Towards High-Reliability Speech Translation in the Medical Domain
Graham Neubig1 , Sakriani Sakti1 , Tomoki Toda1 , Satoshi Nakamura1 ,Yuji Matsumoto1 , Ryosuke Isotani2 , Yukichi Ikeda2
1
Graduate School of Information Science, Nara Institute of Science and Technology
8916-5 Takayama-cho, Ikoma-shi, Nara, Japan{neubig,ssakti,tomoki,s-nakamura,matsu}@is.naist.jp
2
NEC Corporation
5-7-1 Shiba, Minato-ku, Tokyo, Japan{r-isotani@bp,y-ikeda@df}.jp.nec.com
Abstract
In this paper, we describe the overall design for a speech translation system thataims to reduce the problems caused by language barriers in medical situations. As
first steps to building a system according
to this design, we describe a collection ofa medical corpus, and some translation experiments performed on this corpus. As aresult of the experiments, we find that thebest of three modern translation systems isable to translate 33%-81% of the sentences
in a way such that the main content is understandable.

1 Introduction
One of the most important elements to provisionof high-quality medical service is communication
between medical practitioners and patients. However, in situations where practitioners and patients
do not share a common language, the language
barrier prevents effective communication, making
proper diagnosis and treatment much more difficult. Language barriers occur in medical situations
with immigrants who may speak the language of
their country of residence to some extent, but notenough to effectively communicate medical symptoms. There is also the case of medical tourism,
where tourists may visit another country to receivehigh-quality or affordable medical treatment thatis not available in their home country.
One potential method for overcoming the communication barrier in medical situations is throughthe use of automatic speech translation technology (Nakamura, 2009). Automatic translation of
speech in medical situations can be expected to
be challenging for a number of reasons. The firstreason is that communication of incomplete or incorrect information could lead to a mistaken diagnosis with severe consequences, and thus extremely high levels of accuracy and reliability are

required. The second reason is that conversationin the medical domain has its own unique vocabulary and expressions, and thus it is natural to assume that we must adapt the system appropriately
to the medical domain.
There has been some previous work attempting
to adapt communication technology to meet thesetwo challenges. Eck et al. (2004) focus on adapting a translation system to medical vocabulary,although the focus on text translation of medical
documents instead of speech translation for communication. Miyabe et al. (2007) propose a system for reliable multilingual communication, butrely on a graphical interface that is something likea powerful bilingual phrasebook adapted to communication at a hospital reception desk.
In this paper, we describe our vision for fullspeech translation in medical situations, and somefirst steps to achieve this vision. First, in Section
2 we describe our overall design for the speechtranslation system. This system includes the common components of automatic speech recognition (ASR), machine translation (MT), and textto-speech (TTS), augmented to adapt each component to the task at hand. We also consider whatis necessary to ensure the reliability of translation
results, and consider the use of a system to allow
the conversation to be forwarded to human medical interpreters when necessary.
In the first step towards achieving a translation system for the medical domain we have alsocollected a medical-domain corpus for JapaneseEnglish and Japanese-Chinese translation, as described in Section 3. We share some insights
gained in collecting this corpus, particularly comparing and contrasting text data from a medical
domain bilingual phrasebook, and actual conversational data gathered during doctors’ visits.
Based on this data, we then build several prototype translation systems for the four language
pairs as described in Section 4. We perform au-

Figure 1: An overview of the use scenario for the
medical translation system.
tomatic and manual evaluation of the results andevaluate how close we are to our goal of creating a system that can provide a first wave of assistance in medical situations. In particular, wefind that over all four (relatively difficult) language
pairs, we are on our way towards creating a practical medical machine translation system, with from33%-81% of sentences over two tasks and four
language pairs having all content understandable
with some effort.
Finally, in Section 5 we conclude the paper witha discussion of future work.

