Adaptive Policy with Wait-k Model for Simultaneous Translation
Authors: Libo Zhao
Kai Fan
Wei Luo
Wu Jing
Shushu Wang
Ziqian Zeng
Zhongqiang Huang
Venue: EMNLP 2023 Main (2023)
Keywords: simultaneous machine translation
wait-k
adaptive policy
read/write supervision signals
TL;DR: We find the frozen multi-path wait-k model equipped with a plug-in read/write policy net trained with our proposed adaptive read/write supervision signals can achieve better accuracy-latency trade-off.

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.

Adaptive Policy with Wait-k Model for Simultaneous Translation
Libo Zhao' 1. Kai Fan', Wei Luo", Jing Wu', Shushu Wang',
Ziqian Zeng't, Zhongqiang Huang2t
Shinn-Ming Wu School of Intelligent Engineering, South China University of Technology
2 Alibaba DAMO Academy, 3ZheJiang University
*Deparmeent of Computing. Hong Kong Polytechnic University
wilbzhao@mai il 1. scut. edu. cn wangshushu@213ezJJ0 edu cn, zqzeng@scut. edu. cn (k. fan, muzuuo. 1w, wj334275, z. huang inc. com
Abstract
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Simultaneous machine translation (SiMT) re- quires a robust read/write (R/W) policy con- junction with high-quality translation model. Traditional methods rely on either fixed wait- k policy coupled with standalone wait-k translation model, or adaptive policy jointly trained with the translation model. In this study, we propose more flexible approach by decou- pling the adaptive policy model from the trans- lation model. Our motivation stems from the observation that standalone multi-path wait- k model performs competitively with adaptive policies utilized state-of-the- of-tth-rrt SiMT ap- proaches. Specifically. introduce DaP. divergence-based adaptive policy, that makes read/write decisions for any translation model based on the potential divergence in transla- tion distributions resulting from future infor- mation. DaP extends a frozen wait-k model with lightweight parameters, and is both mem- ory and computation efficient. Experimental results across various benchmarks demonstrate that our approach offers an improved trade-off' between translation accuracy and latency, out- performing strong baselines.
wail-2 path
Y1
wall-3 path
adaptive path
Y/2
y5
log Plgllta
Log ((s)
ys
Y6
Figure 1: An example demonstrating that the optimiza- tion of cross-entropy loss on an adaptive path can achieved through multi-path wait-k training. Note that any adaptive path can be composed of subpaths (high- lighted by the green and blue circles) derived from vari- ous wait-k paths.
late from source prefixes to target prefixes (P2P) (Ma al., 2018). Conventionally, the read/write policy and the translation model are designed work in tandem: a simple wait-k policy with an offline or wait-k translation model (Ma et al., 2018; Elbayad et al., 2020; Zhang et al., 2021b), or an adaptive policy (Gu et al., 2017: Dalvi et al., 2018; Zheng et al., 2019, 2020; Ma et al., 2020a; Guo et al., 2023) that dynamically makes read/write decisions based on context, paired with a trans- lation model that learns to translate the prefixes determined by the policy. The latter approach has achieved sfate-of-the-ard results (Zhang and Feng, 2022, 2023). However, it entails dedicated architec- ture designs and multitask learning to jointly train tightly coupled adaptive policy and the translation model in order to balance translation quality and latency, resulting in computational complexity and challenges in optimizing individual components.
1 Introduction
Simultaneous Machine Translation (SiMT) (Gu et al., 2017) poses unique challenge as generates target tokens in real-time while consuming stream- ing source tokens, mostly applying to the scenario of speech translation (Zhang et al., 2019, 2023; Fu et al., 2023). Unlike traditional machine transla- tion (MT) (Bahdanau et al., 2015; Vaswani al., 2017) where the entire source is available, SiMT requires a read/write (R/W) policy to determine whether to generate target tokens or wait for addi- tional 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 ap- proach proposed by (Elbayad al., 2020) intro- duces an effective method for training prefix-Ui -to- prefix translation models by randomly sampling dif-
Corresponding author.
'The code is available at https:://githbb com/ 1bzhac976/DaP-SiMe

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ferent & values between batches. Intuitively, de- picted in Figure I, any read/write path determined by an adaptive policy can be composed of sub- paths from various wait-k paths with different val- ues, which can be effectively translated by a well- trained multi-pate pathh wait-k model. Although the per- formance of the multi-path wait-k approach falls behind the adaptive counterparts, 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 com- bined with the adaptive policy proposed in (Zhang and Feng, 2022). This suggests a decoupled modu- lar 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.
parameters, and introduce additional lightweight parameters for the adaptive policy model. This design choice minimizes the memory and compu- tation 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 sum- marized follows.
argue that
We propose novel method to construct read/write supervision signals from a paral- lel training corpus based on statistical diver- gence.
2.
We present a lightweight policy model that is both memory and computation efficient and enables adaptive read/write decision-making for well-trained multi-path wait-k transla- tion model.
3. Experiments conducted multiple bench- marks demonstrate that approach outper- forms strong baselines and achieves a superior eccuracy-latens trade-off.
A key aspect of this approach lies in acquir-
ing high-quality signals to effectively supervise the learning of the read/write policy model. We draw inspiration from human simultaneous trans- lation (AI-Khanj) et al., 2000; Liu, 2008), where interpreters make a switch from listening to trans- lating once they have gathered enough source con- text x<((t) to determine how to expand the partial translation Y<t to produce the next target word 3/t- In other words, they anticipate that seeing addi- tional future words would not impact their current decisions. This behavior implics a small discrep- ancy between the interpreters' modeling of trans- lation distribution given the partial source context p(y:yy<s <t, x and the translation distribution given the full source context p(yely<c,, x). Con- versely, interpreters would wait for more source words if the discrepancy becomes significant.
Related Works
Existing SiMT policies are mainly classified into fixed and adaptive categories. Fixed policies (Ma et al., 2018; Elbayad et al., 2020; Zhang et al., 2021b) determine read/write operations based on predefined rules. For example, the wait-k policy (Ma et al., 2018) first reads l: 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 be- tween latency and translation quality. Reinforce- ment learning has been used by (Gu et al., 2017) to learn the policy within Neural Machine Transla- tion (NMT) model. Dalvi et al. (2018) designed an incremental decoding that outputs a varying num- ber of target tokens. Mcanwhile, Arivazhagan al. (2019) and Ma et al. (2020a) presented approaches to learning the adaptive policy through attention mechanisms. Recent advancements like the wait- info policy (Zhang et al., 2022b) and ITST (Zhang and Feng. 2022) have quantified the waiting latency and information weight respectively to devise adap- tive policies. To the best of our knowledge, ITST is currently the state-of-the-art method in SiMT.
This observation motivates the utilization of sta- tistical divergence (Leee, 1999) between the two conditional distributions for any prefix-t--prefix pair given translation model as an informative criterion for making read/write decisions. In light of this, we propose DaP-SiMT, a novel divergence- based adaptive policy for simultaneous translation, to enable adaptive simultaneous translation using estimated divergence values, considering that the full source context is unavailable during the trans- lation process.
While there are various options of neural archi- tectures for the policy model and the translation model, we choose to build upon a well-trained multi-path wait-k translation model with frozen
One of the most relevant works to ours the Meaningful Unit (MU) for simultaneous transla- tion (Zhang et al., 2020). This approach detects whether the translation of a sequence of source

