Syntax-based Simultaneous Translation
through Prediction of Unseen Syntactic Constituents
Yusuke Oda

Graham Neubig
Sakriani Sakti
Tomoki Toda
Satoshi Nakamura
Graduate School of Information Science
Nara Institute of Science and Technology
Takayamacho, Ikoma, Nara 630-0192, Japan{oda.yusuke.on9, neubig, ssakti, tomoki, s-nakamura}@is.naist.jp
Abstract
Simultaneous translation is a method to
reduce the latency of communication
through machine translation (MT) by dividing the input into short segments before performing translation. However,short segments pose problems for syntaxbased translation methods, as it is difficult to generate accurate parse trees for
sub-sentential segments. In this paper,
we perform the first experiments applying
syntax-based SMT to simultaneous translation, and propose two methods to prevent degradations in accuracy: a method topredict unseen syntactic constituents that
help generate complete parse trees, and amethod that waits for more input when thecurrent utterance is not enough to generate a fluent translation. Experiments on
English-Japanese translation show that the
proposed methods allow for improvementsin accuracy, particularly with regards toword order of the target sentences.

1 Introduction
Speech translation is an application of machinetranslation (MT) that converts utterances from the
speaker’s language into the listener’s language.
One of the most identifying features of speechtranslation is the fact that it must be performedin real time while the speaker is speaking, andthus it is necessary to split a constant streamof words into translatable segments before starting the translation process. Traditionally, speechtranslation assumes that each segment corresponds
to a sentence, and thus performs sentence boundary detection before translation (Matusov et al.,2006). However, full sentences can be long, particularly in formal speech such as lectures, andif translation does not start until explicit ends ofFigure 1: Simultaneous translation where thesource sentence is segmented after “I think” andtranslated according to (a) the standard method,(b) Grissom II et al. (2014)’s method of final verb
prediction, and (c) our method of predicting syntactic constituents.

sentences, listeners may be forced to wait a considerable time until receiving the result of translation. For example, when the speaker continues
to talk for 10 seconds, listeners must wait at least
10 seconds to obtain the result of translation. Thisis the major factor limiting simultaneity in traditional speech translation systems.
Simultaneous translation (Section 2) avoids thisproblem by starting to translate before observing
the whole sentence, as shown in Figure 1 (a).
However, as translation starts before the wholesentence is observed, translation units are often
not syntactically or semantically complete, and theperformance may suffer accordingly. The deleterious effect of this missing information is lessworrying in largely monotonic language pairs (e.g.
English-French), but cannot be discounted in syntactically distant language pairs (e.g. EnglishJapanese) that often require long-distance reordering beyond translation units.
One way to avoid this problem of missing information is to explicitly predict information neededFigure 2: Process of English-Japanese simultaneous translation with sentence segmentation.

to translate the content accurately. An ambitious
first step in this direction was recently proposedby Grissom II et al. (2014), who describe a methodthat predicts sentence-final verbs using reinforcement learning (e.g. Figure 1 (b)). This approachhas the potential to greatly decrease the delay
in translation from verb-final languages to verbinitial languages (such as German-English), but isalso limited to only this particular case.
In this paper, we propose a more general
method that focuses on a different variety of information: unseen syntactic constituents. Thismethod is motivated by our desire to apply translation models that use source-side parsing, suchas tree-to-string (T2S) translation (Huang et al.,2006) or syntactic pre-ordering (Xia and McCord,
2004), which have been shown to greatly improvetranslation accuracy over syntactically divergent
language pairs. However, conventional methods
for parsing are not directly applicable to the partial sentences that arise in simultaneous MT. The
reason for this, as explained in detail in Section
3, is that parsing methods generally assume thatthey are given input that forms a complete syntactic phrase. Looking at the example in Figure 1,
after the speaker has spoken the words “I think”
we have a partial sentence that will only be complete once we observe the following SBAR. Our
method attempts to predict exactly this information, as shown in Figure 1 (c), guessing the remaining syntactic constituents that will allow us
to acquire a proper parse tree.
Specifically the method consists of two parts:
First, we propose a method that trains a statistical model to predict future syntactic constituents
based on features of the input segment (Section 4).
Second, we demonstrate how to apply this syntac-

tic prediction to MT, including the proposal of aheuristic method that examines whether a future
constituent has the potential to cause a reordering
problem during translation, and wait for more input in these cases (Section 5).
Based on the proposed method, we perform experiments in simultaneous translation of EnglishJapanese talks (Section 6). As this is the first work
applying T2S translation to simultaneous MT, wefirst compare T2S to more traditional phrase-based
techniques. We find that T2S translation is effective with longer segments, but drops off quickly
with shorter segments, justifying the need for techniques to handle translation when full context is
not available. We then compare the proposed
method of predicting syntactic constituents, andfind that it improves translation results, particularly with respect to word ordering in the output
sentences.

