General Paper

Peer-Reviewed

Preﬁx Alignment for Training Simultaneous MachineTranslation
Yasumasa Kano† , Katsuhito Sudoh† and Satoshi Nakamura†
Simultaneous translation is a task that starts translation even before the speaker has
ﬁnished speaking. This study focuses on preﬁx-to-preﬁx translation and proposes amethod to align preﬁxes in a bilingual sentence pair iteratively to train a machinetranslation model to work with preﬁx-to-preﬁx. In the experiments, the proposed
method demonstrated higher BLEU than those of the baseline methods in low latency
ranges on the IWSLT simultaneous translation benchmark. However, the proposed
method degraded the performance in high latency ranges in the English-to-Japaneseexperiments; thus, we analyzed it in length ratios and preﬁx boundary prediction
accuracies. The obtained results suggested that the degraded performance was dueto the large word order diﬀerence between English and Japanese.
Key Words: Neural Machine Translation, Simultaneous Translation

1 Introduction
Simultaneous machine translation (SimulMT) is a task used to translate input text or speechincrementally even before observing its explicit boundaries, such as the end of sentences andpauses. It has to determine when to translate a partially-observed input (input preﬁx ) in realtime and also when to generate an appropriate partial output (output preﬁx ) corresponding to
the partial input. This requirement poses a serious diﬃculty compared to standard full-sentencetranslation because the remaining unobserved input cannot be used as context information. The
SimulMT quality should improve using a longer input at the cost of a long delay, and vice versa.
This trade-oﬀ is controlled by a policy to select the next action between read (waiting for further
partial input) and write (emitting partial output) according to the given input and generated
output preﬁxes (Gu et al. 2017).
Wait-k (Ma et al. 2019) is one of the most commonly used baseline SimulMT models thatwaits for k tokens before starting translation. There are also more adaptive models that determine
whether to read or write every time a new input token becomes available (Chiu and Raﬀel 2018;
Raﬀel et al. 2017; Arivazhagan et al. 2019). These methods train the SimulMT model with the† Nara Institute of Science and Technology(C) The Association for Natural Language Processing. Licensed under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

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given policy to learn the preﬁx-to-preﬁx translation. The latency amount of these SimulMT
models is provided as a hyperparameter of training; thus it is diﬃcult to adjust latency at theinference step.
In contrast, there are SimulMT methods that can share one pre-trained NMT model for any
latency and the hyperparameter of latency is provided at the inference step. For instance, Zhang
et al. (2020a) proposed to segment the input sequence with a unit known as Meaningful Unit
(MU) and translate the partial input. The translation is conducted with a standard NMT model
pre-trained with full sentences in their method. This causes serious over-translation because
the NMT model translates preﬁxes that is shorter than full sentences at the inference step ofSimulMT. (Kano et al. 2021).
In this study, we propose to make the NMT model learn preﬁx-to-preﬁx translation by ﬁnetuning a standard NMT model with data augmentation called Preﬁx Alignment. We use a pretrained full-sentence NMT model to ﬁnd bilingual preﬁx pairs from a bilingual training corpus
through the iteration of preﬁx translation and the matching of common preﬁxes between the preﬁxtranslation result and the corresponding reference in the training corpus. The NMT model ﬁnetuned with the collected bilingual preﬁx pairs can be used with any segmentation methods. The
proposed ﬁne-tuning method improved the SimulMT models with several segmentation methods
in low latency ranges in our SimulMT experiments using IWSLT English-to-Japanese (En-Ja)
and English-to-German (En-De) datasets. We also analyzed the eﬀect of word order diﬀerence
to explain the degradation by the proposed method in high latency ranges in En-Ja.

2 Related Work
The problem of SimulMT has been tackled for a decade. In previous attempts using statisticalmachine translation, decision policies were combined with the beam search decoding (Sankaran
et al. 2010; Bangalore et al. 2012). Fujita et al. (2013) used phrase reordering probabilities
employed in phrase-based statistical machine translation for their decision policy. In later years,
feature-based learned policies were proposed. Oda et al. (2014) proposed a feature-based policy
optimization to maximize BLEU. Syntactic features were also successfully used for the policies(Rangarajan Sridhar et al. 2013; Oda et al. 2015).
Recently, most SimulMT studies are based on NMT, and such methods can output more
ﬂuent translations than before. Cho and Esipova (2016) proposed greedy decoding with policies
conditioned by the prediction of the decoder, known as Wait-If-Worse and Wait-If-Diﬀ. Ma
et al. (2019) proposed a simple policy known as test-time wait-k. This is a very simple ﬁxed
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Preﬁx Alignment for Training Simultaneous Machine Translation