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Any Independent R/W Policy
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Mulri-path Wait-k Model (Frozen)
Linear Layer
Softmax
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Mp
Source Embodding Layer
Target Embeekding Layer
FIST
ITST- I-S--gddded Mp Whit-k
22
Yv
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12
AL
Figure 2: The combination of multi-path wait-k model with any independent read/write policy model for simul- taneous translation inference.
Figure 3: Comparison of BLEU vs. AL curves between multi-path (abbreviated as Mp) wait-k, ITST, and ITST. guided multi-path wait-k.
tokens forms prefix of the full sentence's trans- lation. This method was further generalized to speech translation in MU-ST (Zhang et al., 2022a), While MU-ST inspects the target prefix in the cabulary domain, work advances further by examining the distribution of target tokens.
Multi-path Wait-k (Elbayad et al., 2020) is efficient technique for wait-k training. It randomly samples different & values between batches during model optimization. Concretely, the loss of one training batch is computed as follows,
3 Preliminary
Uniform(A.)
(4)
3.1 Full-sentence MT and SIMT
S
log Logp(uxxsg(ta)) yet),
In the context of full sentence translation task, given translation pair (I1, ..., ... zN) and y (y1, 02..02....,.1 Vr), an encoder-decoder model such as Transformer (Vaswani et al., 2017) maps x into hidden representations and then autore- gressively decodes the target tokens. Generally, the model is optimized by minimizing the cross- entropy loss.
where is the candidate set of k. The main advan- tage of this method its ability to make inferences under different latencies with single model. Ad- ditionally, by adopting the unidirectional encoder, it can cache the encoder hidden states of stream- ing input. Previous experiments have shown that its performance is comparable to multiple wait-k models trained with different & values.
Lit Σ logp (yr x. y()
(1)
Method
For the SiMT task, given that g(t) monotonic non-decrggg function representing the end times- tamp of the source prefix that must be consumed to generate the f-th target token, the objective function of SiMT can be modified as follows,
4.1 Motivation
Typically, a fixed k value is used when performing inference with multi multi-path wait-k model, follow- ing the wait-k read/write path. As exemplified in Figure 1, any adaptive read/write path can be com- posed of subpaths of various wait-k paths. Given that a multi-path wait-k model trained perform trefix-to-preffe translation for different k values, we argue that such a model can also achieve com- petitive performance when used with an adaptive read/ /rad/write policy.
Lximl
Σ
1=1 log (yr xx(tt)- (2)
3.2 Wait-k Policy and Multi-Path Wait-k
Wait-k policy (Ma et al., 2018), the most widely used fixed policy, begins by reading source to- kens and then alternates between writing and read- ing one token. The function g(t) for the wait-k policy can be formally calculated as,
To evaluate this hypothesis, we construct a SiMT system by combining multi-path wait-k model with the adaptive policy from ITST (Zhang and Feng, 2022), as shown in Figure Although ITST
g(t; k) = min(t + N..
(3)

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ATT
READ WRITE
Machine
E-514
200
2.001
3.0
0.
02
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R/W Policy Net
Regression
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5602
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help
Multi-path Whit-k Model (Frozen)
Linear Layer
people
E.500
1.077
2.500
1.
1051
02
20
0.
1oam
Encoder Layer x
Decoder Layer
languages E.91
1.611
3.91
3866
1991
3.01
3.
Source Fibbeeding Layer
Target Embedding Layer
Figure 4: Example of En divergence matrix D using cosine distance, where Di.((t) = D (p: purt l) A potential read/write path is indicated by the ele- ments in the matrix and can be determined based on predefined threshold (0.2 in this case).
Figure 5: Architecture of the DaP-SiM1 approach. It equivalent adding an extra decoder layer to the original SiMT. The output of the extra decoder will be fed into a regression head to determine read/write action.
has an integrated translation module, we only uti- lize its policy module to make read/write decisions and rely on the multi-path wait-k model for transla- tion. As illustrated in the BLEU/AL (BLEU Score vs. Average Lagging) curves in Figure the re- sulting ITST-guided multi-path wait-k approach achieves competitive performance all latency settings. It consistently outperforms the original multi-path wait-k approach. When compared to the original ITST approach, the combined approach achieves significantly better BLEU scores in the low latency region while obtaining comparable re- sults in mid-to-high latency settings.
other given full source:
pi pant ply: (x
(5)
p
full = p(u: = y
(6)
where the distributions can be computed using a well-trained offline translation model simultane- ous translation model. this paper, we quantify D(.,)) with three different divergence measures?:
Fuclidean:
IIp Pt full
KL-divergence:
KL (ppet [|p,
This positive observation leads to a natural ques- tion: Can we develop a better adaptive policy for multi-path wait-k model?
Cosine distance: cos (p,
(artt Pt
We utilize the divergence measures automati- cally construct read/ wrad/write supervisions from par- allel corpus used for MT training. For each parallel sentence pair, we compute divergence matrix D, where each element Di.(()) = D fflll). for all possible prefix-to-pree pairs. We can then make read/write decisions by comparing Dz.(()) with a threshold X:
4.2 Divergence-based Read/Write Supervision
Learning an adaptive policy requires high-quality read/write supervision training data. It is unfeasi- ble to collect manual annotations due to both the cost and complexity of the task. Instead, we draw inspiration from human simultaneous translation and propose to create such data automatically. We consider that good write action should only occur when the partial source information is sufficient make accurate translations, i.e., the translations should be similar to that with the complete source input. To be precise, we want to quantify the diver- gence D (p, part full) between two distributions, one computed given partial source input and an-
write if Dig(t) < X, else read
(7)
Figure shows an example divergence ma- trix and highlighted read/write path. Varying the threshold would result in different latency. For each chosen threshold, we could construct
AAlhhough cosine distance does not meet the indiscernible condition (d(a, h) = + a = b) of statistical divergence for general vectors, it does for prohability distribution vectors)

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read/write samples and train separate read/write policy model, however, the resulting model would be specific for that threshold. Instead, we treat the divergence measures computed from parallel sentences as ground-truth values, and train a sin- gle adaptive read/write policy model to predict the ground-truth values from the partial source and target pair:
tion quality and latency. The inference process of DaP-SiMT summarized in Algorithm D in the Appendix.
5
Experiments
5.1
Datasets
WMT2022 Zh-=En', We use a subset with 25M sentence pairs for training*. We first tokenize the Chinese and English data using the Jieba Chinese Segmentation Tool and Moses", respectively, and then apply BPE with 32,000 merge operations. We employ a validation set of 956 sentence pairs from BSTC (Zhang et al., 2021a) the test set.
(x (xE) R/W Policy Net
D
(t((t)
(8)
4.3 SIMT with Adaptive Policy
The architecture of our proposed DaP-SiMT model, as depicted in Figure 5, integrates a SiMT model with a divergence- baaeed adaptive read/write pol- icy network. In contrast to the design shown in Figure 2, which employs independent policy, approach incorporates policy network that leverages the hidden states from the SiMT model's encoder and decoder as inputs, tailored explicitly to enable adaptive read/write decisions within the SiMT model.
WMT15 De>>En'. All 4.5M sentence pairs from this dataset are used for training, and are tok- enized using 32K BPE merge operations. We use newstest2013 (3000 sentence pairs) for validation and report results on newstest2015 (2169 sentence pairs).
IWSLT15 En-+Vi All 133K sentence pairs from this dataset (Luong and Manning, 2015) used for training. We use TED tst2012 (1553 sentence pairs) for validation and TED tst2013 (1268 sen- tence pairs) as the test set. Following the settings (Ma et al., 2020b), we adopt word-level tokeniza- tion and replace rare tokens (frequency < 5) with <unk>. The vocabulary sizes are 17K for English and 7.7K for Vietnamese, respectively.
More specifically, the policy network includes additional transformer decoder layer placed atop the original SiMT decoder, followed by a regres- sion head responsible for predicting divergence values. The incorporation of an extra functional decoder layer aligns with the common practices found in previous works on NMT (Li et al., 2022, 2023). The design of the regression head adheres to Roberta (Liu et al., 2019), featuring two lin- car layers with tanh activation function sand- wiched in between. In terms of the learning objec- tive, we employ Mean Squared Error (MSE) for divergence measures based on Euclidean distance or KL-divergence, and binary cross-entropy with continuous labels for measures based on cosine distance.
5.2 Settings
All our implementations are based on the Trans- former (Vaswani et al., 2017) architecture and adapted from the Fairseq Library (Ott et al., 2019). For the Zn experiments, former big architecture, while the base and small architectures used for De- and En ex- periments respectively. We use the cosine distance to calculate the read/write supervision signals the main experiments, and investigate the effects of different divergence types in Section 5.4.2.
utilize the trans-
During training, we only tune the parameters of the adaptive policy network while keeping the parameters the multi-path wait-k model fixed. the inference phase, we compare the predicted divergence values with a predefined threshold to make read/write decisions, following Equation 7, and can achieve varying latency levels by adjusting the threshold. Additionally, we have empirically observed that introducing another hyper-parameter limit the maximum number of continuous READ operations for certain languages (see analysis in Section 5.4.2 for the impact on different language pairs) results in a better balance between transla-
For evaluation, following ITST (Zhang and Feng, 2022), report case insenssiiei BLEU (Papineni et al., 2002) scores to assess translation quality and Average Lagging (AL/token) (Ma al., 2018) measure latency. Regarding the maximum num-
Pann statnt, org/wmt22
"The data sources include casia2015, casier201 casie12015, datum2015, datum2217, neu2017, News Commeentary V16, ParaCrawl V9.
2 hhttps: //github. thub con/fxsjy/jiee
6 'https://githur con/roses snt
www. www.nt.ttttoooooo org//mt115 8. hhlp.stanfor sdt/prrjjcts/nnt