2 Simultaneous Translation
In simultaneous translation, we assume that we are
given an incoming stream of words f , which weare expected to translate. As the f is long, we
would like to begin translating before we reach theend of the stream. Previous methods to do so cangenerally be categorized into incremental decoding methods, and sentence segmentation methods.
In incremental decoding, each incoming word is
fed into the decoder one-by-one, and the decoder
updates the search graph with the new words anddecides whether it should begin translation. Incremental decoding methods have been proposed forphrase-based (Sankaran et al., 2010; Yarmohammadi et al., 2013; Finch et al., 2014) and hierarchical phrase-based (Siahbani et al., 2014) SMT

models.1 Incremental decoding has the advantage
of using information about the decoding graph in
the choice of translation timing, but also requires
significant changes to the internal workings of thedecoder, precluding the use of standard decoding
tools or techniques.
Sentence segmentation methods (Figure 2)
provide a simpler alternative by first dividing f into subsequences of 1 or more words[f (1) , . . . , f (N ) ]. These segments are then translated with a traditional decoder into output sequences [e(1) , . . . , e(N ) ], which each are output assoon as translation finishes. Many methods havebeen proposed to perform segmentation, including the use of prosodic boundaries (Fügen et al.,2007; Bangalore et al., 2012), predicting punctuation marks (Rangarajan Sridhar et al., 2013),
reordering probabilities of phrases (Fujita et al.,2013), or models to explicitly optimize translation
accuracy (Oda et al., 2014). Previous work often
assumes that f is a single sentence, and focus on
sub-sentential segmentation, an approach we follow in this work.
Sentence segmentation methods have the obvious advantage of allowing for translation as soon
as a segment is decided. However, the use of theshorter segments also makes it necessary to translate while part of the utterance is still unknown. As
a result, segmenting sentences more aggressively
often results in a decrease translation accuracy.
This is a problem in phrase-based MT, the framework used in the majority of previous research on
simultaneous translation. However, it is an evenlarger problem when performing translation that
relies on parsing the input sentence. We describe
the problems caused by parsing a segment f (n) ,and solutions, in the following section.

3 Parsing Incomplete Sentences
3.1 Difficulties in Incomplete Parsing
In standard phrase structure parsing, the parser assumes that each input string is a complete sentence, or at least a complete phrase. For example,
Figure 3 (a) shows the phrase structure of the complete sentence “this is a pen.” However, in the caseof simultaneous translation, each translation unit
1

There is also one previous rule-based system that uses
syntax in incremental translation, but it is language specificand limited domain (Ryu et al., 2006), and thus difficult tocompare with our SMT-based system. It also does not predict
unseen constituents, relying only on the observed segment.

Figure 3: Phrase structures with surrounding syntactic constituents.
is not necessarily segmented in a way that guarantees that the translation unit is a complete sentence, so each translation unit should be treated
not as a whole, but as a part of a spoken sentence.
As a result, the parser input may be an incompletesequence of words (e.g. “this is,” “is a”), and astandard parser will generate an incorrect parse as
shown in Figures 3(b) and 3(c).
The proposed method solves this problem bysupplementing unseen syntactic constituents before and after the translation unit. For example,
considering parse trees for the complete sentencein Figure 3(a), we see that a noun phrase (NP) canbe placed after the translation unit “this is.” If weappend the syntactic constituent NP as a “blackbox” before parsing, we can create a syntacticallydesirable parse tree as shown in Figure 3(d1) We
also can construct another tree as shown in Figure 3(d2) by appending two constituents DT and
NN . For the other example “is a,” we can create
the parse tree in Figure 3(e1) by appending NP
before the unit and NN after the unit, or can create the tree in Figure 3(e2) by appending only NN
after the unit.
3.2 Formulation of Incomplete Parsing
A typical model for phrase structure parsing is theprobabilistic context-free grammar (PCFG). Parsing is performed by finding the parse tree T that

maximizes the PCFG probability given a sequenceof words w ≡ [w1 , w2 , · · · , wn ] as shown by Eq.
(2):T ∗ ≡ arg max Pr(T |w)(1)