policy that waits for k input tokens, and thereafter writes one target token and reads one source
token alternately. The latency hyperparameter k of test-time wait-k is provided at the inference
step. To learn the policy of READ/WRITE decision from the bilingual corpus, reinforcement
learning-based methods were proposed (Grissom II et al. 2014; Satija and Pineau 2016; Gu et al.
2017; Alinejad et al. 2018). It is a straightforward way to optimize latency and accuracy jointly,
but its training process is relatively complex and sometimes unstable. Therefore, Zheng et al.
(2019) proposed a simpler method to ﬁnd oracle read and write actions using a pre-trained NMT
model. These previous NMT-based SimulMT models used a standard NMT model pre-trainedfor full-sentence translation. However, at the inference step of SimulMT, the model needed totranslate partial input; thus there was a gap between training and inference.
Therefore, Ma et al. (2019) proposed a wait-k that uses preﬁx-to-preﬁx training of a SimulMT
model with full-sentence parallel corpus. The policy of wait-k is the same as that of test-timewait-k, but the latency hyperparameter k of wait-k is provided in the training step. Zheng et al.
(2020) proposed an ensemble of diﬀerent wait-k -based models for adaptive SimulMT. There areother approaches for training a SimulMT model that constrain the attention to the last part ofthe partial input (Chiu and Raﬀel 2018; Raﬀel et al. 2017). The attentions that are extended to
all the tokens in the partial input and latency-augmented loss functions are proposed to jointly
optimize translation quality and latency (Arivazhagan et al. 2019; Ma et al. 2020b). The latency
amount of these SImulMT models is provided as a training hyperparameter similar to wait-k.
These approaches make the conditions of training and inference closer, but we cannot adjust thelatency amount at the inference step and need to train the SimulMT model for each latency.
Zhang et al. (2020a) proposed a method to segment the input sequence with a unit known
as MU. In their SimulMT model, the standard pre-trained full-sentence NMT model translates
the partial input every time a boundary of MU is detected. Their method can adjust latency
at the inference step by a hyperparameter and outperformed wait-k and MILk (Arivazhagan
et al. 2019). Kano et al. (2021) proposed incremental prediction of the syntactic constituents toimprove the segmentation. These methods (Zhang et al. 2020a; Kano et al. 2021) use the NMT
model pre-trained with full sentences; thus, there is a gap between training and inference.
There are some approaches to mitigate the gap between training and inference by ﬁne-tuning
the NMT model. Zhang et al. (2020a) proposed a reﬁned version of MU in which they ﬁne-tuned
the pre-trained NMT model with synthetic monotonic translation sentences. However, eventhough the word order of the source and target becomes similar by their method, the synthetic
data are sentence-length which is longer than partial inputs. Dalvi et al. (2018) attempted to
ﬁne-tune the NMT model with bilingual preﬁx pairs. However, they reported that the ﬁne-tuning3

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largely degraded the performance in the simultaneous translation experiment. To generate thebilingual preﬁx pairs, they extracted the ﬁrst N words, N + M words, N + 2M words, … fromeach source sentence as preﬁxes. Thereafter they found the corresponding target preﬁxes fromeach target sentence.
Our proposed method uses a more sophisticated technique to generate bilingual preﬁx pairs
utilizing a pre-trained NMT model and improves the NMT model with any segmentation methods.

3 Simultaneous Machine Translation
Before explaining the proposed method, we ﬁrst introduce the basics of simultaneous translation.

3.1

Formulation

A sentence-level NMT ﬁnds the best translation that maximizes the conditional probability
formulated as follows, letting x = x1 , x2 , ..., xn be an input sentence and y = y1 , y2 , ..., ym be its
translation:p(y|x) =m∏

P (yt |x, y<t ).

(1)

t=1

In SimulMT, we take a preﬁx of the input for its incremental decoding as follows:p(y|x) =m∏

P (yt |x≤g(t) , y<t ),

(2)

t=1

where g(t) is a monotonic non-decreasing function representing the number of input tokens toread until the t-th step. x≤g(t) denotes an input preﬁx read so far, and y<t is the preﬁx translation
written until the previous step. This implies that we can align the preﬁx translation y≤t with
the input preﬁx x≤g(t) as a pair of input-output preﬁxes.

3.2

Inference of MU

Figure 1 shows the inference process of MU. Suppose we have a SimulMT policy based onboundary prediction to start a write action, and a binary boundary predictor gives the probabilityof a boundary at every step. In Step 1, the boundary predictor identiﬁes a boundary for the given
input of “I,” and we obtain its corresponding translation “私は.” In Steps 2 and 3, it does notpredict a boundary; thus, we do not update the translation. Next, in Step 4, it ﬁnds a boundary4

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Preﬁx Alignment for Training Simultaneous Machine Translation

Read source
words

Boundary
Prediction

translation

私はStep 1I

0.9 > 0.5

Step 2I bought

0.2 < 0.5

Step 3I bought a

0.3 < 0.5

Step 4

I bought a pen

0.7 > 0.5

私はペンを買った

Step 5

I bought a pen .

0.7 > 0.5

私はペンを買った。

How to extract bilingual prefix pairs

Figure 1: Example of the translation process of MU from English to Japanese. The threshold ofboundary probability is 0.5 in this case. The underlined part is the output preﬁx used in forced
decoding.

1. Translate the source prefix with pre-trained NMT model
Source Prefix

Source prefix
Translation

Full-sentencetranslation

boundaryI

私は。

私はペンを買った。

0

I bought

私は買った。

私はペンを買った。

0

I bought a

私は買った。

私はペンを買った。

0

I bought a pen

私はペンを買った

私はペンを買った。

1

I bought a pen . 私はペンを買った。 私はペンを買った。

1

Figure 2: Process of generating training dataset for MU-based boundary predictor. The underlined part is the preﬁx translation included in the full-sentence translation. The boundary label1

becomes 1 when such underlines exist.

after the observation of “I bought a pen,” and we translate the input with the output preﬁx
constraint “私は” and obtain “私はペンを買った.” Finally, in Step 5, we translate the entire
input with the output preﬁx constraint “私はペンを買った” and obtain “私はペンを買った。” We
put underlines for the preﬁx constraints in Figure 1, which are used as the forced output preﬁxes
in decoding. The inference processes of MU can be used with diﬀerent boundary prediction
methods using surface- or length-based heuristics (Kano et al. 2021), a statistical model, and soon. The translation process of our proposed method is also the same as MU, but we use the
NMT model ﬁne-tuned with bilingual preﬁx pairs.