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BLEU
Mp
Mp
Mp
24
13
ITST guided Mp Wankk
IEST guided Mp
IIST guided Mp
(a) Zh-=n, En, Transformer-B Big
(b) De-EE, Transformer Base
(c) En-Vi, Transformer Small
Figure 6: Comparison of BLUE vs. AL curves between multi-path (abbreviated as Mp) wait-k, ITST, ITST-guided
multi-path wait-k, and our proposed DaP-SiMT approach on three language pairs.
ber of continuous read actions in our method, empirically select the perffrringg configura- tions, which are no constraint, 4, no constraint for Zh, De-En, En- respectively.
4.2
Wail-E
ITST
DaP APMT Prediction
3.8
Dal SiMI Ground Trath
5.3 Main Results
The performance of our method is compared to previous approaches on three language pairs Fig- ure 6.
10
First, it is evident that the performance of multi- path wait-k model can be significantly improved when guided by an adaptive read/write policy like ITST, compared to using a fixed wait-k policy. This enhanced performance often closely matches or even surpasses that of ITST, the previous state-of- the-art SiMT model, particularly in low latency set- tings for De-+En translation. These results under- score the competitiveness and flexibility of prefix- to-prefix translation within the multi-path wait-k model, a potential that remains largely untapped with fixed wait-k policy.
AL
Figure 7: NLL vs. AL curves comparing four read/write policies utilizing the same SiMT model. Ground Truth" indicates read/write paths derived from ground truth divergence values calculated using the full sentence, while "DaP-SiMT Prediction" based on divergence values predicted by the policy model.
like ITST, which features a closely integrated adap- tive policy and translation model.
5.4 Analysis
Secondly, our proposed DaP-SiMT approach sig- nificantly enhances the performance of the multi- path wait-k model, outperforming all other ap- proaches. The divergence-basee adaptive pol- icy consistently surpasses the fixed wait-k policy across all latency levels. Furthermore, when com- pared to the adaptive policy in ITST, it achieves comparable results in the De >nn scenario and superior performance in the Zn and En- Vi translations, all while using the same multi-path wait-k model as the translation model. This result suggests that the divergence-bart only surpasses the fixed wait-k policy but also com- petes effectively with state-of-fe oftte-----h---- the-art approaches
In our analysis, we aim to provide a more in-depth understanding of our proposed approach. Unless otherwise stated, results are based on the Zh->En Transformer Big model.
5.4.1 The NLL vs. AL Curve
In addition to the commonly used BLEU vs. AL curves that assess the quality of complete transla- tions across different latency levels, introducc novel evaluation metric: the NLL AL curve. This metric enables qualitative measurement of the average impact of various read/write policies on translation quality at each translation step, all while utilizing the same translation model. To construct
approach not

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19 Zi-La
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17
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Exira Decoder Layer
Mi
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(a) Effect of translation model
(b) Effect of the divergence measures
(c) Effect of decoder layers
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(d) Effect of the constraint on the maximum number of continuous read
Figure 8: Ablation studies the proposed DaP-SiMT method
the NLL vs. AL curve, we begin with read/write policy and the necessary hyper-parameters, such as k for the fixed wait-k approach and the threshold A for the divergence-basee adaptive policy, which controls the latency level. Given parallel sen- tence, first derive the read/write path, denoted under the read/write policy, and then calculate the negative log-likelln likeliiokhoo of the translation along the read/write path, follow- ing Eq. (2). By aggregating these NLL scores and their corresponding latency levels across an entire dataset, we can generate NLL vs. AL curves for any read/write policy.
model is more adept at accurately predicting the correct translation along the resulting read/write paths. Furthermore, the DaP-SiMT approach ex- hibits lower NLL curve compared to ITST's pol- icy model, aligning with the trends observed in the BLEU AL curves depicted in Figure 6. Notably, the DaP-SiMT variant employing ground truth di- vergence values has much lower curve than its counterpart based on predicted divergence values. This suggests that there is potential for further im- provement through better modeling of divergence supervision signals.
g(1),
5.4.2 Ablation Study
Figure provides a comparative analysis of NLL VS. AL curves for four read/write policies: wait- k. ITST, and two variations of DaP-SiMT. The first DiP-SiMT variant is based on divergence val- ues predicted by the policy model, while the sec- ond relics ground truth divergence values com- puted using full sentences. The results underscore the effectiveness of our DaP-SiMT approach, consistently yields substantially lower NLL. scores when compared to the fixed wait-k policy at equiv- alent latency levels. This confirms that the SiMT
Effect of translation model for divergence su- pervision In main experiment, utilized an offline translation model to caleulate the diver- gence supervision values. Here, we assess the im- pact of utilizing a SiMT model for this purpose. Figure 8(a) illustrates that supervision signals com- puted by the multi-path wait-k model result in al- most identical performance to those obtained from the offline model.
Effect of the divergence measures
We exam-

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ine the influence of various divergence measures, including Euclidean distance, KL-divergence, and cosine distance, on the performance of DaP-SiMT.
As depicted in Figure 8(b), the almost identical curves suggest that our approach is not sensitive to the choice of divergence measures.
15
Effect of the number of layers for the policy
Dal SiMI Ground Truth DuP-SIMT Prodiction
net To examine whether single additional de- coder layer can effectively model the divergence supervision signals, we conducted comparative ex- periments using either or 3 additional decoder layers. Figure 8(c) demonstrates that configura- tions with or 3 additional decoder layers yield similar results. Although the configuration with O additional decoder layers does not perform as strongly, it still manages to achieve reasonable balance between accuracy and latency.
2
10
Figure 9: BLEU vs. AL curves comparing between DaP-SiMT with ground truth divergence and standard DaP-SiMT with predicted divergence.
the policy model. Specifically, we substitute the model-predicted divergence scores with the ground truth divergence calculated using the complete sen- tence D (p: -p:")) during DaP-SiMT inference, following the procedure outlined in Algorithm D. As illustrated in Figure 9, in line with the find- ings from Section 5,4.1, the upper bound perfor- mance of DaP-SiMT is substantially higher than that achieved with the learned policy model. This observation highlights the potential for further im- provement in policy modeling.
Effect of the max continuous READ con- straint As discussed Section 4.3, we introduce a constraint on the maximum number continu- ous reads during inference, forcing a write action after reaching the limit. Figure 8(d) shows that this constraint has varying impacts different lan- guage pairs. In the cases of Zn and En- Vi, this hyperparameter has minimal influence re- sults, especially in the low-latency region, which is a primary focus in SiMT. However, for De En, substantial improvement is observed with the intro- duction of this constraint.
5.4.4 Examples
We provide several examples in Appendix A com- paring the divergence matrix predicted by the learned policy model with the ground truth. Al- though there are some discrepancies, the predicted divergence matrix closely resembles the ground truth matrix and can be used to make reasonable read/write decisions.
We hypothesize that this difference is related to the modeling difficulty for language pairs with varying degrees of word order variations. As quan- tified in Appendix B, the De->En translation direc- tion exhibits the highest anticipation rate among the three language pairs and demonstrates the most significant divergence in word order (Wang et al., 2023). Consequently, it naturally requires more read actions for an accurate De-+En translation, which is reflected in the distribution of divergence supervision signals and subsequently influences the learned policy model. By adjusting the threshold A achieve low latency, we inadvertently exacerbate the negative effects of exposure bias, resulting in excessive reads, as observed in our experiments. Introducing the maximum number of continuous reads serves as an ad-hoc solution to address this challenge, and we leave it to future research to investigate this issue thoroughly.
Conclusion
In this paper, we introduce divergence- -based adaptive policy for SiMT, which makes read/write decisions based on the potential divergence in trans- lation distributions resulting from future informa- tion. Our approach extends a frozen multi-path wait-k translation model with lightweight param- eters 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 strong baselines. We hope that our approach can inspire novel per- spective on simultaneous translation.
5.4.3 Upper Bound DaP-SiMT
We evaluate the upper bound performance of DaP- SiMT to study the impact of modeling errors within

