Simultaneous Neural Machine Translation with Constituent Label Prediction (2021)

Abstract: Simultaneous translation is a task in which translation begins before the speaker has finished speaking, so it is important to decide when to start the translation process. However, deciding whether to read more input words or start to translate is difficult for language pairs with different word orders such as English and Japanese. Motivated by the concept of pre-reordering, we propose a couple of simple decision rules using the label of the next constituent predicted by incremental constituent label prediction. In experiments on English-to-Japanese simultaneous translation, the proposed method outperformed baselines in the quality-latency trade-off.

## introduction simultaneous machine translation is a task in which the machine starts outputting a translation before reading the entire input sentence. this task is more difficult than full-sentence translation because it translates the initial part of a sentence without the context of the latter part. this involves a trade-off between delay and quality of the translation; using a longer context should improve translation quality at the cost of a longer delay, and vice versa. in practice, we should control the latency so that it's not too large, but we may also need to allow a long latency depending on the situation. most of the recent simultaneous translation models @xcite @xcite @xcite @xcite @xcite @xcite are based on neural machine translation (nmt), although earlier studies were based on statistical machine translation @xcite @xcite . in simultaneous nmt, there are two major approaches: those in which a latency hyperparameter is given before the training and those in which it is given at the time of inference. the former approach requires training a model individually for each pre-defined latency setting, while the latter approach uses a single model for different latency conditions. most human simultaneous interpreters would not need such long training to slightly adjust latency, while it takes much more time to learn other languages to develop their translation skill. therefore, the latter approach is closer to the learning process of human simultaneous interpreters. there is another method in the latter approach that uses meaningful unit @xcite . in this model, chunk-based incremental decoding is done at inference time by segmentation with a boundary predictor. this model outperformed baselines of the former approach. they refined their basic boundary predictor to deal with sentence pairs in which full-sentence translation needs longdistance reordering. however, its training process is very complicated: it first generates monotonic translations, fine-tunes the nmt model with them, then generates an oracle boundary with the model, and finally fine-tunes a boundary-prediction model based on bert @xcite . simultaneous translation is still difficult for language pairs such as english-japanese, which often require long-distance reordering. to tackle the reordering problem, we propose an inputsegmentation method for simultaneous translation, using a couple of simple rules and incremental prediction of the label of a syntactic constituent coming immediately after the input existing so far. this source sentence i bought a pen. monotonic translation watashi wa katta pen wo. full-sentence translation watashi wa pen wo katta. our proposed method is inspired by head finalization @xcite . head finalization reorders words of the source sentence before translating from an svo (subject-verb-object) language to an sov language in full-sentence statistical machine translation. this method moves a syntactic head into a later position so that the word order of the source language (e.g., english) becomes similar to that of the target language (e.g., japanese). this enables us to monotonically translate from english, which is a typical svo language, to japanese, a typical sov language. recent nmt models like transformer @xcite works well on reordering in general, so this kind of pre-reordering is not usually used. however, simultaneous translation monotonically reads input words one by one, and therefore the difference in word order remains a problem. as shown in in experiments on english-to-japanese simultaneous translation, the proposed method outperformed baselines in the quality-latency trade-off. ## related work in statistical machine translation, there are several approaches to finding boundaries of segments for simultaneous translation. @xcite proposed a method to choose segment boundaries that maximize the bleu score. rangarajan @xcite proposed segmentation strategies based on lexical cues. in nmt, there have been many studies on simultaneous translation. the amount of latency is decided either before training or at inference time. wait-k @xcite is the simplest variant using fixed latency: it simply waits for k tokens before starting translation @xcite . the latency policy can be learned from a parallel corpus together with an nmt model. milk @xcite and other approaches @xcite used a latency-augmented loss function in training to balance latency and accuracy. in contrast, the latency policy can be learned with a pre-trained nmt model, such as test-time wait-k @xcite and static-rw @xcite . these have fixed policies that wait for the fixed number of tokens before translation, but there are other models that learn a more flexible policy for a given pre-trained nmt model. some studies use reinforcement learning to learn an adaptive read/write policy @xcite @xcite . training by reinforcement learning can be unstable depending on the condition. one method that does not use reinforcement learning is wait-if-* @xcite , which translates and segments jointly to maximize the translation quality. @xcite extended wait-k to an adaptive policy by adaptively choosing the strategy at inference. there is another method that generates oracle read/write actions by a pre-trained nmt model and predicts actions using a neural network model @xcite . meaningful unit @xcite works along the same lines and has outperformed baselines such as milk and wait-k. with respect to the use of syntactic clues for simultaneous translation, @xcite proposed a method to incrementally parse an incomplete sentence by predicting unseen syntactic constituents on the right and left side of each segment. they concatenated the predicted constituents and the words in a segment and then input the result into tree2string translation. they decided to wait for more tokens or output the translation depending on where the constituents appear in the translation result. our proposed method is based on chunk-based simultaneous translation using chunk boundary detection with simple rules on next-constituent labels. it basically segments an input before a verb phrase. this is much simpler and easier to implement than the work by @xcite and @xcite . ## proposed method figure 1 shows a step-by-step example of our proposed method described in this section. ## conclusion we proposed a novel segmentation method for simultaneous translation that uses simple rules and iclp. our proposed method is simple, and it outperformed the baselines in the quality-latency trade-off in en-ja translation. on the other hand, the proposed method did not work effectively in en-de translation due to the smaller word order differences than those in en-ja translation. in future work, we expect to extract segmentation rules automatically and apply these rules to other language pairs as well.