T≃ arg max[T∑log Pr(X → [Y, · · ·]) +(X→[Y,···])∈T∑

log Pr(X → wi ) ],(2)

(X→wi )∈T

where Pr(X → [Y, · · ·]) represents the generative probabilities of the sequence of constituents[Y, · · ·] given a parent constituent X, and Pr(X →wi ) represents the generative probabilities of eachword wi (1 ≤ i ≤ n) given a parent constituent
X.
To consider parsing of incomplete sentenceswith appended syntactic constituents, We defineL ≡ [L|L| , · · · , L2 , L1 ] as the sequence of preceding syntactic constituents of the translation unit
and R ≡ [R1 , R2 , · · · , R|R| ] as the sequence offollowing syntactic constituents of the translation
unit. For the example Figure 3(d1), we assume
that L = [ ] and R = [ NP ].
We assume that both sequences of syntactic
constituents L and R are predicted based on thesequence of words w before the main parsing step.
Thus, the whole process of parsing incomplete
sentences can be described as the combination ofpredicting both sequences of syntactic constituents
represented by Eq. (3) and (4) and parsing withpredicted syntactic constituents represented by Eq.
(5):L∗ ≡ arg max Pr(L|w),(3)

R∗ ≡ arg max Pr(R|w),(4)

T ∗ ≡ arg max Pr(T |L∗ , w, R∗ ).

(5)

L

R
T

Algorithmically, parsing with predicted syntactic constituents can be achieved by simply treatingeach syntactic constituent as another word in the
input sequence and using a standard parsing algorithm such as the CKY algorithm. In this process,the only difference between syntactic constituentsand normal words is the probability, which we define as follows:{

Pr(X → Y ) ≡1, if Y = X
0, otherwise.

(6)

It should be noted that here L refers to syntactic constituents that have already been seen in thepast. Thus, it is theoretically possible to store pastparse trees as history and generate L based on thishistory, or condition Eq. 3 based on this information. However, deciding which part of trees touse as L is not trivial, and applying this approachrequires that we predict L and R using different
methods. Thus, in this study, we use the same
method to predict both sequences of constituentsfor simplicity.
In the next section, we describe the actual
method used to create a predictive model for thesestrings of syntactic constituents.

4 Predicting Syntactic Constituents
In order to define which syntactic constituentsshould be predicted by our model, we assume thateach final parse tree generated by w, L and R
must satisfy the following conditions:
1. The parse tree generated by w, L and R mustbe “complete.” Defining this formally, thismeans that the root node of the parse tree forthe segment must correspond to a node in theparse tree for the original complete sentence.
2. Each parse tree contains only L, w and R as
terminal symbols.
3. The number of nodes is the minimum necessary to satisfy these conditions.
As shown in the Figure 3, there is ambiguity regarding syntactic constituents to be predicted (e.g.
we can choose either [ NP ] or [ DT , NN ] as R
for w = [ “this”, “is” ]). These conditions avoid
ambiguity of which syntactic constituents should
predicted for partial sentences in the training data.
Looking at the example, Figures 3(d1) and 3(e1)satisfy these conditions, but 3(d2) and 3(e2) donot.
Figure 4 shows the statistics of the lengths of
L and R sequences extracted according to thesecriteria for all substrings of the WSJ datasets 2 to
23 of the Penn Treebank (Marcus et al., 1993), a
standard training set for English syntactic parsers.
From the figure we can see that lengths of up to 2
constituents cover the majority of cases for both L
and R, but a significant number of cases require
longer strings. Thus methods that predict a fixed
number of constituents are not appropriate here. In
Algorithm 1, we show the method we propose toAlgorithm 1 Prediction algorithm for following
constituents R∗
T ′ ← arg max Pr(T |w)
TR∗ ← [ ]loopR+ ← arg max Pr(R|T ′ , R∗ )R