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Limitations
493-499, New Orleans, Louisiana, Association for Computational Linguistics.
Our evaluation primarily focused on assessing the impact of the proposed adaptive policy on simul- tancous translation using BLEU vs. AL and NLL vs. AL curves. However, acknowledge that intrinsic evaluations of the policy model itself are lacking, and further investigation in this area nec- essary to guide improvements. We provided only a limited exploration of modeling variations for the policy model, leaving room for more in-depth analysis and enhancements. It's worth noting that while the threshold parameter À controls latency, it doesn't 't have direct one-to-one relationship with latency, is the case with the fixed wait-k policy. This nuanced aspect requires careful consideration in future investigations.
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Ethics Statement
After careful review, to the best of our knowledge, we have not violated the ACL Ethics Policy.
1, Long Papers, pages 1053-1062, Valencia, Spain. Association for Computational Linguistics.
Acknowledgements
Shoutao Guo, Shaolei Zhang, and Yang Feng. 2023. Learning optimal policy for simultaneous machine translation via binary search. arXiv preprint arXiv:2305.12774
We would like to thank all the anonymous review- ers for the insightful and helpful comments. This work was supported by Guangzhou Basic and Ap- plied Basic Research Foundation (2023404T1687), and Alibaba Group through Alibaba Research In- tern Program.
Lillian Lee. 1999. Measures of distributional similarity. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguisties, pages 25- 32, College Park, Maryland, USA. Association for Computational Linguistics.
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---

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Xutai Ma, Juan Miguel Pino, James Cross, Liezl Pu- zon, and Jiatao Gu. 2020a. Monotonic multihead attention. In International Conference on Learning Representations.
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---

A Divergence Matrix Examples
Divergence Type
Fuclidcan Distance
KI. Divergence
Cosine Distance
Divergence matrix examples are shown Fig- ure 10, Figure 11, Figure 12.
AL
Threshold
AL
Threshold
AL
Threshold
0,67
0.5
0.72
1.8
1.18
0.52
1.47
0,4
1.37
1,4
1.85
0.4
2.1
0.33
2.21
1.0
2.8
0.26
B Anticipation Rate
3,51
0.24
3,03
0.7
3.72
0.18
4.67
0.2
4.07
0.5
4.54
0.14
Anticipation occurs in simultaneous translation
5.53
0.18
4.94
0.4
5.85
0.1
when target word generated prior to the re-
6.73
0.16
6.32
0.3
6.83
0.08
ceipt of its corresponding source word. To detect
7.42
0.15
7.61
0.24
8.36
0.06
9.04
0.13
8.56
0.2
10.71
0.04
instances of anticipation, accurate word alignment between the paired sentences is needed. Fast-align (Dyer et al., 2013) is employed to obtain the word aligmment between a source sentence X and target sentence Y. The resulting alignments com- prise of source-target word index pairs (s, l). where the 8th source word Is aligns with the gt target word yt- A target word Yt k-anticipated (Ax(t, a) - 1) it aligns to at least one source word Vg where 2 t + k:
10.01
0.12
9.88
0.16
Table 1: The sensitivity of AL the threshold during
inference based on different settings of divergence types.
Experiment
k
k
=
k ==
5
h: =
En
30.4
15.2
8.5
Zh-+En
25.4
12
6.3
En-+V
17.3
5.2
1.9
0.8
Table 2: Anticipation rates (AR%) of different language pairs
A)(1,) 1[((s,1) als 1+ Ø)
Algorithm 1: SiMT inference with DaP
Input:
streaming source tokens: X ggj. threshold: 5,
The k-anticipation rate (ARx) of an (X, Y, a) triple is further defined as follow:
target idx: i 6 1,
source idx: j <- 1,
AR o.
at
max continuous READ constraint:
Y
Times
current number of continuous
The anticipation rates (AR%) of different language pairs are shown in Table 2, from which can find that the De->En translation task exhibits greater word order differences than the other two cases.
READ: Ta <-
Output: target tokens: Y <- (<BOS>}
while Y, <EOS> do
2 using the R/W decision mentioned in 4.3:
calculate R/W confidence with Y;-1
How Sensitive Is The AL To Thresholds During Inference?
if c S or re 2 Tmar then
Table exhibits the sensitivity of the AL to thresh- olds during inference based on different settings of
4
translate with X Y S if * <EOS> or j 2 [X| then // execute WRITE action
5
divergence types. It can be observed that the AL is not particularly sensitive to the threshold over- all, which makes the process of determining the threshold straightforward.
Y Append((y))
Te 0:
i 1;
else
D Algorithm
11 execute READ action
The inference process DaP-SiMT is summarized in Algorithm
j <- +1;
11
14
else
E
Numerical Results
15
// execute READ action
j + 1;
The numerical results presented in Table Ta-
ble 4, Table
+ Te
return Y

---

(city) kit
(oi)
(night))
(view)
(vary) 18
(besutiful)
())
X +
62
The
night
view
0.979
0.972
0.003
0.0
0.0
0.872
0.012
0.017
0.002
0.004
0.0
0.002
0.0
0.989
0.032
0.083
0.001
0.0
0.0
0.0
0.95
0.977
0.922
0.998
0.999
0.0
0.0
0.0
beautiful
0.884
0.718
0.808
0.848
0.762
0.147
0.003
0.0
0.003
0.158
0.008
0.131
0.387
0.884
0.834
0.0
(a) Ground Truth Matrix
(this)
(city)
(of)
(night)
(view)
(very)
(beautifful)
()
1x
@
1R
The
0.344
0.285
night
view
0.096
0.023
0.001
0.026
0.001
city-
0.05
0.0
0.917
0.959
0.945
0.966
0.962
0.035
0.005
0.0
beautiful-
0.899
0.886
0.894
0.898
0.905
0.513
0.082
0.006
0.273
0.452
0.367
0.589
0.71
0.721
0.784
0.003
(b) Prediction Matrix
Figure 10: Example

---

(ead)) (elessiiccation) X
(natural (bingunge) (prooma))
(o0
(anda)
Omportani/app wes))
99 98
E #
18
AL-2
89
FB SEH2
Text
classification
1.0
0.244
0.082
0.041
0.000
0.023
0.003
0.004
0.002
0.002
0.0
an
0.987
0.928
0.995
0.908
0.887
0.952
0.923
0.54
0.01
0.002
0.0
important-
0.198
0.987
0.35
0.768
application
0.984
0.988
0.988
0.982
0.978
0.978
0.982
0.98
0.983
0.004
0.0
0.993
0.994
of- 0.991
0.986
0.98
0.958
0.777
0.621
0.34
0.028
0.089
0.021
0.0
0.986
0.997
0.0
janguage
0.284
0.116
0.287
0.788
9.004
0.0
0.0
0.0
0.0
processing
0.994
0.993
0.896
0.01
0.022
0.126
0.529
0.893
0.814
0.625
0.793
(a) Ground Truth Matrix
Bexd) (elassificatio)
(naturin (language) (prooosa)
(oft
(anva)
@mportantX
23 St
A @2
38
AL-2
89
MM
12 QUA
Text
0.480
0.51
0.15
0.105
0.083
0.084
0.085
0.043
0.028
0.018
0.001
classification
0.181
0.064
0.888
0.868
0.364
0.053
0.032
0.031
0.033
0.008
0.067
0.004
0.0
3m
0.932
0.919
0.308
important
0.882
0.844
0.983
0.914
0.889
0.878
0.85
0.75
0.004
0.001
0.0
application
0.778
0.9
0.924
0.862
0.412
0.291
0.386
0.322
0.246
0.258
0.018
0.0
of
natural
0.771
0.889
0.949
0.403
0.137
0.113
0.048
0.015
0.01
0.005
0.0
language
processing
0.643
0.764
0.765
0.53
0.900
0.065
0.031
0.116
0.612
0.004
0.0
(b) Prediction Matrix
Figure Example