3.3

MU-based boundary-predictor

Figure 2 shows the example process of generating the training dataset for an MU-based
boundary predictor. First, the preﬁxes of source sentences in the training dataset are translated5How to extract bilingual prefix pairs
1. Translate the source prefix with pre-trained NMT model
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Source Prefix

Source prefix
Translation

Full-sentencetranslationI

私は。

私はペンを買いました。私は1

I bought

私は買った。

私はペンを買った。

0

I bought a

私は買った。

私はペンを買った。

0

I bought a pen

私はペンを買った 私はペンを買った。

I bought a pen . 私はペンを買った。私はペンを買った。

Extracted Target boundary
Prefix

私はペンを買った1

私はペンを買った。

1

Figure 3: Proposed method to extract the pairs of the source preﬁx and the corresponding
1
target translation preﬁx. Underlines represent the longest common preﬁxes. We perform forced
decoding with the extracted target preﬁx for both source preﬁx and full-sentence translation.
Therefore, the full-sentence translation in the second row changes from that in the ﬁrst row
because the beam search result is diﬀerent owing to forced decoding. The label of the boundaryis 1 when the target preﬁx is extracted.

by a pre-trained full-sentence NMT model. If the entire source preﬁx translation is included in
the full-sentence translation, it is regarded that it is a boundary of MU. In the fourth step of
Figure 2, ”私はペンを買った” is the ﬁrst source preﬁx translation included in the full-sentencetranslation, and the label of the boundary becomes 1. Once a boundary is detected, the preﬁxtranslation output is used for forced decoding in the next preﬁx translation. In Figure 2, thefourth preﬁx translation ”私はペンを買った” is used for forced-decoding when the ﬁfth source
preﬁx ”I bought a pen.” is translated.
After this process, pairs of a source preﬁx and boundary label such as (”I”, 0), (”I bought”,0), ... are obtained. Thereafter, these pairs are used to train an end-to-end binary classiﬁer. This
classiﬁer is used as described in Section 3.2

4 Proposed Method: Preﬁx Alignment
We ﬁne-tune the pre-trained NMT model with bilingual preﬁx pairs to mitigate the gap
between training and inference. To generate such preﬁx pairs, we propose a method called preﬁxalignment, which is shown in Figure 3. The procedure is as follows.

4.1

Generating Preﬁx Translation Pairs

First, we generate pairs of the source preﬁx and the corresponding preﬁx translation using
the pre-trained full-sentence NMT model. Let x be a source language sentence and y be the6Kano et al.

Preﬁx Alignment for Training Simultaneous Machine Translation

corresponding translation result in the target language obtained using the pre-trained NMT
model.
Thereafter, we translate a preﬁx of x with one word1 , x|w|≤1 , into a target language preﬁx(1)ȳ (1) . Here, if the longest common preﬁx ȳlcp between y and ȳ (1) is not empty, we extract(1)(x|w|≤1 , ȳlcp ) as a preﬁx translation pair. Taking the longest common preﬁx is motivated by the
local agreement (Liu et al. 2020), which veriﬁes the stability of a preﬁx translation based on theagreement between preﬁx translation results for growing input preﬁxes. We iterate this processby growing the input preﬁx individually; when we translate the m-word input preﬁx x|w|≤m into(m)ȳ (m) , we ﬁnd the longest common preﬁx ȳlcp between y and ȳ (m) . In the preﬁx translation, weconstrain the decoding to follow the output preﬁxes found thus far for the same bilingual sentencepair by forced decoding, similar to chunk-based incremental decoding described in Section 3.2,
to ﬁnd consistent preﬁx translation pairs. The forced decoding avoids possible changes in the
output preﬁx caused by a beam search. Furthermore, once we extract a preﬁx translation pair
(i)

(i)

(x|w|≤i , ȳlcp ), we update the full-sentence translation y by forced decoding using ȳlcp as an
output preﬁx constraint and use the updated y for later steps ȳ (j) (j > i). If we obtain the same
longest common preﬁx for diﬀerent source language preﬁxes, we only take the ﬁrst appearance(i.e., the pair from the smallest m) and ignore the remainder to avoid inconsistency among the
preﬁx translation pairs. This preﬁx extraction strategy is diﬀerent from that used for ﬁnding an
MU (Zhang et al. 2020a), in which the entire preﬁx translation ȳ (i) should be a preﬁx of the fullsentence translation y without taking the longest common preﬁx as in this study. Additionally,
our strategy works ﬂexibly by the possible update of the full-sentence translation by extracted
output preﬁxes.
Figure 3 shows an example of the proposed method. The ﬁrst preﬁx translation ends with apunctuation mark “。” Thus, MU (Zhang et al. 2020a) cannot extract the ﬁrst preﬁx as the pairbecause the punctuation mark does not match the end of the preﬁx of full-sentence translation
as shown in Figure 2. In contrast, the proposed method can extract the common output preﬁx
by ignoring the latter part of the preﬁx translation. Therefore, the proposed method identiﬁes
more boundaries than the MU.
The extracted target preﬁxes are a part of source preﬁx translations. Therefore, we can ﬁnetune the NMT model with the extracted preﬁx pairs to prevent outputting unnecessary end ofsentence expressions such as “。”. In contrast, the main purpose of the MU is only to ﬁnd the

1 We use the word-based preﬁx length even though we use subwords for simplicity, and x

one or more subwords.

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boundaries. The target preﬁxes with boundaries are already entirely included in the full-sentencetranslation; thus, they do not need to use the target preﬁxes for ﬁne-tuning.

4.2

Preﬁx Alignment with References

The preﬁx translations obtained through the described process are NMT results and are
diﬀerent from their references (ŷ) in general. We obtain reference-based preﬁx alignment byleveraging the extracted bilingual preﬁx pairs. We use BERTScore (Zhang et al. 2020b) to ﬁnd
the correspondence between an NMT-based target preﬁx and a reference preﬁx, varying the
preﬁx window over the reference. We select the reference preﬁx ŷ (i) with the largest BERTScore(i)(in F-measure) against a given NMT-based target preﬁx ȳlcp and extract (x|w|≤i , ŷ (i) ) as areference-based bilingual preﬁx pair.