Figure 4: Statistics of numbers of syntactic constituents to be predicted.

predict R for constituent sequences of an arbitrary
length. Here ++ represents the concatenation of
two sequences.
First, our method forcibly parses the input sequence w and retrieves a potentially incorrectparse tree T ′ , which is used to calculate features
for the prediction model. The next syntactic constituent R+ is then predicted using features extracted from w, T ′ , and the predicted sequence
history R∗ . This prediction is repeated recurrently
until the end-of-sentence symbol (“nil” in Algorithm 1) is predicted as the next symbol.
In this study, we use a multi-label classifier
based on linear SVMs (Fan et al., 2008) to predict
new syntactic constituents with features shownin Table 1. We treat the input sequence w andpredicted syntactic constituents R∗ as a concatenated sequence w ++ R∗ . For example, if we have
w = [ this, is, a ] and R∗ = [ NN ], then theword features “3 rightmost 1-grams” will take thevalues “is,” “a,” and NN . Tags of semi-terminal
nodes in T ′ are used as part-of-speech (POS) tags
for corresponding words and the POS of each predicted syntactic constituent is simply its tag. “nil”
is used when some information is not available.
For example, if we have w = [ this, is ] andR∗ = [ ] then “3 rightmost 1-grams” will take thevalues “nil,” “this,” and “is.” Algorithm 1 and Table 1 shows the method used to predict R∗ but L∗
can be predicted by performing the prediction process in the reverse order.

5 Tree-to-string SMT with Syntactic
Constituents
Once we have created a tree from the sequenceL∗ ++ w ++ R∗ by performing PCFG parsing withpredicted syntactic constituents according to Eqs.
(2), (5), and (6), the next step is to use this tree intranslation. In this section, we focus specifically

if R+ = nil thenreturn R∗
end ifR∗ ← R∗ ++[R+ ]end loop

Table 1: Features used in predicting syntactic constituents.
Type Feature
Words 3 leftmost 1,2-grams in w ++ R∗
3 rightmost 1,2-grams in w ++ R∗
Left/rightmost pair in w ++ R∗
POS
Same as “Words”
Parse Tag of the root node
Tags of children of the root nodePairs of root and children nodes
Length |w||R∗ |on T2S translation, which we use in our experiments, but it is likely that similar methods are applicable to other uses of source-side syntax suchas pre-ordering as well.
It should be noted that using these trees in T2S
translation models is not trivial because each estimated syntactic constituent should be treated as anaggregated entity representing all possibilities ofsubtrees rooted in such a constituent. Specifically,
there are two problems: the possibility of reordering an as-of-yet unseen syntactic constituent into
the middle of the translated sentence, and the calculation of language model probabilities considering syntactic constituent tags.
With regards to the first problem of reordering,consider the example of English-Japanese translation in Figure 5(b), where a syntactic constituent
PP is placed at the end of the English sequence(R∗ ), but the corresponding entity in the Japanesetranslation result should be placed in the middle ofthe sentence. In this case, if we attempt to translate
immediately, we will have to omit the as-of-yet
unknown PP from our translation and translate it
later, resulting in an unnatural word ordering in theAlgorithm 2 Waiting algorithm for T2S SMT
w←[]
loop
w ← w ++ NextSegment()L∗ ← arg max Pr(L|w)
LR∗ ← arg max Pr(R|w)
R

T ∗ ← arg max Pr(T |L∗ , w, R∗ )Te∗ ← arg max Pr(e|T ∗ )e

if elements of R∗ are rightmost in e∗ thenOutput(e∗ )w←[]
end ifend loop(a)(b)

6 Experiments
Figure 5: Waiting for the next translation unit.

target sentence.2
Thus, if any of the syntactic constituents in R
are placed anywhere other than the end of thetranslation result, we can assume that this is a hintthat the current segmentation boundary is not appropriate. Based on this intuition, we propose aheuristic method that ignores segmentation boundaries that result in a translation of this type, and instead wait for the next translation unit, helping to
avoid problems due to inappropriate segmentation
boundaries. Algorithm 2 formally describes thiswaiting method.
The second problem of language model probabilities arises because we are attempting to generate a string of words, some of which are not actual
words but tags representing syntactic constituents.
Creating a language model that contains probabilities for these tags in the appropriate places is nottrivial, so for simplicity, we simply assume that every syntactic constituent tag is an unknown word,
and that the output of translation consists of bothtranslated normal words and non-translated tags asshown in Figure 5. We relegate a more complete
handling of these tags to future work.