---

(machiee
(ranndation)
(haa) 2
(become) 68
(hurmen) (ranslation) A,
(or)
(frpodtantjeuxllayy
(teh
$128 @
80
96 R22
I R
Machine
translation
0.998
0.277
0.078
0.0
0.004
0.008
0.001
0.001
0.0
0.0
0.0
has
become-
0.80
0.78
0.805
0.002
0.002
0.002
0.001
0.001
0.0
0.0
0.0
0.781
important-
0.751
0.618
0.974
0.988
0.000
0.958
0.994
0.0
0.0
0.0
0.0
auxiliary
0.982
0.984
0.964
tool-
0.877
0.882
0.92
0.916
0.518
0.586
0.833
0.716
0.957
0.0
0.0
0.148
0.003
human-
0.881
0.902
0.854
0.902
0.278
0.974
0.017
0.004
0.002
0.0
0.985
0.121
0.1
..800
0.409
0.561
0.565
0.582
0.914
(a) Ground Truth Matrix
imachina (ransiation)
(bocoma) 18,73
(hamani (ranslation)
(fo)
(arportant)/auxlllyy
(toon
60 B
28 39
AII
89
38 10.32
I ML
Machine-
0,883
0.158
0.075
0.041
0.033
0.028
0.035
0.02
0.017
0.915
0.001
Iranslation-
0.073
0.088
0.029
has 0.929
0.873
0.238
0.062
0.031
0.023
0.019
0.009
0.008
0.007
0.0
become-
0.011
0,711
0.623
0.738
0.622
0.658
0.613
0.69
0.251
0.128
0.056
0.0
imporlant-
0.836
audilarr
0.814
0.794
0.791
0.812
0.801
0.764
0.789
0.779
0.061
0.015
0.0
tool-
for
0.84
0.941
0.811
0.84
0.754
0.455
0.341
0.066
0.034
0.015
0.0
human
translation
0.698
0.446
0.554
0.619
0.654
0.123
0.051
0.015
0.01
0.008
0.001
(b) Prediction Matrix
Figure Example

---

Main Resalts (Figare 6)
Mp Wait-k
ITST
ITST guided Mp Wait-k
DaP-SiMT
AL
BLEU
AL
BLEU
AL
BLEU
AL
BLEU
1.31
11.7
0.7
8.91
0.86
12.06
1.18
13.07
2.23
13.46
1.46
11.92
2.35
14.94
1.85
14.67
2.96
14.37
2.16
14.35
3,58
16.45
2.8
16.7
7hh+Fi En
3.87
15.15
2.76
15.55
4.45
17.46
3.72
17.25
4.76
16.34
3.5
17.06
5.38
18.22
4.54
17.73
5.63
16.98
4.27
17.72
6.98
18.11
5.06
18.14
6.45
17.61
4.79
17.95
8.92
18.55
5.85
18.19
7.27
17.87
5.74
18.07
13.52
19.06
6.83
18.76
8.09
18.05
6.82
18.63
8.36
18.88
8,82
18.54
7.66
18.58
10.71
18.9
9.56
18.45
8.74
18.61
10.26
18.55
9.96
18.75
10.9
18.55
19.15
11.46
18.76
AL
BLEU
AL
BLEU
AL
BLEU
AL
BLEU
0.47
21.08
1.57
19.2
0.49
22.15
0.49
21.65
1.45
23.97
2.17
24.71
1
24.03
1.3
24.51
2.12
26.21
2.77
28.26
1.56
25.98
2.17
27.12
De-XEn EEn
3.12
27.15
3.31
28.85
2.6
28.04
3.25
29.19
28.53
4.01
29.55
3.28
28.81
4.31
29.97
5,05
29.16
4.82
30.35
3.98
29.57
5.87
30.84
6.03
5.66
30.52
4.79
7.65
31.29
6.97
6.65
30.91
5.71
30.71
8.98
31,52
30.69
7.7
31.05
6.66
31.07
10.53
31,6
8.78
8.73
31.08
7.67
31.22
12.53
31.79
9.7
31.11
9.79
31.2
8.78
10.57
12.6
31.32
31.45
11.42
31.41
12.65
31.72
12.24
31.41
AL
BLEU
AL
BLEU
AL
BLEU
AL
BLEU
27.87
1.29
23.06
1.3
22.71
0.89
21.89
1.85
26.33
1.92
26.14
1.41
27.11
30.11
2.44
28.7
2.52
28.59
1.99
29.31
En-Vi
5.57
3.23
29.37
3.35
29.74
3.06
29.63
3.76
29.5
4.51
29.95
4.6
30.15
4.42
29.48
5.23
29.95
5,44
30.09
8.12
30.14
5.15
29.79
5.92
29.95
30.13
8.93
30.11
5.91
29.83
6.81
29.98
7.49
30.15
9.7
30.1
6.7
29.94
7.72
29.91
8.08
30.2
10.43
30.2
7.69
29.95
8.71
29.98
8.74
30.17
11.13
30.16
8.67
29.84
9.95
30.07
9.61
30.01
11.79
30.13
9.93
29.95
12.55
30.09
10.67
30.11
12.41
30.16
12.58
30.01
11.69
30.1
13.01
30.18
NLL curves (Figure 7)
Wait-k
IIST
DiP-SiMT prediction
DiM- Ground Truth
AL
NLL
AL
AL
NL.
0.549
4.105
0.076
4.066
0.004
3.956
0.096
3.761
1.507
3.798
0.778
3.801
0.767
3,703
0.828
3.524
2,466
3.591
1,514
3.559
1.557
3.472
1.542
3.329
Zh Fn En
3.401
3.427
2.228
3.354
2.28
3.307
2.346
3.182
4.323
3.295
2.955
3.228
3.097
3.17
3.054
3.083
5.234
3,205
3.813
3.112
3.888
3.081
3.924
3.02
6.112
3.128
4.718
3.031
4,738
3.019
4.646
2.987
6,958
3.068
5.6
2.978
6.005
2.964
5.885
2.948
7.774
3.024
6.36
2.952
7.013
2.935
7.164
2.924
8.564
2.987
7.259
2.928
8.378
2.908
9.541
2,903
9,318
2.963
8.162
2.914
10.67
2.886
10.021
2,938
8.868
2.905
11.276
2.912
9.779
2.896
11.027
2.885
Table 3: Numerical results in Figure 6 and Figure 7.

---

Translation Model Type (Figure 8(a))
Mp Wait-k Model
Offlien model
AL
BLEU
AL
BLEU
1.26
13.56
1.18
13.07
1.83
14.98
1.85
14.67
2.65
16.14
2.8
16.7
Zh-+En
3.7
17.07
3.72
17.25
4.26
17.41
4.54
17.73
18
18.14
5.71
18.28
18.19
6.42
18.78
18.76
7.57
18.97
18.88
18.94
10.71
12.22
18.79
Divergence Type (Figure 8(b))
Euclidean Distance BLEU
KL. Divergence
Cosine Distance
AI.
BI.EU
AI.
BLEU
0.67
11.88
0.72
12.3
1.18
13.07
1.47
14.02
1.79
14.63
1.85
14.67
2.45
16.34
2.8
Zh-EBn
3,51
17.11
17.45
3.72
17.25
4.67
17.96
4.54
17.73
18.12
6.32
5.06
18.14
18.54
18.78
5.85
18.19
18.74
8.56
18.83
6.83
18.76
9.04
18.94
9.88
18.85
8.36
18.88
10.1
18.85
10.8
18.89
10.71
18.9
Number of Extra Decoder Layers (Figure 8(e))
0 Extra Decoder Layer
Extra Decoder Layer
Extra Decoder Layers
BLEU
AL
BLEU
AL.
BLEU
13.57
13.07
1.2
13.53
2,24
15
14.67
2.29
15.6
3,58
16.58
3.6
17.09
EEn
17.49
3.72
17.25
4.84
17.97
6.02
18.3
17.73
6.55
18.67
7.49
18.63
18.14
7.87
9.7
18.74
18.19
10.05
18.74
6.83
18.76
8.36
18.88
10.71
18.9
Table 4: Numerical results in Figure 8(a), Figure 8(b) and Figure 8(c).

