Modeling Dual Read/Write Paths for Simultaneous Machine Translation (2022)

Abstract: Simultaneous machine translation (SiMT) outputs translation while reading source sentence and hence requires a policy to decide whether to wait for the next source word (READ) or generate a target word (WRITE), the actions of which form a read/write path. Although the read/write path is essential to SiMT performance, no direct supervision is given to the path in the existing methods. In this paper, we propose a method of dual-path SiMT which introduces duality constraints to direct the read/write path. According to duality constraints, the read/write path in source-to-target and target-to-source SiMT models can be mapped to each other. As a result, the two SiMT models can be optimized jointly by forcing their read/write paths to satisfy the mapping. Experiments on En-Vi and De-En tasks show that our method can outperform strong baselines under all latency.

## introduction simultaneous machine translation (simt) @xcite @xcite , which outputs translation while reading source sentence, is important to many live scenarios, such as simultaneous interpretation, live broadcast and synchronized subtitles. different from full-sentence machine translation which waits for the whole source sentence, simt has to decide whether to wait for the next source word (i.e., read action) or translate a target word (i.e., write action) to complete the translation. the sequence of read and write actions in the translation process form a read/write path, which is key to simt performance. improper read/write path will bring damage to translation performance as compared to the following write actions too many but not necessary read actions will result in high translation latency while too few but not sufficient read actions will exclude indispensable source information. therefore, an ideal read/write path is that the read actions compared to the following write actions are just sufficient and necessary, which means the source words covered by consecutive read actions and the target words generated by the following consecutive write actions should be semantically equivalent. ensuring sufficiency and necessity between read/write actions will lead to a proper read/write path and thereby good simt performance. but unfortunately, the existing simt methods, which employ a fixed or adaptive policy, do not consider the sufficiency or necessity in their policy. the fixed policy performs simt based on a pre-defined read/write path @xcite , where the number of read actions before write is fixed. the adaptive policy @xcite @xcite @xcite dynamically decides to read or write guided by translation quality and total latency, but skips the evaluation of sufficiency and necessity between read/write actions. under these grounds, we aim at introducing the evaluation of sufficiency and necessity between read/write actions to direct the read/write path without involving external information. as mentioned above, in an ideal read/write path, the source segment (i.e., source words read by the consecutive read actions) and the corresponding target segment (i.e., target words generated by the following consecutive write actions) are supposed to be semantically equivalent and thus translation to each other, which constitutes a separate segment pair. hence, an ideal read/write path divides the whole sentence pair into a sequence of segment pairs where the source sentence and the target sentence should be translation to each other segment by segment. that means if the translation direction is reversed, an ideal read/write path for target-to-source simt can also be deduced from the same sequence of segment pairs. for example, according to the alignment in figure 1 (a), the ideal read/write paths should be 'rrwww|rw|rw' in @xmath0, as shown in figure 1(b) , both of which share the same segment pair sequence of ⟨fand ich, i fount it⟩, ⟨super, great⟩ and ⟨., .⟩. therefore, agreement on the segment pairs derived from read/write paths in source-to-target and targetto-source simt, called duality constraints, can be a good choice to evaluate sufficiency and necessity between read/write actions. based on the above reasoning, we propose a method of dual-path simt, which uses the simt model in the reverse direction to guide the simt model in the current direction according to duality constraints between their read/write paths. with duality constraints, the read/write paths in sourceto-target and target-to-source simt should reach an agreement on the corresponding segment pairs. along this line, our method maintains a source-totarget simt model and a target-to-source simt model concurrently, which respectively generate their own read/write path using monotonic multi-head attention @xcite . by minimizing the difference between the segment pairs derived from the two read/write paths, the two simt models successfully converge on the segment pairs and provide supervision to each other. experiments on iwslt15 en↔vi and wmt15 de↔en simt tasks show that our method outperforms strong baselines under all latency, including the state-ofthe-art adaptive policy. ## background we first briefly introduce simt with a focus on monotonic multi-head attention @xcite . for a simt task, we denote the source sentence as read/write path can be represented in multiple forms, such as an action sequence of read and write (e.g., rrwwwrw• • • ), or a path from (0, 0) to (i, j) in the attention matrix from the target to source, where moving right (i.e., →) means read action and moving down (i.e., ↓) means write action, as shown in figure 1(b) . mathematically, a read/write path can be represented by a monotonic non-decreasing sequence {g i } i @xmath1, where the g i represents the number of source words read in when writing the i th target word y i . the value of {g i } i @xmath2, where monotonic multi-head attention (mma) @xcite is the current state-of-the-art simt performance via modeling read/write action as a bernoulli variable. monotonic multi-head attention mma processes the source words one by one, and concurrently predicts a selection probability p ij to indicates the probability of writing y i when reading x j , and accordingly a bernoulli random variable z ij is calculated to determine read or write action: if z @xmath3, mma sets g @xmath4. therefore, the decoding probability of y with parameters θ is note that when integrated into multi-head attention, all attention heads in decoder layers independently determine the read/write action. if and only when all heads decide to perform write action, the model starts translating, otherwise the model waits for the next source word. expectation training since sampling a discrete random variable z ij precludes back-propagation, mma applies expectation training @xcite to replace z ij with a expected writing probability, denoted as to trade-off between translation quality and latency, mma introduces a latency loss l g to the training loss: ## the proposed method our dual-path simt model employs a source-totarget (forward) model and a target-to-source (backward) model, called single-path simt, which generate their own read/write path based on mma. according to duality constraints that the read/write paths of the two single-path simt models should share the same segment pair sequence, the two read/write paths should be transposed to each other in principle as shown in figure 1 . but in practice, after transposing one of the read/write paths, there is always a gap between the transposed read/write path and the original one in the reverse translation direction. by closing the gap between the to accomplish the duality constraints, we generate the transposed path of the forward (or backward) read/write path, and use this transposed path to direct the read/write path in another direction; vice versa. ## related work dual learning is widely used in dual tasks, especially machine translation. for both unsupervised @xcite @xcite and supervised nmt @xcite , dual learning can provide additional constraints by exploiting the dual correlation. unlike most previous dual learning work on nmt, which use the reconstruction between source and target sequences, we focus on simt-specific read/write path and explorer its intrinsic properties. simt policy falls into two categories: fixed and adaptive. for fixed policy, the read/write path is defined by rules and fixed during translating. dalvi et al. (2018) rule-based sequences to guide the read/write path @xcite @xcite . however, these methods rely too much on heuristic rules, and thus their performance is not comparable to jointly optimizing read/write path and translation. our method internally explorers the duality between the read/write paths in two directions, and accordingly uses the duality to constrain the read/write paths, thereby obtaining better simt performance. ## analysis we conducted extensive analyses to understand the specific improvements of our method. unless otherwise specified, all results are reported on @xmath5. ## conclusion in this paper, we develop the dual-path simt to supervise the read/write path by modeling the duality constraints between simt in two directions. experiments and analyses we conducted show that our method outperforms strong baselines under all latency and achieves a high-quality read/write path.