2

It is also potentially possible to create a predictive modelfor the actual content of the PP as done for sentence-final
verbs by Grissom II et al. (2014), but the space of potential
prepositional phrases is huge, and we leave this non-trivial
task for future work.

6.1 Experiment Settings
We perform 2 types of experiments to evaluate theeffectiveness of the proposed methods.
6.1.1 Predicting Syntactic ConstituentsIn the first experiment, we evaluate prediction accuracies of unseen syntactic constituents L and R.
To do so, we train a predictive model as describedin Section 4 using an English treebank and evaluate its performance. To create training and testing
data, we extract all substrings w s.t. |w| ≥ 2 in
the Penn Treebank and calculate the corresponding syntactic constituents L and R by according
to the original trees and substring w. We use the
90% of the extracted data for training a classifier
and the remaining 10% for testing estimation recall, precision and F-measure. We use the Ckylark
parser(Oda et al., 2015) to generate T ′ from w.
6.1.2 Simultaneous Translation
Next, we evaluate the performance of T2S simultaneous translation adopting the two proposed
methods. We use data of TED talks from theEnglish-Japanese section of WIT3 (Cettolo et al.,2012), and also append dictionary entries and examples in Eijiro3 to the training data to increase
the vocabulary of the translation model. The total
number of sentences/entries is 2.49M (WIT3, Eijiro), 998 (WIT3), and 468 (WIT3) sentences fortraining, development, and testing respectively.
We use the Stanford Tokenizer4 for English
tokenization, KyTea (Neubig et al., 2011) for
34

http://eijiro.jp/
http://nlp.stanford.edu/software/tokenizer.shtml

Japanese tokenization, GIZA++ (Och and Ney,
2003) to construct word alignment, and KenLM(Heafield et al., 2013) to generate a 5-gram target
language model. We use the Ckylark parser, which
we modified to implement the parsing method ofSection 3.2, to generate T ∗ from L∗ , w and R∗ .
We use Travatar (Neubig, 2013) to train the T2Stranslation model used in the proposed method,
and also Moses (Koehn et al., 2007) to train
phrase-based translation models that serve as abaseline. Each translation model is tuned using MERT (Och, 2003) to maximize BLEU (Papineni et al., 2002). We evaluate translation accuracies by BLEU and also RIBES (Isozaki etal., 2010), a reordering-focused metric which has
achieved high correlation with human evaluation
on English-Japanese translation tasks.
We perform tests using two different sentence
segmentation methods. The first is n-words segmentation (Rangarajan Sridhar et al., 2013), a simple heuristic that simply segments the input every n words. This method disregards syntactic
and semantic units in the original sentence, allowing us to evaluate the robustness of translation
against poor segmentation boundaries. The second
method is the state-of-the-art segmentation strategy proposed by Oda et al. (2014), which finds
segmentation boundaries that optimize the accuracy of the translation output. We use BLEU+1(Lin and Och, 2004) as the objective of this segmentation strategy.
We evaluate the following baseline and proposed methods:

shows results of two evaluation settings, where
the order of generated constituents is considered
or not.
We can see that in each case recall is lower thanthe corresponding precision and the performanceof L differs between ordered and unordered results. These trends result from the fact that themodel generates fewer constituents than exist in
the test data. However, this trend is not entirely unexpected because it is not possible to completely
accurately guess syntactic constituents from every
substring w. For example, parts of the sentence“in the next 18 minutes” can generate the sequence“in the next CD NN ” and “ IN DT JJ 18 minutes,” but the constituents CD in the former caseand DT and JJ in the latter case are not necessary in all situations. In contrast, NN and IN
will probably be inserted most cases. As a result,the appearance of such ambiguous constituents inthe training data is less consistent than that of necessary syntactic constituents, and thus the prediction model avoids generating such ambiguous constituents.

PBMT is a baseline using phrase-based SMT.

