into a cascaded simultaneous translation system. it receives asr output and produces mus as mt input. ## experiments we carry out experiments on two translation tasks: the nist chinese-english (zh-en) translation task (2m sentences), and the wmt 2015 german-english (de-en) translation task (4.5m sentences).we use bleu @xcite score to evaluate translation quality, and average lagging foot_1 @xcite to measure latency. ## related work recent simultaneous translation work focuses on exploring a policy to decide whether to wait for another source word or generate a target word. @xcite investigated a variety of policies depending on lexical cues. @xcite proposed to optimize a segmentation model with the target of achieving better translation quality. however, their performance is limited largely by weak features such as n-gram and pos. some research learns the policy depending on reinforcement learning, with the goal of good translation quality and low latency @xcite @xcite . but reinforcement learning is notorious for its unstable training process. @xcite proposed a heuristic measure to determine the policy at inference time, without using a deep model. @xcite and @xcite applied fixed policy independent of contextual information, which inevitably need to guess the future context in translation @xcite . some work applied advanced attention mechanisms that replace the softmax attention with a stepwise bernoulli selection probability @xcite . @xcite proposed infinite lookback to integrate the hard monotonic attention with soft attention. @xcite proposed multi-head monotonic attention and obtained further improvements. however, the autoregressive training process makes its exploration inefficient. ## conclusions in this paper, we propose a novel adaptive segmentation policy for simultaneous translation. motivated by human interpreters, the model constantly reads streaming text and dynamically segments it into meaning units. we first generate training data for mu via a translation-prefix based method, keeping consistency between the segmentation model and the translation model. further, we propose a refined-method to extract fine-grained mus to reduce latency. experimental results on both chinese-english and german-english show that our model outperforms the previous state-of-the-art. the method obtains a good trade-off between translation accuracy and latency and can be easily implemented into a practical simultaneous translation system.