Inducing Bilingual Lexicons from Small Quantities of Sentence-Aligned Phonemic
Transcriptions
Oliver Adams1 , Graham Neubig2 , Trevor Cohn1 , Steven Bird1
1

Department of Computing and Information Systems, The University of Melbourne, Australia
2
Graduate School of Information Science, Nara Institute of Science and Technology, Japanoadams@student.unimelb.edu.au, neubig@is.naist.jp, {t.cohn,sbird}@unimelb.edu.au

Abstract
We investigate induction of a bilingual lexicon from a corpusof phonemic transcriptions that have been sentence-alignedwith English translations. We evaluate existing models thathave been used for this purpose and report on two additionalmodels, which demonstrate performance improvements. The
first performs monolingual segmentation followed by alignment, while the second performs both tasks jointly. We showthat monolingual and bilingual lexical entries can be learnt
with high precision from corpora having just 1k–10k sentences. We explain how our results support the applicationof alignment algorithms to the task of documenting endangered languages.

1. Introduction
Language documentation involves the construction of text
collections, lexicons and grammars in the interest of creating a record of a language for future linguistic, cultural andanthropological analysis. Traditional approaches to language
documentation are labour-intensive, requiring much one-onone time between a field linguist and the mother tongue
speakers. Unfortunately, there aren’t enough linguists to document the world’s languages using these approaches before
many of the approximately 7,000 languages die out.
There is a movement to increase the rate of data collection of endangered languages using cheap and widespreadelectronics to record speech in a more ad hoc manner[1, 2, 3, 4, 5], in an attempt to provide the field linguist withleverage to acquire data faster. This data is primarily audio,
since most languages have no established written form andcapturing audio is comparatively fast. Additionally, much of
the data is bilingual, as an important aspect of the language
documentation process is the construction of bilingual corpora and lexicons.
In this paper we consider the task of automatically learning monolingual and bilingual lexical items from unsegmented phonemic transcriptions of interleaved audio (segments of speech in one language along with spoken translations in another). Such transcriptions could arise from two
scenarios. The first is when future philologists phoneticallytranscribe speech of a language post-mortem, without native

speakers to assist in word segmentation. In such instances
lexicon induction would aid in linguistic analysis of the language. The second is by instead employing automatic speech
recognition technologies for the same task. In both cases lexicon induction could aid in bootstrapping automatic speech
recognition (ASR) systems targeting the language’s untranscribed audio. Note that we assume a transcription of the
English translation, since English speech can be reliably andcheaply transcribed.
Previous work on bilingual lexicon induction using
sentence-aligned corpora has focused primarily on large corpora of written text [6, 7, 8, 9]. However, bilingual lexicon
induction applied to phonemically transcribed audio introduces problems, including the lack of word segmentation andthe small quantities of data. There has been limited workon learning lexicons from phonemic transcriptions. [10, 11]
take a first look at phoneme–word translation modeling, using traditional IBM Models [12] in order to determine alignments and applying heuristics to extract dictionaries. [13]propose Model 3P, which builds upon the generative story of
IBM Model 3 by adding additional word length parameters
and allowing it to significantly outperform the IBM models[14, 15, 16].
Building on this work, we investigate two models thathaven’t been considered in this context and demonstrate thatthey can outperform the models that have been considered.
The first performs unsupervised word segmentation followed
by word alignment. The second jointly performs word segmentation and alignment. Importantly, we evaluate the models on a data set that is significantly smaller than has been
evaluated on previously, containing between just 1k and 10k
sentences, corresponding to 13k and 132k words. This likely
corresponds to something in the order of 1 to 10 hours ofspeech [17, 18, 5]. These quantities of data are realistic in thecontext of documentation of endangered languages, though
the applicability of these techniques also applies more generally to low-resource languages that have no body of written
resources.
We run experiments to assess the induced lexicons’ precisions at k entries. We do this by applying the alignment
models to a German–English corpus, using heuristics to ex-

tract lexical entries before having them manually annotated1 .
German was used since it permitted easier manual annotation of lexical entries than an endangered language. Although German and English are more closely related languages than language pairs encountered in linguistic fieldwork, modeling of the language pair is still complex due tovarying word order between the languages and the morphological richness of German relative to English.
Results demonstrate that hundreds of bilingual lexical entries can be learnt with good precision, with the additional
proposed methods outperforming Model 3P on a data set of
10k sentences. This offers promise of the technique’s applicability in a language documentation context. Moreover, themajority of incorrect entries correspond to well-segmented,
but misaligned, source words.