2

Medical Translation System Design

We show the overall use scenario in Figure 1. Ina typical doctor’s visit, the patient first enters thedoctor’s office, speaks with receptionists, and fills
out forms. The patient will then enter the doctor’s
office and communicate with doctors/nurses. In
order to introduce a speech translation system foruse in this scenario, we will provide a device thatwill translate between the language of the patient
and that of the medical practitioners.
The device provides two possible methods ofcommunication. The first is through the use of
automatic speech translation technology, where
the speaker’s voice will be recorded, recognized,
translated, and synthesized entirely automatically.
In addition, as speech translation technology is
still far from perfect, the device will also have theability to connect to an actual human medical interpreter located in a call center. However, as thecost of hiring and maintaining medical interpreters
is quite high, we would also like to reduce our reliance on human effort as much as possible. Thus,
each device will use automatic translation by default, but also have functionality to connect to hu-

man interpreters, either based on a manual requestof one of the users, or through automatic detectionof when the dialogue is going poorly, such as themethod described by Walker et al. (2000).
Even with this fall-back to human interpreters,
it is still desirable that the automatic translation
system is effective as possible. In order to ensure
this, we must be certain that the ASR, MT, andTTS models are all tuned to work as well as possible in medical situations. Some potential problems
that we have identified so far based on our analysisof data are as follows:
Specialized Vocabulary: Perhaps the most obvious problem is that the ASR, MT, and TTS
systems must all be able to handle the specialized vocabulary and usage that occurs
in the medical domain. For example, we
found that unadapted systems had trouble
handling specialized terms such as “cardiogram,” medicine names such as “Sudafed,”
and disease names such as “chicken pox.”
This will require the creation of domain specific corpora/dictionaries, and domain adaptation for each of the components (Leggetterand Woodland, 1995; Bellegarda, 2004).
Conversational Speech: The speech during doctor’s visits will generally be somewhat informal and conversational when compared tothat of speeches, news, or other more formal
locations. As a result, we can expect ASR
to be more difficult due to fillers, disfluencies
and other factors (Goldwater et al., 2010).
Translation/Synthesis of Erroneous Input: As
we can expect ASR not to be perfect, it will
be necessary to be able to translate input that
contains errors. This problem can potentiallybe ameliorated by passing multiple speech
recognition hypotheses to translation (Ney,
1999), and jointly optimizing the parametersof ASR and MT (Zhang et al., 2004; Ohgushi
et al., 2013). In addition, it will also benecessary to resolve difficulties in TTS due
to grammatical errors, lack of punctuation,and unknown words (Parlikar et al., 2010).
While all of these problems need to be solved
to provide high-reliability speech translation systems, in this paper as a first step we focus mainlyon the MT system, and relegate the last problem
of integration with ASR to future work.

3

Medical Translation Corpus
Construction and Analysis

In this section, we describe our collection of atri-lingual (Japanese, English, Chinese) corpus toserve as an initial testbed for our medical translation experiments, and an analysis of the corpus.

PhraseConv.
3.1 Corpus Construction
In general, when creating a corpus for training/testing a machine translation system, it is important to collect content that is as close as possible to that which we will encounter when the system is actually used. In our medical translation situation, this is true for both vocabulary (the corpus
must cover special medical terms) and for speaking style (the corpus must have a similar style tothat used by actual doctors and patients speaking
through the system). There is also the practicalconcern that the cost of corpus collection is high,so we would like to perform collection in efficienta manner as possible.
Based on these principles, we designed and collected the following two corpora:
Medical Phrasebooks: The first corpus consistsof sentences designed based on sentencesfrom Japanese-English bilingual phrasebooks designed for interpreters focusing on
the medical domains. Chinese translations
were obtained by translating each phrasefrom Japanese to Chinese. This corpus has
the advantages of relatively efficient construction, and good coverage of medicaldomain terminology, but the conversations
are not necessarily exactly representative of
the conversations that actually occur at a doctor’s office.
Medical Conversation: The second corpus wegathered consists of actual conversations between the patient and the receptionists or doctors recorded during a doctor’s visit. The
doctors and receptionists were all actual practicioners, but for privacy reasons the person
acting as a patient was actually healthy, but
given a scenario to act out. Conversations
were recorded in Japanese and all participants were native Japanese speakers. The
conversations were then segmented by utterance and translated into English and Chinese.
This corpus has the advantage of being highly