4.3

Fine-Tuning of a Simultaneous Machine Translation Model

We ﬁne-tune the pre-trained NMT model for the SimulMT task using the extracted bilingual
preﬁx pairs. The model works to translate an input preﬁx incrementally in the chunk-based
decoding as presented in Section 3.2.

5 Experimental Setup
We conducted experiments on En-De and En-Ja simultaneous translation to compare theperformance of the proposed method with the baselines in the quality-latency trade-oﬀ.

5.1

Compared Methods

The baseline is the NMT model trained with full-sentences (full-sentence NMT model), andwe compared it with the NMT model ﬁne-tuned with preﬁx pairs extracted by the proposed
method. We used several boundary prediction methods for both models in the experiments. The
compared boundary predictors are as follows.
• Meaningful Unit (MU): We implemented the standard version of the MU boundary
predictor (Zhang et al. 2020a) with the NMT model trained with full-sentences 2 . We

2 We attempted to implement the reﬁned version of MU (Zhang et al. 2020a). In the reﬁned version, monotonic

translations with pre-trained NMT were ﬁrst generated using the training dataset. Thereafter, the NMT
model was ﬁne-tuned with the monotonic translations. However, in our experiment, the BLEU score of the
NMT model ﬁne-tuned with monotonic translations was extremely low because it was diﬃcult to generate
high-quality monotonic translations based on attention following a previous study. Therefore, we used thestandard version in our experiments.

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Preﬁx Alignment for Training Simultaneous Machine Translation

can adjust the SimulMT latency by the detection threshold for boundary probabilities
given by the boundary predictor. We used 0.5 as its default value but also attempted thefollowing values for further investigation: {0.1, 0.15, ..., 0.95}, {0.99, 0.991, 0.992, ..., 0.999}
and {0.9991, 0.9992, ..., 0.9999}. We used three ranges because it is not clear how muchthe diﬀerence of hyperparameters changes the latency in the experiment.
• Incremental Constituent Label Prediction (ICLP): Following our previous study(Kano et al. 2021), we used a one-look-ahead label predictor. We segmented the input
sequence based on the rules with predicted labels VP and S. We varied the minimum
segment length to adjust latency among {1, 2, 3, ..., 29}.
• Fixed-length segmentation (Fixed): We also attempted simple ﬁxed-length segmentation with a length hyperparameter L, implying that the boundary comes to every L
words. We varied L among {2, 4, 6, ..., 30}.
The translation process is the same with all the boundary predictors as described in Section 3.2.
We additionally compared wait-k as another baseline that has a hyperparameter of latency
provided at the training step diﬀerent from the described three methods. We trained the wait-kmodel for each latency hyperparameter k among {2, 4, 6, ..., 30}.

5.2

Dataset and Preprocessing

Pre-train
Fine-tune
Fine-tune by PA
Dev
Test

En-De
4.5M (WMT2014 train)
206K (IWSLT2017 train)
206K (IWSLT2017 train)
5,589 (Mixed)
1,080 (IWSLT tst2015)

En-Ja
17.9M (WMT2020 train)
223K (IWSLT2017 train)
223K (IWSLT2017 train)
5,312 (Mixed)
1,442 (IWSLT dev2021)

Table 1: Number of sentence pairs in the dataset. For the dev dataset, we used IWSLT dev2010,
tst2010, tst2011 and tst2012 for En-De, and IWSLT dev2010, tst2011, tst2012, and tst2013 for
En-Ja. PA is short for preﬁx alignment.

Table 1 lists the dataset that we used in the experiment. First all the translation models weretrained with pre-training dataset. Next we ﬁne-tuned these models with the IWSLT training
dataset. Simultaneous translation is basically used for spoken language. The IWSLT training
dataset is a spoken language corpus, but it is relatively small. Therefore, we ﬁrst pre-trained

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the NMT model with a large training dataset from WMT, and subsequently ﬁne-tuned it with
the IWSLT training dataset. This NMT model is the baseline. For the proposed method, we
generated bilingual preﬁx pairs only from the ﬁne-tuning dataset, because extracting preﬁx pairs
from a large training corpus requires a signiﬁcant amount of time. Thereafter, we ﬁne-tuned thebaseline NMT model with the preﬁx pairs generated after the process of preﬁx alignment withreferences explained in Section 4.2.
We tokenized Japanese sentences using MeCab (Kudo 2005) and English and German sentences using tokenizer.perl in Moses (Koehn et al. 2007), followed by the subword tokenization
based on Byte Pair Encoding (BPE) (Sennrich et al. 2016) with a shared subword vocabulary of
16K entries.

5.3

NMT Models

All the NMT models were based on the transformer-base (Vaswani et al. 2017) implementationin fairseq (Ott et al. 2019). The hyperparameter settings basically followed the oﬃcial baseline
for IWSLT 20213 , for both pre-training and ﬁne-tuning. The models were saved on checkpoints
in every 5,000 updates for pre-training and every 200 updates for ﬁne-tuning. We applied early
stopping with patience for four checkpoints, based on the loss on the development set. We set thelearning rate to 0.0007, and the minibatch size to 4,096 with the parameter update frequency of
4. We applied a chunk-based beam search described in Section 3.2 to the methods except wait-k.
We used a greedy decoding for wait-k, because of the nature of its model.