---

The Maximum Number of Continuous Read (Figure 8(d))
Max
Conti
READ
Max Conti READ 6
Max Conti READ
No Constraint
AL
BLEU
AL
BLEU
AL
BLEU
AL
BLEU
0.89
12.45
1.08
12.74
1.12
12.76
1.18
13.07
1.6
14.26
1.79
14.66
1.89
14.97
1.85
14.67
2,36
15.77
2.7
16.62
2.83
16.79
2.8
16.7
Zh-+En
3.1
16.97
3.67
17.39
4
17.52
3.72
17.25
4.25
17.38
4.44
17.61
4.9
17.65
4.54
17.73
5.67
18.03
5.68
18.42
6.44
18.53
5,06
18.14
18.21
6.87
18.7
7.74
18.48
5.85
18.19
9.12
18.35
8,57
18.62
9.73
18.78
6.83
18.76
10.84
19.14
12.06
19.19
8.36
18.88
10.71
18.9
Max
Conti READ
Max
Conti READ 6
Max Conti READ 8
No Constraint
Al.
BLEU
AL.
BLEU
AL
BLEU
AL
BLEU
0.49
21.65
0.89
22.56
1.13
23
1.79
23.88
1.3
24.51
1.76
25.39
2.04
25.99
2.5
26.07
2.17
27.12
2.69
28.01
3.09
28.45
3.24
27.81
De-FEn
3.25
29.19
3.54
29.39
3.98
29.77
4.03
29.12
4.31
29.97
4.53
30.23
5.16
30.54
5.13
30.22
5.87
30.84
5,55
30.83
6.32
31.12
6.02
30.77
7.65
31.29
6,36
31.12
7,25
31.34
7.59
31.05
8.98
31.52
7.5
31.54
8.59
31.66
8.88
31.39
10.53
31.6
9.77
31.7
11.21
31.89
11.13
31.56
12.53
31.79
Max Conti READ
Max Conti READ 6
Max Conti READ 8
No Constraint
AL
BLEU
AL
BLEU
AL
BLEU
AL
BLEU
0.88
21.81
0.89
21.87
0.89
21.89
0.89
21.89
1.4
27.03
1.41
27.11
1.41
27.12
1.41
27.11
1.94
29.3
1.97
29.27
1.98
29.3
1.99
29.31
En
2.74
29.64
2,89
29.56
2.96
29.58
3.06
29.63
3.7
30.04
4.08
30.02
4.31
30.07
4.6
30.15
4.24
30.01
4.76
29.98
5.05
30.02
5.44
30.09
5.38
30.07
5.32
30.07
5.7
30.08
6.25
30.13
6.1
30.2
6.21
30.09
6.69
30.05
7.49
30.15
7.15
30.04
7.1
30.18
7.7
30.19
8.08
30.2
7.76
30.12
7.67
30.08
8.32
30.04
8.74
30.17
8.49
30.17
8.35
29.98
9,06
30.04
9.61
30.01
11.04
30.03
9.04
30.02
9.83
30.06
10.67
30.11
9.9
30.01
10.83
30.02
11.69
30.1
12.77
30.05
Table Numerical results Figure 8(d).