Train
Dev
Test
Train
Dev
Test

Sent.
3420
855
855
671
168
168

ja
68k
17k
17k
5.6k
1.4k
1.5k

Word
en
zh
43k 38k
12k 9.6k
12k 9.6k
4.7k 3.4k
1.3k 900
1.2k 880

Table 1: Size in sentences and words of each language for each split for the phrasebook and conversation corpora.

Figure 2: The cumulative length distribution ofsentences in each corpus.
natural and covering medical domain terminology, but requires a large amount of timeand effort for the creation of scenarios, gathering the participants, execution of the actual
dialog, and transcription/translation of the results.
At the end of the collection, we had 5130 and1007 sentences for the phrasebook and conversation corpus respectively. In addition, we create
three splits of the corpus for use in the training,tuning, testing of our machine translation systemwith a ratio of 4:1:1. The final size of the data inall of these corpora is shown in Table 1.
3.2 Corpus Analysis
In this section, we describe some insights gained
from the analysis of both corpora, with some examples illustrating the features of each corpus in
Table 2.
One feature of the data with major implications is that there were large differences in speaking style between the phrasebook and conversation corpora. The data in the phrasebook corpus
generally consisted of longer sentences, while thePhrase

Conv.

1)
2)
1)
2)

I was sewing my jeans using a sewing machine and the needle broke and stabbed
my left cheek.
I have been told that I have early indications of liver cirrhosis.
No more than two vials of blood. Possibly three, if for a blood sugar test.
Go straight, and on your left there is a green chair. / Here? Which way should I face?

Table 2: Examples of sentences (or several sentences separated by slashes) from the phrasebook andconversational corpora.
majority of the utterances in the conversation corpus contained short questions, requests, responses,
and commands. This trend of longer sentences in
the phrasebook corpus is shown clearly in Figure
2, which shows the cumulative length distributionof English sentences under a certain length in both
corpora. Focusing on sentences under length 15,
we can see that this covers a total of 95% of theconversation corpus, but only 72% of the phrasebook corpus.
In addition, the language in the conversation
corpus is significantly less formal, particularly inJapanese where spoken language includes features
such as dropped subjects or particles and abbreviations, which rarely occur in written language(Neubig et al., 2012). We hypothesize that in across-lingual medical conversation situation, thecontent of the utterances will fall somewhere between these two situations, as the content will be
conversational, but the kind of natural and informal interaction seen two native speakers will bedifficult to achieve through an automatic translation system.
The second enlightening feature of the two corpora that we noticed was that medical terminology
was significantly less prevalent in the conversation
corpus. This is also natural, as actual patients to adoctors office will likely be unfamiliar with difficult medical terms, and thus the doctors will tend
to explain in language that is understandable for
their audience. This observation will likely carryover to computer-mediated medical communication as well. As a result, it is likely that adapting
to medical terminology of the domain is somewhat
less important than adapting to the conversational
speaking style of the speech.