5.4

Evaluation Metrics

We used BLEU (Papineni et al. 2002) to evaluate translation quality and evaluated the latencyof simultaneous translation with Average Lagging (AL) (Ma et al. 2019) and Average Token Delay(ATD) (Kano et al. 2023).
5.4.1

Average Lagging (AL)Ma et al. (2019) proposed Average Lagging (AL). Suppose we have an input x = x1 , ..., x|x|
and an output y = y1 , ..., y|y| . AL is denoted as follows:)τg (|x|) (∑1
t−1
ALg (x, y) =g(t) −,
τg (|x|) t=1r(3)

3 https://github.com/pytorch/fairseq/blob/master/examples/simultaneous translation/docs/enja-waitk.md,

https://github.com/pytorch/fairseq/issues/346

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Preﬁx Alignment for Training Simultaneous Machine Translation

where r denotes the length ratio deﬁned as |y|/|x| and g(t) represents the number of source
tokens already read to output the t th target token. τg (|x|) denotes the cut-oﬀ step deﬁned asfollows:
τg (|x|) = min{t | g(t) = |x|},(4)

implying the index of the output token predicted right after the observation of the entire source
sentence.
AL is the most commonly used latency metric that focuses on when to start the translation.
However, AL has the problem that the longer the segment translation becomes, the smaller thelatency becomes although long segment translation should delay the latter translation in actual
situations. Therefore, AL is compatible with wait-k that outputs translation tokens individually,
but not with chunk-based models that output multiple tokens instantly.
5.4.2

Average Token Delay (ATD)

ATD is a metric that adequately considers when to ﬁnish the translation. It includes thedelay at the end of segment translation for that of simultaneous translation. Therefore, we also
used ATD to appropriately evaluate chunk-based translation models.
Suppose x and y are segmented into C chunks x = x1 , x2 , ..., xC and y = y 1 , y 2 , ..., y C ,respectively. An input chunk xc is observed after the previous preﬁx translation y c−1 and used
to predict y c .
ATD is deﬁned as follows:|y|

ATD(x, y) =)1 ∑(T (yt ) − T (xa(t) )|y| t=1

(5)

where

a(t) =s(t)if s(t) ≤ Lacc (xc(t) )Lacc (xc(t) ) otherwises(t) = t − max(Lacc (y c(t)−1 ) − Lacc (xc(t)−1 ), 0)

(6)

(7)

T (·) in Eq. (5) denotes the ending time of each token and a(t) denotes the index of the input∑ctoken corresponding to yt . Lacc (xc ) = j=1 |xj | is the cumulative length up to the c-th chunk,and Lacc (x0 ) = 0. Lacc (y c ) is deﬁned similarly. c(t) denotes the chunk number c to which yt

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belongs.
AL and ATD were calculated using SimulEval (Ma et al. 2020a) and drawn in scatterplots toshow the quality-latency trade-oﬀ.

6 Result
Figures 4a to 4d compare the baselines with the PA (preﬁx alignment)-based methods. Here,full implies that the NMT model was trained only with full sentences. PA implies that the NMT
model was ﬁne-tuned with preﬁx pairs of the source and reference in the ﬁne-tuning dataset.
The AL results in En-De shown in Figure 4a indicate that the PA-based models demonstrated
higher performance than the others in all latency ranges. In contrast, those in En-Ja exhibitedthe advantage only in an extremely low latency range (AL < 3), as shown in Figure 4b.
The ATD results in Figure 4c show that PA-based models also worked well in most latency
ranges for En-De, except for wait-k in the lowest latency range (k = 2, 4). ATD is a latency metricconsidering the delay caused by long translation outputs in previous steps; thus, so this result
reﬂects a possible advantage of wait-k in ATD; Its alternating actions of reading and writing
one token at a step are advantageous in the ATD measurement. In En-Ja, the PA-based model
worked well in the low-middle latency ranges in Figure 4d. The gap between PA-based models
and full-sentence-based models, particularly in low latency is larger in ATD than in AL.

7 Analyses
We performed the following analyses to investigate the characteristics of the proposed method
in detail.

7.1

Length-Related Issues

7.1.1

Length Ratio

First, we analyzed the length ratios of the translation outputs and references to investigate
over-translation phenomena in preﬁx-to-preﬁx translation. Figures 5a and 5b show the scatterplots of AL and the length ratio for En-De and En-Ja, respectively. Fine-tuning using preﬁxalignment pairs decreased the length ratio toward 1.0. This implies that the translation outputs
had fewer unnecessary words, resulting in higher BLEU by increasing n-gram precisions. Fromthe perspective of the latency in ATD, this length ratio reduction derives better ATD because
it considers delays caused by the outputs. In contrast, the full-MU exhibited extremely large
12Preﬁx Alignment for Training Simultaneous Machine Translation30

full-ICLP
full-fixed
full-MU
wait-kPA-ICLP
PA-fixed
PA-MU

BLEU

25
20

full-ICLP
full-fixed
full-MU
wait-kPA-ICLP
PA-fixed
PA-MU

16
14
BLEU

Kano et al.

15

12
10

10
0.0

2.5

5.0

7.5

10.0
AL

12.5

15.0

8

17.5

2

(a) BLEU-AL (En-De)

6

8
AL

10

12

14

16

(b) BLEU-AL (En-Ja)
full-ICLP
full-fixed
full-MU
wait-kPA-ICLP
PA-fixed
PA-MU

25
20
15

full-ICLP
full-fixed
full-MU
wait-kPA-ICLP
PA-fixed
PA-MU

16
14
BLEU

30

BLEU

4

12
10

10
4

6

8

10
12
ATD

14

16

18

8

10

(c) BLEU-ATD (En-De)

12

14

ATD

16

18

20

(d) BLEU-ATD (En-Ja)

Figure 4: Each point represents each value of latency hyperparameter. For instance, in Figure 4a,
the leftmost black dot of wait-k represents k = 2. The x-axis represents delay and the delay islarger on the right side. The y-axis represents quality and the quality is better on the upper side.
Therefore, the dot near the upper left corner is better in quality-latency trade-oﬀ.

length ratios (> 1.2) with their hyperparameter settings for smaller AL. Such large length ratios
increased the ATD as shown in the bottom part of Figures 4c and 4d.