Reviews:
[{"review_id": "1e09TMAfJ4", "reviewer": "Reviewer_VTic", "summary": "", "questions": "Could you please compare your method with the following two papers?\n1. Zheng et al. Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation. EMNLP, November 2019.\n2. Zheng et al. Simultaneous translation policies: from fixed to adaptive. ACL, 2020, arXiv", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### Thanks for the valuable comments. Here is our response.\n\n> Q1: There are two papers which are very related to this paper. Could you please compare your method with the following two papers?\n\n[1] Zheng et al. Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation. EMNLP, November 2019.\n\n[2] Zheng et al. Simultaneous translation policies: from fixed to adaptive. ACL, 2020, arXiv\n\nA1: Thanks for the professional suggestion. The two papers are excellent in the field of simultaneous translation. We will cite both of these papers in the final version of our paper. However, since these papers did not have open-source code and our experimental settings differ from them, it is challenging to achieve a fair comparison. Nevertheless, we have found that the ITST paper reproduced the method [2] with the same experimental setting as ours in the De-En scenario. We therefore include a comparison in this specific experimental setting as follows. It shows that our DaP-SiMT outperforms the method in [2].\n\n- Adaptive Wait-k [2]\n|  |  |  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| AL | 0.83 | 1.9 | 2.94 | 4.10 | 5.11 | 6.09 | 7.21 | 8.23 | 10.12 | 11.55 | 12.18 |\n| BLEU | 20.29 | 23.56 | 25.96 | 27.44 | 28.29 | 28.91 | 29.73 | 30.10 | 30.76 | 30.78 | 30.74 |\n\n- DaP-SiMT\n|  |  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| AL | 0.49 | 1.3 | 2.17 | 3.25 | 4.31 | 5.87 | 7.65 | 8.98 | 10.53 | 12.53 |\n| BLEU | 21.65 | 24.51 | 27.12 | 29.19 | 29.97 | 30.84 | 31.29 | 31.52 | 31.6 | 31.79 |", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The main contribution of the paper is the proposal of a novel method to construct read/write supervision signals from a parallel training corpus based on statistical divergence. The paper also presents a lightweight policy model that enables adaptive read/write decision-making for a well-trained multi-path wait-k translation model. The proposed approach is both memory and computation efficient and offers an improved trade-off between translation accuracy and latency, outperforming strong baselines. The paper makes contributions to the field of NLP engineering experiment and provides a new approach for SiMT that can be applied to various languages.", "reasons_to_reject": "There are two papers which are very related to this paper:\n1. Zheng et al. Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation. EMNLP, November 2019.\n2. Zheng et al. Simultaneous translation policies: from fixed to adaptive. ACL, 2020, arXiv\n\nThe authors should compare their work with these two lines of work.", "questions_to_authors": "Could you please compare your method with the following two papers?\n1. Zheng et al. Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation. EMNLP, November 2019.\n2. Zheng et al. Simultaneous translation policies: from fixed to adaptive. ACL, 2020, arXiv", "paper_topic_and_main_contributions": "This paper is about the development of an adaptive policy with wait-k model for simultaneous translation (SiMT). The paper addresses the challenge of SiMT, which requires a trade-off between translation accuracy and latency. One of the contributions of the paper is the proposal of a flexible approach to SiMT by decoupling the adaptive policy model from the translation model. This approach allows for improved trade-off between translation accuracy and latency. The paper also proposes a new read/write supervision signal for the adaptive policy model, which is based on the wait-k model. The experimental results show that the proposed approach outperforms strong baselines in terms of both translation accuracy and latency.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "1. Zheng et al. Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation. EMNLP, November 2019.\n2. Zheng et al. Simultaneous translation policies: from fixed to adaptive. ACL, 2020, arXiv"}}, {"review_id": "LZ6DUU7eAP", "reviewer": "Reviewer_Pp2p", "summary": "", "questions": "Question A: Have you computed the curves for \"DaP-SiMT Ground Truth\" for Figure 6? I would like to see the gap between the prediction and the ground truth in terms of BLEU vs. AL.\n\nQuestion B: I have read in the ablation studies that there seem not to be an effect of the divergence type, but have you tried other divergences such as Jensen-Shannon?\n\nQuestion C: I guess you have determined the thresholds for Euclidean, KL-divergence and cosine distance using the validation sets of each task, but could you elaborate on that? How sensitive are BLEU vs. AL to these thresholds? What type of divergence do you use for Figure 6?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### Thanks for the insightful comments. We will address your concern and questions accordingly.\n\n> Concern: The translation quality improvement over the SoTA ITST model is not significant, but systematic across tasks and latency values.\n\nThanks for raising this issue. We will make it more clear in the revised version. \n\nIn Figure 6 of our paper's main experimental results, it visually appears that the two curves (our DaP-SiMT and ITST) are close. However, when we focus on the AL-BLEU curves in the low-latency region and observe the BLEU gap for given latency, we can see that our method is actually much superior to ITST, e.g., when AL$\\approx$2, $\\Delta$BLEU$\\approx +1.5$ for Zh-En,  $\\Delta$BLEU$\\approx +2.5$ for De-En, $\\Delta$BLEU$\\approx +3$ for En-Vi. Because the curve is steep in the low-latency region, the actual BLEU difference is larger than it visually appears.\n\n> Question A: Have you computed the curves for \"DaP-SiMT Ground Truth\" for Figure 6? I would like to see the gap between the prediction and the ground truth in terms of BLEU vs. AL.\n\nThanks for your professional suggestions. We suppose your suggestion is to calculate the R/W signal by $D(p_{pre}^t, p_{full}^t)$ in Eqn. (6,7) during inference, while the target generation remains the same $p(y_t | y_{<t}, x_{\\leq g(t)})$. \n\nWe have computed the curves for \"DaP-SiMT Ground Truth\" for Figure 6 on the Zh-En test set (as shown in the table below) and will include this result in the final version of the paper. As expected, the results of DaP-SiMT Ground Truth guided inference are significantly better ($\\Delta$BLEU$\\approx +1.7$ for AL$\\approx 2.3$) and can be considered as the upper bound of our current approach. In future work, we can continuously approach this upper bound by optimizing the regression task of the RW policy net.\n\n- DaP-SiMT Ground Truth guided inference\n|  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| AL | 0.41 | 1.02 | 1.77 | 2.28 | 2.91 | 3.56 | 4.71 | 6.19 |\n| BLEU | 13.46 | 14.84 | 17.13 | 17.77 | 18.26 | 18.29 | 18.81 | 18.94 |\n\n- DaP-SiMT\n|  |  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| AL | 0.96 | 1.61 | 2.36 | 2.95 | 3.74 | 4.67 | 5.33 | 6.36 | 7.88 | 10.06 |\n| BLEU | 12.55 | 14.23 | 16.08 | 16.92 | 17.72 | 17.83 | 18.09 | 18.65 | 18.62 | 19.16 |\n\n> Question B:  I have read in the ablation studies that there seem not to be an effect of the divergence type, but have you tried other divergences such as Jensen-Shannon?\n\nThanks for your expertise in our field. We conducted experiments based on Jensen-Shannon divergence (JSD) and found that its AL-BLEU curve is comparable to other divergences mentioned in the paper (with slightly lower BLEU scores in the low-latency region). However, we do not recommend using JSD because it has a more concentrated distribution compared to other divergences. This concentration makes the determination of an appropriate threshold for the specific AL more sensitive (as shown in the table below), resulting in difficulties in finding a suitable threshold.\n\n|  |  |  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| AL | 1.44 | 2.7 | 3.68 | 4.52 | 5.85 | 6.41 | 7.88 | 8.92 | 9.76 | 10.69 | 11.2 |\n| threshold | 0.26 | 0.14 | 0.1 | 0.08 | 0.06 | 0.055 | 0.045 | 0.04 | 0.036 | 0.032 | 0.030 |\n\n> Question C: I guess you have determined the thresholds for Euclidean, KL-divergence, and cosine distance using the validation sets of each task, but could you elaborate on that? How sensitive are BLEU vs. AL to these thresholds? What type of divergence do you use for Figure 6?\n\nThanks for your reminder. We will include relevant discussion in the revised version.\n\nAs you mentioned, we first determined the list of thresholds on the validation set and then applied them to the test set. The detailed process is similar to find the threshold list in ROC curve. \n\nThe specific approach is a modified grid search method, where we start by testing the AL and BLEU values with thresholds of {0.1, 0.2, ..., 1.0} with the interval of 0.1. If some regions (e.g., [0.1, 0.3]) have a large drop for AL, we continue to search within such regions with a smaller interval (such as 0.02) until finding a suitable threshold. Alternatively, a simple and native approach is to directly test BLEU-AL on all thresholds with a predefined minimum interval, e.g., 0.02.\n\nBecause the AL is not particularly sensitive to the threshold overall, the process of determining the threshold is straightforward. The table below shows the relationship between AL and threshold for Cosine distance, Euclidean, and KL-divergence, respectively. The divergence used in Figure 6 is Cosine distance. \n\n- Cosine distance\n|  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| AL | 1.18 | 1.85 | 2.8 | 3.72 | 4.54 | 5.85 | 6.83 | 8.36 | 10.71 |\n| threshold | 0.52 | 0.4 | 0.26 | 0.18 | 0.14 | 0.1 | 0.08 | 0.06 | 0.04 |\n\n- Euclidean distance\n|  |  |  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| AL | 0.67 | 1.47 | 2.1 | 3.51 | 4.67 | 5.53 | 6.73 | 7.42 | 9.04 | 10.01 | 11.19 |\n| threshold | 0.5 | 0.4 | 0.33 | 0.24 | 0.2 | 0.18 | 0.16 | 0.15 | 0.13 | 0.12 | 0.11 |\n\n- KL-divergence\n|  |  |  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| AL | 0.72 | 1.37 | 2.21 | 3.03 | 4.07 | 4.94 | 6.32 | 7.61 | 8.56 | 9.88 | 10.8 |\n| threshold | 1.8 | 1.4 | 1.0 | 0.7 | 0.5 | 0.4 | 0.3 | 0.24 | 0.2 | 0.16 | 0.14 |\n\n**Typos Grammar Style And Presentation Improvements**\n\nThank you for your constructive suggestions, and we will make improvements based on your recommendations in the final version of the paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The approach to independently train the adaptive policy based on the idea of divergence is original and the results support the benefits of this simpler approach. The presentation of the paper is excellent and easy to understand the motivation behind the authors' decisions.", "reasons_to_reject": "The translation quality improvement over the SoTA ITST model is not significant, but systematic across tasks and latency values.", "questions_to_authors": "Question A: Have you computed the curves for \"DaP-SiMT Ground Truth\" for Figure 6? I would like to see the gap between the prediction and the ground truth in terms of BLEU vs. AL.\n\nQuestion B: I have read in the ablation studies that there seem not to be an effect of the divergence type, but have you tried other divergences such as Jensen-Shannon?\n\nQuestion C: I guess you have determined the thresholds for Euclidean, KL-divergence and cosine distance using the validation sets of each task, but could you elaborate on that? How sensitive are BLEU vs. AL to these thresholds? What type of divergence do you use for Figure 6?", "paper_topic_and_main_contributions": "This paper proposes an adaptive policy net for SiMT that is trained independently from the translation model and based on the idea of divergence between the prefix-based and the full-sentence probability distro. This adaptive policy is integrated with the multi-path SiMT model (but could be other SiMT model) to prove that outperforms the SoTA ITST SiMT model. This paper provides an alternative simpler approach to policy training in SiMT that is supported by extensive experiments with positive results and ablation studies.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "I have not detected missing references", "Typos_Grammar_Style_and_Presentation_Improvements": "Typo:\n- Table 3 : Ectra -> Extra\n\nPresentation improvements:\n- Tables should have caption as header.\n- Ground truth curves for Figure 6.