4Preliminary Evaluation of Medical
Machine Translation

In this section we describe a preliminary evaluation of the effectiveness of automatic translationon the medical domain data described in the pre-vious section. In particular, we focus on the MTcomponent, leaving evaluation of ASR, TTS, andthe system as a whole for future work.
4.1 Experimental Setup
For the tuning and test data for our translation
system, we use the data described in the previous section. For training, 4,000 sentences is
not enough to build an accurate MT system, so
we add several additional corpora for each language pair. For Japanese-English parallel training data, we add the Eijiro dictionary1 and its accompanying sample sentences, the BTEC corpus(Takezawa et al., 2002), and Wikipedia data fromthe Kyoto Free Translation Task (Neubig, 2011),
for a total of 1.33M parallel sentences and 1.97M
dictionary entries. For Japanese-Chinese parallel training data, we add a dictionary extracted
from Wikipedia’s language links2 , the BTEC corpus, and TED talks (Cettolo et al., 2012) for a
total of 519k sentences and 184k dictionary entries. In addition, we add monolingual from English GigaWord with 22.5M sentences and Chinese Wikipedia with 841k sentences.
We compare three different statistical translation methodologies: phrase-based MT (PBMT,(Koehn et al., 2003)), hierarchical phrase-based
MT (Hiero, (Chiang, 2007)), and forest-to-string
MT (F2S, (Mi et al., 2008)). The reason why wetest these three methodologies is because the former two methodologies do not rely on syntactic
analysis, and thus may be more robust to conversational input that is ill-formed and/or informal. On the other hand, using syntactic information has been shown to improve translation, particularly between language pairs with different syntactic structures such as those we are handling in
our experiments. Thus it will be interesting to
see which methodology can produce better results,
and also if any difference in the effectiveness of1
2

http://eijiro.jp
http://wikipedia.org

the methodologies will be seen between the two
corpora.
For training the translation models and decoding, we use the Moses toolkit (Koehn et al., 2007)
for PBMT and Hiero, and the Travatar (Neubig,2013) toolkit for F2S with the default settings. For
training language models, we use SRILM (Stolcke, 2002), training Kneser-Ney smoothed 5-grammodels for each individual language model training corpus, and linearly interpolating these models
to maximize likelihood on the tuning corpus.
For tokenization we use the Stanford Tokenizer/Segmenter for English and Chinese (Tsenget al., 2005), and the KyTea segmenter forJapanese (Neubig et al., 2011). For syntactic
parsing in English and Chinese we use a modified version of the Egret parser,3 and for Japanese
we use the Eda parser (Flannery et al., 2011)
and the dependency-to-CFG conversion rules in
the Travatar toolkit. Alignment is performed using the unsupervised aligner GIZA++ (Och andNey, 2003) for Japanese-Chinese, and the supervised aligner Nile for Japanese-English (Riesa andMarcu, 2010), with the alignment models being
trained on the alignments distributed with the Kyoto Free Translation Task.4
To measure translation accuracy, we use the automatic evaluation measures of BLEU (Papineni etal., 2002) and RIBES (Isozaki et al., 2010) measured over all sentences in the test corpus. We
also perform a manual evaluation on 120 sentencesfrom the phrasebook corpus and 80 sentences fromthe conversation corpus. These were randomly
selected from all sentences of length 1-30, andgraded using 1-5 adequacy (Goto et al., 2011)as our evaluation measure. We also report thepercentage of sentences that received a rating ofgreater than or equal to 2, indicating that the main
points of the sentence can be understood, possiblywith some difficulty.
4.2 Experimental Results
The results of the experimental evaluation are
shown in Figure 3. This graph shows many
results, but we first focus on the furthestmost
right graph, which shows the percentage of sentences understandable to some extent for eachof the systems. From this graph, we can see
3

http://github.com/neubig/egretThis preprocessing pipeline is available as part of theTravatar toolkit: http://phontron.com/travatar/
preprocessing.html