Reference sentence / Source sentence
Translation preﬁx / Source preﬁx

En-De
1.03
1.05

En-Ja
1.06
0.87

Table 2: Length ratio of source to target

7.1.2

Degradation in high latency for En-Ja

The degradation of PA-based models in high latency for En-Ja can be explained by word
order diﬀerence and the quality of the full-sentence NMT model. From Figures 5a and 5b, we
13

full-ICLP
full-fixed
full-MU
wait-kPA-ICLP
PA-fixed
PA-MU

1.7
1.6
1.5
ratio

Vol. 28 No. 1

1.4
1.3

February 2021

1.4

full-ICLP
full-fixed
full-MU
wait-kPA-ICLP
PA-fixed
PA-MU

1.3
1.2
ratio

Journal of Natural Language Processing

1.2

1.1
1.0

1.1

0.9

1.0
0.0

2.5

5.0

7.5

10.0
AL

12.5

15.0

17.5

2

4

(a) En-De

6

8
AL

10

12

14

16

(b) En-Ja

Figure 5: Length ratio and Average Lagging

Source Prefix

Source prefix Translation

Extracted Target Prefix

I

Ich.

Ich

I likeIch mag.

I like playing

Ich spiele gerne.

Ich spiele gerne

I like playing soccer

Ich spiele gerne Fußball.

Ich spiele gerne Fußball

I like playing soccer in

Ich spiele gerne Fußball im

Ich spiele gerne Fußball im

I like playing soccer in the

Ich spiele gerne Fußball im

I like playing soccer in the parkIch spiele gerne Fußball im Park

Ich spiele gerne Fußball im ParkI like playing soccer in the park.

Ich spiele gerne Fußball im Park.

Ich spiele gerne Fußball im Park.

Figure 6: Example of Preﬁx Alignment (En-De)

F1

En-De
0.87

En-Ja
0.80

Table 3: BERTScore of target preﬁx and reference preﬁx

ﬁnd that the length ratios by the PA-based methods were smaller than those by the full-sentencetranslation, and they became less than 1.0 in En-Ja. We investigated the diﬀerence between
the language pairs on the training data. Table 2 lists the length ratios of the source to target
in the IWSLT training dataset for the original sentence pairs and the preﬁx pairs extracted by
Preﬁx Alignment. We found that the length ratio of the preﬁx pairs of En-Ja was much smaller
than En-De, while it was almost the same in the sentence level. This is probably because of thelarger word order diﬀerence between English and Japanese. Figures 6 and 7 show examples of
the Preﬁx Alignment for an English sentence “I like playing soccer in the park,” with a German

14

Kano et al.

Preﬁx Alignment for Training Simultaneous Machine Translation

Source Prefix

Source prefix Translation

Extracted Target Prefix

I

私は。

私は

I like

私は好き。

I like playing

私は遊ぶのが好き。

I like playing soccer

私はサッカーをするのが好き。

I like playing soccer in

私はサッカーをするのが好き。

I like playing soccer in the

私はサッカーをするのが好き。

I like playing soccer in the park

私はサッカーを公園でするのが好き

私はサッカーを公園でするのが好き

I like playing soccer in the park.

私はサッカーを公園でするのが好き。

私はサッカーを公園でするのが好き。

私はサッカーをFigure 7: Example of Preﬁx Alignment (En-Ja)translation “Ich spiele gerne Fußball im Park,” and a Japanese translation “ 私はサッカーを公園でするのが好き。”. For an English preﬁx “I like playing soccer”, we extract a semanticallyequivalent German preﬁx “Ich spiele gerne Fußball” but obtain a Japanese preﬁx “ 私はサッカーを” that misses the counterparts of “like (好き)” and “playing (する)”. This is because Preﬁx

Alignment does not guarantee the semantic equivalence of an extracted preﬁx pair. Our preﬁx
pair extraction described in Figure 3 ﬁnds the longest common preﬁx between a preﬁx and fullsentence translations. The remaining part of the preﬁx translation is discarded and is missing in
the extracted preﬁx pair, and this causes the aforementioned semantic inequivalence. For a given
source language preﬁx, Preﬁx Alignment gives possible translation preﬁxes that can be determinedat the time of the observation of the source language preﬁx and does not guarantee these preﬁxes
cover the contents of the entire source language preﬁx. Such an issue happens mainly becauseof word order diﬀerences because some parts in a source language preﬁx can be translated only
after the translation of later parts that are not observed thus far. For instance, in the translationfrom a Subject-Verb-Object language (English) to a Subject-Object-Verb language (Japanese), a
verb should be translated after the translation of its object. Consequently, the PA-based models
derived short translations as shown in Figure 5b and resulted in worse BLEU for En-Ja.
From the perspective of the quality of preﬁx pairs, Table 3 lists the similarity between the
hypothesis- and reference-based preﬁx measured by BERTScore (Zhang et al. 2020b). We observed a lower score in En-Ja than in En-De. This is because of the diﬀerence in the translation
quality by the full-sentence NMT model as shown in Figures 4b and 4a.
Therefore, the degradation of PA-based models in high latency for En-Ja is caused by theword omission in the extracted target preﬁx and low quality of extracted preﬁx pairs.