6.2.2 Simultaneous Translation

T2S uses T2S SMT with parse trees generated
from only w.

Next, we evaluate the translation results achievedby the proposed method. Figures 6 and 7 show the
relationship between the mean number of words inthe translation segments and translation accuracyof BLEU and RIBES respectively. Each horizontal axis of these graphs indicates the mean numberof words in translation units that are used to generate the actual translation output, and these canbe assumed to be proportional to the mean waitingtime for listeners. In cases except T2S-Wait, thesevalues are equal to the mean length of translation
unit generated by the segmentation strategies, andin the case of T2S-Wait, this value shows the lengthof the translation units concatenated by the waiting
strategy. First looking at the full sentence results(rightmost points in each graph), we can see thatT2S greatly outperforms PBMT on full sentences,

T2S-Tag further predicts unseen syntactic constituents according to Section 4. Before evaluation, all constituent tags are simply deleted
from the output.
T2S-Wait uses T2S-Tag and adds the waiting
strategy described in Section 5.
We also show PBMT-Sent and T2S-Sent which
are full sentence-based PBMT and T2S systems.
6.2 Results
6.2.1 Predicting Syntactic Constituents
Table 2 shows the recall, precision, and F-measureof the estimated L and R sequences. The table

Table 2: Performance of syntactic constituent prediction.
TargetP%
R%
F%
L(ordered)
31.93
7.27 11.85(unordered) 51.21 11.66 19.00
R(ordered)
51.12 33.78 40.68(unordered) 52.77 34.87 42.00

(a) n-words segmentation(b) optimized segmentation

Figure 6: Mean #words and BLEU scores of each method.

(a) n-words segmentation(b) optimized segmentation

Figure 7: Mean #words and RIBES scores of each method.
underlining the importance of considering syntax
for this language pair.
Turning to simultaneous translation, we firstconsider the case of n-words segmentation, which
will demonstrate robustness of each method topoorly formed translation segments. When wecompare PBMT and T2S, we can see that T2S issuperior for longer segments, but on shorter segments performance is greatly reduced, dropping
below that of PBMT in BLEU at an average of 6
words, and RIBES at an average of 4 words. This
trend is reasonable, considering that shorter translation units will result in syntactically inconsistentunits and thus incorrect parse trees. Next looking at the results for T2S-Tag, we can see that in
the case of the n-words segmentation, it is able
to maintain the same translation performance of
PBMT, even at the shorter settings. Furthermore,
T2S-Wait also maintains the same performanceof T2S-Tag in BLEU and achieves much higher
performance than any of the other methods in
RIBES, particularly with regards to shorter translation units. This result shows that the method of
waiting for more input in the face of potential re-ordering problems is highly effective in maintaining the correct ordering of the output.
In the case of the optimized segmentation,
all three T2S methods maintain approximately
the same performance, consistently outperforming
PBMT in RIBES, and crossing in BLEU around 56 words. From this, we can hypothesize that the
optimized segmentation strategy learns featuresthat maintain some syntactic consistency, whichplays a similar role to the proposed method. However, RIBES scores for T2S-Wait is still generally
higher than the other methods, demonstrating that
waiting maintains its reordering advantage even in
the optimized segmentation case.

7 Conclusion and Future WorkIn this paper, we proposed the first method toapply SMT using source syntax to simultaneoustranslation. Especially, we proposed methods tomaintain the syntactic consistency of translation
units by predicting unseen syntactic constituents,
and waiting until more input is available when it isnecessary to achieve good translation results. Ex-periments on an English-Japanese TED talk translation task demonstrate that our methods are morerobust to short, inconsistent translation segments.
As future work, we are planning to devise
more sophisticated methods for language modeling using constituent tags, and ways to incorporate previously translated segments into the estimation process for left-hand constituents. Next,
our method to predict additional constituents does
not target the grammatically correct translation
units for which L = [ ] and R = [ ], although
there is still room for improvement in this assumption. In addition, we hope to expand the methods proposed here to a more incremental setting,where both parsing and decoding are performed
incrementally, and the information from these processes can be reflected in the decision of segmentation boundaries.

Acknowledgement
Part of this work was supported by JSPS KAKENHI Grant Number 24240032, and Grant-in-Aid
for JSPS Fellows Grant Number 15J10649.

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