that the scores range from 81% understandable
sentences for Japanese-Chinese phrasebook sentences, to only 33% understandable sentences on
Japanese-English phrasebook sentences. On theother hand, for conversational sentences, most language pairs hovered at around 55% understandable, with Japanese-English being significantly
worse.
An in-depth analysis of the mistaken sentences
identified several issues for improvement thatwere generally shared by all three systems.
Omitted pronouns: Japanese is a pro-drop language, which means that pronouns, usually
the subject of the sentence can be omitted andinferred from the context. This phenomenon
is particularly prevalent in the types of dialogue contained in the conversation corpus,
with the majority of sentences having theirsubject omitted. Given that it is difficult for
the translation systems used in the experiments to accurately reproduce these omitted
subjects in a non-pro-drop target language
such as English, it is likely that replacingthese subjects in a preprocessing step would
lead to gains in accuracy (Taira et al., 2012)
Dropped words: There were many cases wherewords central to the sentence were missing
from the translation output by the system.
This problem is rooted in a number of problems, such as words being mistakenly unaligned in the training data.
Word segmentation: Both Chinese and Japaneserequire the segmentation of raw text intowords, but occasionally word segmentation
errors occurred either due to conversational
speech or specialized medical terms. Thus,
using domain adaptation techniques (Neubig
et al., 2011) to fix the word segmentationsin the medical domain could potentially improve down-stream accuracy of translation as
well.
Medical domain terms: As expected, there werea few medical domain terms not covered bycorpora from more general domains, such as“Benadryl.” However, the number was also
relatively small, with only 5 untranslatablewords occurring in a 200 sentence corpus.

4

Overall, an interesting shared point between the
majority of members of the list is that they are notFigure 3: Results in BLEU, RIBES, Average Acceptability, and % Understandable for phrase-based(PBMT), hierarchical phrase based (Hiero), and forest-to-string (F2S) systems over translation of medical phrasebooks and conversations.

1

2

3

Input
Ref
PBMT
Hiero
F2S
Input
Ref
PBMT
Hiero
F2S
Input
Ref
PBMT
Hiero
F2S

糖尿病でインスリンを使用しています。
I use insulin because I have diabetes.
I have diabetes using insulin.
Diabetes has been using insulin?
I have been using insulin for diabetes.
反対の目をお願いします。
Let’s test the other eye.
Other eye, please.
Please check your other eye.
I’d like other eye.
もう一回眼をぱちぱちしてください。
Once again, open and close your eyes.
Their eyes again, please.
Their eyes again, please.
Please ぱちぱち eye again.

Table 3: Examples of translations generated byeach system for Japanese-English.
specific to medical translation, but more related tothe style of the text. Thus while raising the level of
medical MT will certainly involve covering medical terminology, it is also equally, if not more,
important to overcome obstacles facing the moregeneral speech translation task as well.
Finally, in Table 3, we show concrete examples for each of the three translation methods inJapanese-English translation. The first example
is from the phrasebook data, uses some medical
terms, and has a very typical syntactic structure
for a written Japanese sentence. As a result F2S
is able to translate almost perfectly, but PBMTand Hiero have reordering problems garbling themeaning of the sentence. The second example literally means “other eye, please,” and PBMT isable to generate this very literal translation. Hiero,on the other hand, mistakenly makes the listener

the subject of “check,” and F2S mistakenly translates “please” as “I’d like,” which doesn’t makesense in this context. In the third example, allthree systems have trouble translating the colloquial word for “blink one’s eyes,” with PBMT and
Hiero dropping the word altogether, and F2S leaving it untranslated.

5 Conclusion and Future WorkIn this paper, we described an overall design for a
speech translation system that aims to reduce theproblems caused by language barriers in medical
situations. We describe a collection of a medical corpus, and some translation experiments performed on this corpus. As a result of the experiments, we find that the best of three modern translation systems is able to translate 33%-81% of thesentences in a way such that the main content isunderstandable.
While these preliminary results are encouraging, this is just the first step towards a full medical speech translation system. As described, there
are still a number of challenges related to the MT
module itself, including the handling of informal
speech. These will further be compounded whencombined with the need for robust ASR and TTS.
However, given the potential for speech translation
technology to be useful in medical situations, we
believe that meeting these research challenges is aworthy target for research and development in thenear future.

Acknowledgments
Part of this research was executed under the Commissioned Research for “Research and Development on Medical Communication Support System for Asian Languages based on Knowledge
and Language Grid” of National Institute of Information and Communications Technology (NICT),Japan.

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