15

Journal of Natural Language Processing

Vol. 28 No. 1# Preﬁx pairs# Words in sentences

En-De
1,874,909
4,228,604

February 2021

En-Ja
1,059,865
4,593,194

Table 4: Statistics of the training data

7.1.3

Length Distribution in the Training Set

Table 4 summarizes statistics from the IWSLT training set in the number of extracted preﬁx
pairs and words in the entire dataset. Even though the number of words was almost similar, thatof preﬁx pairs in En-De was almost two times larger than that in En-Ja. This is also due to thelarge word order diﬀerence discussed above because we have to wait for the later source language
input to grow a preﬁx translation in the target language. Figure 8 shows the length distributionsof the source language portions of the preﬁx pairs. The numbers of short source language preﬁxes
in En-Ja were much smaller than those in En-De. Figures 9 and 10 show the length distributions
from the entire training data with and without Preﬁx Alignment. Preﬁx alignment augmented
much more preﬁx translation pairs for English-to-German than for English-to-Japanese. Theseﬁndings suggest that many short preﬁx pairs should contribute to improving SimulMT in En-De.

7.2

Comparison of Boundary Predictors

Second, we investigated the eﬀects of boundary prediction on the performance of the PA-basedmodels by comparing diﬀerent boundary prediction methods.
7.2.1

Boundary Predictors Based on Preﬁx Pairs

In addition to the existing segmentation methods, we tested a boundary predictor that issuitable to the NMT model trained with preﬁx pairs. The predictor identiﬁes a preﬁx boundary
to start a write action. The training data for the boundary prediction model comprise pairs of asource language sentence preﬁx and the corresponding boundary label. The label is set to 1 for a
preﬁx included in the extracted preﬁx pairs and 0 otherwise, as shown in the rightmost column
in Figure 3. The boundary prediction can also be extended to use a target language preﬁx as anadditional clue. The extended model takes a pair of input and output preﬁxes at the time of theboundary prediction and can be trained similarly. For instance, at the fourth step in Figure 3,
it should identify a boundary with an input-output preﬁx pair of (“I bought a pen”, “ 私は”) in
the form of a concatenates string “I bought a pen ||| 私は”.
We trained the following boundary prediction models for comparison:

16

Kano et al.

Preﬁx Alignment for Training Simultaneous Machine Translation

En-De
En-Ja

120000Frequency

100000
80000
60000
40000
20000
0

10

20

30

40

50

Prefix length

60

Figure 8: Source preﬁx length distribution in the IWSLT training data
175000w/ PA
w/o PAfrequency

150000125000100000
75000
50000
25000
0

20

40

60

80

Length of prefix and sentence

100

Figure 9: Source sentence length distribution in the training data (En-De)
w/ PA
w/o PA

Frequency

500000400000300000200000100000
0

20

40

60

80

Length of prefix and sentence

100

Figure 10: Source sentence length distribution in the training data (En-Ja)

17

Journal of Natural Language Processing

Language Pair

En-De

En-Ja

Vol. 28 No. 1

Modelbert-src-w
ro-src-w
ro-src-tgt
ro-src-tgt-w
bert-src-w
ro-src-w
ro-src-tgt
ro-src-tgt-w

Precision
0.58
0.63
0.78
0.74
0.43
0.43
0.73
0.48

February 2021

Recall
0.70
0.76
0.75
0.81
0.69
0.77
0.51
0.81

F1
0.64
0.69
0.77
0.77
0.53
0.56
0.60
0.61

Table 5: Result of boundary prediction• bert-src-w• ro-src-w• ro-src-tgt-w• ro-src-tgt
where bert means the use of a pre-trained English BERTbase model 4 (Devlin et al. 2019), and romeans the use of a pretrained XLM-RoBERTabase model 5 (Conneau et al. 2020). src and tgt
mean the use of the current preﬁx in the source and target languages, respectively. w means theuse of a weighted loss to mitigate the label imbalance for training boundary prediction models.
We also compared oracle segmentation to investigate the limitation of the PA-based boundary
prediction. To obtain oracle segmentation boundaries, we applied Preﬁx Alignment to the test
set and the end of source preﬁxes with label 1 are the oracle boundaries.
7.2.2

Boundary Prediction Performance

We evaluated their performance using the development set listed in Table 1 against the boundaries obtained through Preﬁx Alignment.
Table 5 summarizes the results. For both language pairs, ro-src-tgt-w demonstrated thehighest F1 scores, and those of ro-src-w were slightly better than those of bert-src-w. ro-src-tgt
resulted in a higher precision and lower recall, particularly in En-Ja, but the F1 scores were almost
the same as those of ro-src-tgt-w.

4 https://huggingface.co/bert-base-uncased
5 https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr

18

Kano et al.

Preﬁx Alignment for Training Simultaneous Machine Translation

30

17

28

16

24

PA-ICLP
PA-fixed
PA-MU
PA-oracle
PA-bert-src-w
PA-ro-src-w
PA-ro-src-tgt-w
PA-ro-src-tgt

22
20
18
2.5

5.0

7.5

10.0
AL

12.5

15.0

BLEU

BLEU

26
15

PA-ICLP
PA-fixed
PA-MU
PA-oracle
PA-bert-src-w
PA-ro-src-w
PA-ro-src-tgt-w
PA-ro-src-tgt

14
13

17.5

2

(a) BLEU-AL (En-De)

4

6

8
AL

10

12

14

16

(b) BLEU-AL (En-Ja)

30

17

28

16

24

PA-ICLP
PA-fixed
PA-MU
PA-oracle
PA-bert-src-w
PA-ro-src-w
PA-ro-src-tgt-w
PA-ro-src-tgt

22
20
18
4

6

8

10
12
ATD

14

16

BLEU

BLEU

26
15

PA-ICLP
PA-fixed
PA-MU
PA-oracle
PA-bert-src-w
PA-ro-src-w
PA-ro-src-tgt-w
PA-ro-src-tgt

14
13

18

10

(c) BLEU-ATD (En-De)

12

14
ATD

16

18

(d) BLEU-ATD (En-Ja)

Figure 11: Models ﬁne-tuned using the proposed method with diﬀerent boundary predictors