2. Translation Models
Our lexicon induction approach uses various phrase alignment techniques to segment sequences of phonemes intowords and learn phrase tables. There are several methods
for word segmentation in machine translation [19, 20, 21,
22, 16], but there has been limited application in a low resource context. In this paper we examine four representative
methods to apply to parallel sentences comprised of source
phoneme tokens and target words.
The first two, GIZA++ and Model 3P, have been investigated previously for the task of phoneme–word alignment [10, 14]. They are evaluated as a point of comparison
for the latter two methods we demonstrate are effective forthis task, which use unsupervised word segmentation (UWS)
with GIZA++ and a Bayesian inversion transduction grammar (ITG) framework.
2.1. GIZA++
GIZA++ is the baseline that follows the standard statisticalmachine translation (SMT) pipeline of performing alignment
with the IBM Models [12], as implemented in GIZA++ [23].
This approach to alignment was used in seminal work onphoneme–word alignment [10, 11]. The problem with thisapproach is that it attempts to capture relationships between
individual foreign phonemes and English words, which is extremely difficult.
2.2. Model 3P
PISA2 is an implementation of the Model 3P model of [13].
It builds upon the generative model of IBM Model 3 [12] byadding additional word length parameters (see Figure 1), allowing it to outperform traditional IBM models on phoneme–word alignment tasks. After initializing model parameterswith learnt GIZA++ parameters, the PISA implementation
1 These annotations will be released along with code for the lexicon induction.
2 https://code.google.com/p/pisa

this is not the case here
Fertility
this is not the case here
Distortion
this is here not the case
Word length
Trans.

this/3 is/4 here/3 not/4 the/3 case/3

d a s ? I s t h i: 6 n I C t d E 6 f a l

Figure 1: The generative model of Model 3P.
das ?Is t hi:6 nIC t dE6 fal
this is not the case here
Figure 2: Monolingual segmentation of phonemes followedby alignment, as done in the UWS GIZA++ approach.
of Model 3P uses a genetic algorithm to learn the parametersof the model.
The additional word length parameters, distinct from thefertility parameters, allow Model 3P to learn latent word representations that would not be able to be captured in a direct
phoneme–word mapping. This allows for better segmentation performance.
2.3. UWS GIZA++UWS GIZA++ first performs unsupervised word segmentation using the Bayesian Pitman-Yor language model [24], as
implemented in the tool pgibbs3 [25]. Alignment is then performed between these phoneme sequences and the English
words using GIZA++ (see Figure 2). This was hypothesized
to be more appropriate than GIZA++ alone since it would result in breaking the foreign phoneme sequences into coarser
tokens that translate better to English. Note that there is
not an expectation that the word segmentation perform wellwith respect to what is considered a “word” in the given language. Instead, the key idea is that the segmenter breaks
phonemes into frequently repeating units that capture moremeaning than just using individual phonemes. Consider Figure 2: the erroneous segmentation nevertheless allows for
accurate alignment after monolingual segmentation.
2.4. Bayes ITG
Bayes ITG performs joint word segmentation and alignment
using the substring alignment model of [26], as implemented
in pialign4 [27]. Alignments are obtained through Bayesianlearning of inversion transduction grammar trees [28], which
3 http://github.com/neubig/pgibbs
4 http://github.com/neubig/pialignd a s ? I s t h i: 6 n I C t d E 6 f a lthis is not the case here
REG
das?Istthis ish i: 6 n I C t d E 6 f a l
not the case hereINV