\n"}, "extra_text": {}}, {"review_id": "aqMh3QxdpG", "reviewer": "Reviewer_sUJ2", "summary": "", "questions": "Q1: Have you tried jointly fine-tuning Multi-path Wait-k Model and R/W Policy Net to enhance the performance of the translation model on the predicted READ/WRITE path from Policy Net? Can freezing the Multi-path Wait-k Model achieve comparable performance to fine-tuning?\n\nQ2: Since the R/W Policy Net is the main part of the proposed method, I would like to know how accurate the R/W Policy Net is in predicting divergence. Experiments on R/W Policy Net prediction accuracy and READ/WRITE path should be added.\n\nQ3: The effectiveness of DaP-SiMT strongly depends on the correctness and generalization of the empirical observations of divergence matrix. Therefore, can you give some statistical properties about the divergence matrix (such as correlation with alignment) instead of some case studies.\n\n\nQ4: Considering that the divergence matrix is calculated by the offline MT model, can the offline model be used instead of the Multi-path Wait-k Model in the proposed method? Because the lower divergence indicates that offline MT predicts that the distribution of the current target word is similar based on full-sentence and prefix, can offline MT be used directly?\n\nThe issue of whether the translation model uses offline, frozen Multi-path Wait-k, or trainable Multi-path Wait-k should be systematically analyzed, which will help to enhance the universality of the proposed method.\n", "limitations": "", "rating": 3, "confidence": 5, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### Thanks for the valuable comments. Here is the response to Reviewer sUJ2\n\n> Q1: Have you tried jointly fine-tuning Multi-path Wait-k Model and R/W Policy Net to enhance the performance of the translation model on the predicted READ/WRITE path from Policy Net? Can freezing the Multi-path Wait-k Model achieve comparable performance to fine-tuning?\n\nA1: We appreciate this professional suggestion. As we mentioned the motivation to decouple the MT model and RW policy net, which is more applicable in practice, so we did not explore the joint fine-tuning. \n\nIn order to address this concern, we conducted a simple experiment on jointly fine-tuning. For quick and stable training, we initialized the model with our well-trained DaP-SiMT checkpoint. To optimize the model, we apply the original multi-path wait-k loss, the sequence cross-entropy loss with the derived path based on our RW policy net, and the RW regression loss. \n\nIn this setup, we observe that the AL-BLEU curve of jointly fine-tuning is almost identical to the model by freezing Multi-path Wait-k Model. Further exploration in this direction will be our future work.\n\n> Q2: Since the R/W Policy Net is the main part of the proposed method, I would like to know how accurate the R/W Policy Net is in predicting divergence. Experiments on R/W Policy Net prediction accuracy and READ/WRITE path should be added.\n\nA2: Thanks for your expertise and attention to our work. First, we would address that it is almost impossible to directly compare the predicted divergence matrix and the corresponding ground truth or calculate the accuracy, because the predicted target has different lengths and different tokens in the same position with the reference. So in our paper, we propose a pseudo-predicted divergence matrix in the case study, where the target used is the reference rather than predicted result. \n\nSecond, to be honest, it is difficult for the RW policy net to predict accurate divergence values at every single position in the matrix. As shown in the case study, the absolute differences for the same positions are non-negligible. Interestingly, we observe that the predicted divergence values effectively learn the relative magnitude relationship, which could result in a similar path as the ground truth matrix if an appropriate threshold is selected. \n\nTherefore, our main concern is whether suitable R/W paths can be obtained based on the predicting matrix. To achieve this goal, we analyze the quality of the path with the proposed AL-NLL metric in Section 5.4.1. \n\nFor this response, we came up with a method for accuracy evaluation on two R/W paths. Specifically, we align two paths on the same matrix and calculate the area surrounded by two paths. Then the accuracy is $1 - \\frac{S_{area}}{|x| |y|}$. If the predicted path completely coincides with the ground truth path, the area surrounded is 0, then the accuracy is 1. We calculated the accuracy on the Zh-En test set at different AL values. The table below shows the results.\n\n|  |  |  |  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  | AL | 0.55 | 1.51 | 2.46 | 3.41 | 4.32 | 5.23 | 6.12 | 7.83 | 8.55 | 9.98 | 10.69 |\n| Predicted Path  | Accuray | 0.898 | 0.915 | 0.913 | 0.909 | 0.9 | 0.895 | 0.893 | 0.894 | 0.897 | 0.906 | 0.911 |\n| Wait-k Path  | Accuray | 0.85 | 0.857 | 0.845 | 0.833 | 0.822 | 0.815 | 0.814 | 0.816 | 0.82 | 0.83 | 0.835 |\n\n> Q3:  The effectiveness of DaP-SiMT strongly depends on the correctness and generalization of the empirical observations of the divergence matrix. Therefore, can you give some statistical properties about the divergence matrix (such as correlation with alignment) instead of some case studies.\n\nA3: This is an insightful question. We have also carefully considered to quantify the effectiveness of the divergence matrix with the proposed AL-NLL metric in Section 5.4.1. \n\nIn fact, the turning point of a suitable R/W path may not necessarily indicate an alignment. However, when preparing for a write operation, the current source prefix should usually include the source tokens aligned to the target prefix, which means that a good R/W path should have less hallucination or a smaller Anticipation Rate (defined in Appendix B). Therefore, we have also added the AR results of the R/W path obtained based on the divergence matrix in the Zh-En test set. \n\n|  |  |  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  | AL | 1.51 | 2.46 | 3.41 | 4.33 | 5.23 | 6.12 | 7.83 | 8.55 | 9.38 | 10.69 |\n| Divergence Matrix based Path | AR | 28.06 | 18.60 | 12.52 | 8.84 | 6.38 | 4.74 | 2.67 | 2.17 | 1.84 | 1.25 |\n| Wait-k Policy based Path | AR | 31.40 | 23.37 | 17.54 | 13.33 | 10.26 | 7.85 | 4.79 | 3.73 | 3.00 | 1.81 |\n\nMore evidence to show the effectiveness or potential of our approach can also refer to the response to Question A for Reviewer Pp2p.\n\n> Q4: Considering that the divergence matrix is calculated by the offline MT model, can the offline model be used instead of the Multi-path Wait-k Model in the proposed method? Because the lower divergence indicates that offline MT predicts that the distribution of the current target word is similar based on full-sentence and prefix, can offline MT be used directly?\n\nA4: Theoretically, offline MT is not suitable for the streaming input in simultaneous translation, because it always requires bidirectional attention and full context. However, we would like to follow the reviewer's suggestion and test the SiMT performance based on the offline MT in the Zh-En test dataset.  The results are shown in the following table. As expected, the BLEU score of the offline MT model is higher in the high-latency region. However, in the mid-to-low latency range, the translation performance of the offline MT model is not ideal. Additional Prefix2Prefix training (as the joint fine-tuning mode in Q1) for the offline MT model should improve this situation.\n\n- DaP-SiMT based on Wait-k model\n|  |  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| AL | 0.96 | 1.61 | 2.36 | 2.95 | 3.74 | 4.67 | 5.33 | 6.36 | 7.88 | 10.06 |\n| BLEU | 12.55 | 14.23 | 16.08 | 16.92 | 17.72 | 17.83 | 18.09 | 18.65 | 18.62 | 19.16 |\n\n- DaP-SiMT based on offline MT model\n|  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| AL | 1.52 | 2.67 | 3.28 | 4.02 | 4.78 | 5.64 | 6.93 | 7.91 | 9.41 |\n| BLEU | 9.51 | 13.67 | 15.38 | 16.28 | 17.03 | 17.92 | 18.11 | 18.73 | 19.32 |\n\n**Missing References**\n\nThank you for your reminder. We will include these references in the final version of the paper.\n\n**Typos Grammar Style And Presentation Improvements**\n\nYour understanding is completely correct, and we appreciate your suggested revisions. We will make improvements based on your recommendations in the final version of the paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "-\tSiMT has received more and more attention due to its low latency characteristics. The proposed method achieves promising results, surpassing the current state-of-the-art ITST.\n-\tThe proposed divergence-based policy is interesting and has certain interpretability.\n-\tThe proposed method adopts a frozen multi-path wait-k translation model and only needs to train the policy module.\n-\tThe article is well written and easy to follow.\n", "reasons_to_reject": "-\tThe article has no obvious weaknesses to reject.\n-\tA potential issue is that divergence-based policies may be affected by language pairs. For example, the word order difference in De-En may make the model READ more words, and the author added Max-Continuous-READ to alleviate this issue.\n", "questions_to_authors": "Q1: Have you tried jointly fine-tuning Multi-path Wait-k Model and R/W Policy Net to enhance the performance of the translation model on the predicted READ/WRITE path from Policy Net? Can freezing the Multi-path Wait-k Model achieve comparable performance to fine-tuning?\n\nQ2: Since the R/W Policy Net is the main part of the proposed method, I would like to know how accurate the R/W Policy Net is in predicting divergence. Experiments on R/W Policy Net prediction accuracy and READ/WRITE path should be added.\n\nQ3: The effectiveness of DaP-SiMT strongly depends on the correctness and generalization of the empirical observations of divergence matrix. Therefore, can you give some statistical properties about the divergence matrix (such as correlation with alignment) instead of some case studies.\n\n\nQ4: Considering that the divergence matrix is calculated by the offline MT model, can the offline model be used instead of the Multi-path Wait-k Model in the proposed method? Because the lower divergence indicates that offline MT predicts that the distribution of the current target word is similar based on full-sentence and prefix, can offline MT be used directly?\n\nThe issue of whether the translation model uses offline, frozen Multi-path Wait-k, or trainable Multi-path Wait-k should be systematically analyzed, which will help to enhance the universality of the proposed method.\n", "paper_topic_and_main_contributions": "In this study on Simultaneous Machine Translation (SiMT), the researchers propose a flexible approach by decoupling the adaptive policy model from the translation model. They introduce DaP, a divergence-based adaptive policy, which efficiently makes read/write decisions based on prediction discrepancy. DaP extends a frozen wait-k model with lightweight parameters for adaptive policy. Experimental results across show that their approach improves translation accuracy and latency trade-off.\n\nThe proposed method outperforms the current state-of-the-art ITST, and the policy is somewhat interpretable. The article is well written and easy to understand.\n", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "-\tACL 2023: Learning Optimal Policy for Simultaneous Machine Translation via Binary Search\n\t- This work also observes that the generation probability of the target word is related to the amount of source information, and uses binary search to find the optimal policy based on this, and builds a separate module to learn the policy.\n\n-\tSome of the latest SIMT work has achieved similar performance to ITST, which can be referred to.\n\t-\tICLR 2023: Hidden Markov Transformer for Simultaneous Machine Translation\n\t-\tACL 2023: Better Simultaneous Translation with Monotonic Knowledge Distillation\n", "Typos_Grammar_Style_and_Presentation_Improvements": "-\tSection 4.3 should add some formulas to clearly explain the training objective of R/W Policy Net. Also, the \"READ or WRITE\" in Figure 5 is confusing. In my understanding, R/W Policy Net learns each item in the divergence matrix instead of directly learning READ/WRITE. It is recommended that you provide a clearer explanation in the final version."}}]

Author rebuttal:

Meta-review: The paper moves the pareto frontier for the quality-latency tradeoff in simultaneous machine translation by proposing to separate the adaptive policy model from the translation model and introducing a supervision signal based on statistical divergence.

The reviewers agree that this work is technically sound, well written, and exciting for the community.
Decision: Accept-Main