7.3

Comparison of PA-based Models

Figures 11a to 11d show the quality-latency results of PA-based models in AL and ATD
on En-De and En-Ja SimulMT experiments. For PA-based boundary predictors, we used thesame thresholds as MU to adjust the latency. The AL results in Figures 11a and 11b show that
PA-ro-src-tgt-w and PA-ro-src-tgt were the best in the middle and high latency ranges forboth En-Ja and En-De. In low latency ranges, PA-bert-src-w and PA-MU are the best for En-De
and En-Ja respectively, and their curves are near the oracle segmentation. Surprisingly, PA-fixed
was comparably eﬀective in the low latency range.
The ATD results in Figures 11c and 11d show that PA-ro-src-tgt-w and PA-ro-src-tgt
were slightly better than the other models in the high latency range. In the low latency range,
PA-fixed worked the best. The ﬁxed-length heuristics ran much faster than the other segmenta19

Journal of Natural Language Processing

Latency
2
6
10
14
18
22
26
30

# Null
822
24
1
1
0
0
0
0

Vol. 28 No. 1# Total
9752
3607
2364
1841
1555
1398
1290
1229

February 2021

Latency
2
6
10
14
18
22
26
30

(a) En-De# Null
2978
244
49
16
3
0
0
0

# Total
11393
4297
2852
2282
1953
1765
1638
1567

(b) En-Ja

Table 6: Total number of input preﬁxes and frequencies of those that resulted in null outputs
by PA-fixed in the test set translation

tion models requiring more computations; thus, we may prefer it in practice. Although ICLP has
a limitation on the number of segmentation because of the number of verbs, the ﬁrst few points
in low latency have achieved the best performance in ATD for En-Ja. When we observe oracle
segmentation, it is not the best in ATD.
In conclusion, the curves by PA-ro-src-tgt are very similar to those by PA-ro-src-tgt-w
despite the diﬀerence in the precision-recall trade-oﬀs shown in Table 5. In the high latency
range, PA-ro-src-tgt and PA-ro-src-tgt-w were the best with high thresholds because they
are compatible with the NMT model ﬁne-tuned with PA and use the richer input feature forprediction compared to other PA-based boundary predictors. These models also have the highest
precision and recall as listed in Table 5. In the low latency range, mixed results were obtained
and the PA-based boundary predictors were not always the best for low thresholds.

7.4

Analysis of Fixed-size Segmentation

Fixed-size segmentation (PA-fixed) worked comparably or sometimes better than modelbased boundary predictions. Tables 6a and 6b list the numbers of input preﬁxes that yield null
outputs, where the end-of-the-sentence (EOS) symbol was predicted at the beginning, on thetest at diﬀerent latency (in the number of input tokens) settings of PA-fixed. As summarized
in these tables, the preﬁx-to-preﬁx translation often generated null outputs in the low latency
settings. The null outputs do not impose any constraints on the following preﬁx translations;thus, PA-fixed worked ﬂexibly to wait for later inputs. The tables also reveeal that it happened
more frequently in En-Ja than in En-De. This indicates that En-Ja translation requires a longer
input preﬁx than En-De because of the larger word order diﬀerence.
20

Kano et al.

Preﬁx Alignment for Training Simultaneous Machine Translation

8 Conclusion
We proposed a method to train the neural SimulMT model by extracting bilingual preﬁx pairs
through preﬁx alignment. The proposed method outperformed the baselines in a quality-latency
trade-oﬀ in En-De SimulMT, but the results in En-Ja were mixed. We investigated the resultsin detail and found the performance degradation in En-Ja came from poor preﬁx pair extraction
mainly because of the word order diﬀerence. The proposed method can be extended to the
speech-to-text translation by extracting a pair of a source language speech and target language
text fragments.6
In future work, we will tackle En-Ja problems and improve the method to work for morelanguage pairs.

Acknowledgement
This paper is an extended version of our conference paper (Kano et al. 2022) with additional
experiments and analyses. Part of this work was supported by JSPS KAKENHI with Grant
Numbers JP21H05054 and JP21H03500.

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Yasumasa Kano：He is a Ph.D. student at Nara Institute of Science andTechnology. He received his bachelor’s degree in economics from Yokohama
National University in 2019 and his master’s degree in engineering from Nara
Institute of Science and Technology in 2021.
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Vol. 28 No. 1

February 2021

Katsuhito Sudoh：He is an associate professor at Nara Institute of Scienceand Technology. He received his bachelor’s degree in engineering in 2000 andhis master’s and Ph.D. degrees in informatics in 2002 and 2015, respectively,from Kyoto University. He worked with the NTT Communication Science
Laboratories from 2002 to 2017. He currently works on machine translationand natural language processing. He is a member of ACL, ISCA, ANLP, IPSJ,
ASJ, and JSAI.
Satoshi Nakamura：He is a professor at Nara Institute of Science and Technology and an honorary professor at the Karlsruhe Institute of Technology,
Germany. He received his B.S. from the Kyoto Institute of Technology in1981 and a Ph.D. from the Kyoto University in 1992. He was the Director of
ATR Spoken Language Communication Research Laboratories in the period
2000–2008 and Vice President of ATR in the period 2007–2008. He was the
Director General of Keihanna Research Laboratories and Executive Director of
Knowledge Creating Communication Research Center, National Institute of Information and Communications Technology in 2009–2010. He is currently the
director of the Augmented Human Communication laboratory and a full professor at the Data Science Center and Information Science Division, Graduate
School of Science and Technology, Nara Institute of Science and Technology.
He was an Elected Board Member of the International Speech Communication
Association, ISCA, in the period June 2011-2019, and IEEE Signal Processing
Magazine Editorial Board member in the period 2012-2015, and IEEE SPS
Speech and Language Technical Committee Member in the period 2013-2015.
He is an ATR Fellow, IPSJ Fellow, IEEE Fellow, and ISCA Fellow.

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