h i: 6
here

nICtdE6fal
not the case

Figure 3: An ITG tree structure learnt by pialign. Note thatpialign forces alignments down to individual tokens, but theleaf nodes presented here represent alignments that were generated as single phrase by the model.
completely describe the sentence and its translation as a treeof aligned phrases and binary reordering operations. In Figure 3 the sentence is decomposed, with phrases of different
granularities being captured. The REG and INV tags illustrate the reordering capacities of the ITG trees, with REG being a monotone alignment ordering and INV flipping the English side with respect to the foreign phonemes. The advantage of this joint learning approach over GIZA++, Model 3P
and UWS GIZA++ is that the segmentation on the phoneme
side can be informed by the English, which has been shown
to be valuable [20, 21, 22]. Furthermore, the base distribution Bayes ITG draws from uses cooccurrence probabilitiesof phrases. This contrasts with Model 3P’s initialization,
which uses only the limited phoneme–word alignments of
GIZA++.

3. Experimental Setup
3.1. Data
To train the translation models we used the German–English
parallel corpus from Europarl v7 [29]. In order to imitate a phoneme transcription, we converted the German side
to a sequence of phonemes (represented with the SAMPA5
phoneme alphabet) using the MARY text-to-speech system[30]. For example, ‘dieser’ is represented as a sequence of
space-separated phonemes, ‘d i: z 6’.
The phonemic output of MARY includes some information that cannot reasonably be detected by an ASR system. In
particular, stress markers and syllable boundaries are features
output by the system (‘´’ and ‘-’ respectively), so we filteredthem out. The granularity of tokens on the source side wasthus at the phoneme level while English words were used on
the target side.
Small quantities of data were used in order to mimic therealities of data collection for endangered languages. We experimented with varying data sizes to evaluate how the best

method’s performance scales. We used data sets of 1k, 2k,
5k, and 10k parallel sentences (corresponding to between∼13k and ∼132k words), a quantity that is vastly smaller thanwhat is typically used in statistical machine translation experiments but which approaches reasonable size for reliable
manual transcription. We limited training sentences to thosefewer than 100 phonemes in length.
3.2. Translation Model Training Parameters
GIZA++ was trained using the train-model.perl script included in Moses with default settings, using the grow-diagfinal-and heuristic for symmetrization/phrase extraction andthe msd-bidirectional-fe reordering model.
PISA was trained with default settings.
UWS GIZA++ was trained by running pgibbs first, andthen running GIZA++ over the segmented phoneme sequences with default settings. The pgibbs settings were default, with the following exceptions: block sampling wasused with a block size of 50, a Pitman-Yor distribution wasused, and 1000 iterations were run. The final sample output
by pgibbs was used as input to GIZA++. GIZA++ was run in
the same way as above, using train-model.perl with heuristics for phrase extraction. It’s worth noting that the hyperparameters supplied to pgibbs dictate segmentation granularity.
Were they to change, we would expect the average length of
the word units learnt to be different.
We ran pialign for 10 iterations with the base distribution being a log-linear interpolation of phrase cooccurrence
probabilities in both directions (with a discount of 5), a beamwidth of 10-6 and a batch length of 40. The final sample wasused for the purposes of phrase table extraction.
3.3. Bilingual Lexicon Extraction
To create bilingual lexicons using the above approaches, entries in the phrase tables were first sorted according to theirjoint probabilities. We only included entries where the lengthof the phonemic side was 2 or greater. This heuristic wasused since it removed many spurious entries where one foreign phoneme was aligned to an entire word. Additionally,
for a given English entry no more than the top 5 translations
were included. A similar filter was applied to prevent morethan 5 English translations of a given phoneme sequence.
The top 500 entries of each lexicon were then manually annotated.
3.4. Annotation
Entries in the lexicon were evaluated by a native German
speaker.6 They were determined to be correct, incorrect orambiguous. Correct entries are those that can readily befound in existing German–English dictionaries. For example, the entry vIs@n⇔know (‘wissen’). Incorrect entries arethose whose translations are deemed to be clearly incorrect
6 We measured inter-annotator agreement by doubly annotating a sample

5 http://www.phon.ucl.ac.uk/home/sampa/german.htm

of 1k entries, using a non-native German speaker, resulting in κ = 0.69.

1.0
0.9

0.7

10k

0.6
0.5

5k

0.9

2k
1k

0.8

Precision

Precision

0.8

0.7

0.6

0.5

0.4
0.3
0

1.0

Bayes ITGUWS GIZA++
Model 3P
GIZA++

0.4

100

200

300

# of entries

400

500

Figure 4: Comparison of the methods’ precisions over the
10k dataset. Note that these are the results of the strict evaluation.
by the annotator. These include entries such as tsu:?aIn⇔theand b@dINUN⇔be. In the latter case, note that although theword alignment is incorrect, the phonemes represent a correctly segmented German word, ‘Bedingung’.
Ambiguous entries are those that are neither strictly correct nor incorrect. These include entries that have boundary errors. For example, nvi:6⇔we (‘wir’) includes an extra‘n’ in an otherwise correct entry. Other ambiguous entries
are those that, while not found in lexicons, are nonethelessmeaningful. These usually highlight interesting linguistic
phenomena. For example, nICt⇔does not (‘nicht’) couldn’t
be found in Leo,7 however it captures a meaningful grammatical relationship between the languages. Consider the phrase‘er rennt nicht’ and one English translation ‘he does not run’,
where this entry makes sense.

4. Quantitative Evaluation
4.1. Precision at k over bilingual entries
We compare the four models described in Section 2, eachof which takes as input sentences of unsegmented phonemes
and English translations. Figure 4 shows the precisions of thebilingual lexicons as the number of entries increases from 1
to 500 (sorted by the joint probability given by the model),using the methods trained on 10k sentences.8 The ‘traditional’ approach with GIZA++ is the worst performer acrossthe board. This is to be expected as it uses lexical translation
probabilities between poorly translated German phonemes
and English words as the basis for the extracted phrases. Asa point of comparison to these models, we trained an ‘oracle’ model on correctly segmented phonemes using GIZA++,
7 http://www.leo.org
8 Note that we do not investigate recall as it is both difficult to establish

and less relevant in the early stages of language documentation as only asmall fraction of words will be captured in any case.

0.3
0

100

200

300

400

500

# of entries

Figure 5: Comparison of Bayes ITG precisions for different
sized data.
which removes the effect of segmentation errors but still includes the effect of alignment errors. This oracle modelyielded a lexicon with a precision of 0.932 over the top 500
entries.
The other methods are more similar in performance, withthe best performing approach being Bayes ITG. Though theresults are close, the better performance of Bayes ITG as
compared to the unsupervised word segmentation approach
can possibly be attributed to the added information the English side provides in determining useful German phrases.
This contrasts to the unsupervised word segmentation approach which segments using only monolingual Germanphonemic data. Performance gains over PISA’s Model 3P
can perhaps be attributed to limitations in Model 3P’s generative model. Rather than learning explicit phrasal relationships between phoneme groups and words, Model 3P conditions the generation of phonemes from latent words and thelocation within that word.
Similar trends in the scores were demonstrated whenevaluating precisions that accepted ambiguous entries as also
correct.
Given that Bayes ITG was the best-performing approachon 10k sentences, we additionally evaluated it on smaller
data sizes (see Figure 5). The fewer sentences of phonemesthat are supplied the more reasonable it is to assume that they
can be acquired through reliable manual transcription in areal language preservation scenario. Precision appears to bea logarithmic function of the size of the training data. These
results suggest that the first few hundred entries in a lexicon
can be acquired with good precision even with very limited
data.
4.2. Word segmentation performance
In addition to evaluating the quality of the bilingual entries,
we evaluated the quality of monolingual lexical entries on

Method
Bayes ITG
Bayes ITG
Bayes ITG
Bayes ITGUWS GIZA++
GIZA++
Model 3P

Sents
1k
2k
5k
10k
10k
10k
10k

Incorrect %
26.2
16.6
13.4
9.6
7.2
19.4
14.6

Correct seg. %
52.7
60.2
62.7
62.5
38.9
15.5
46.6

Table 1: The accuracy of the segmentation of phonemic lexical entries judged incorrect. The Incorrect % columns indicate the percentage of the 500 annotated entries that were labeled completely incorrect as bilingual entries . The Correct
seg. % column indicates the percentage of those incorrect
entries that were correctly segmented monolingual entries.
the phoneme side. This is motivated by the observation that
often correct phonemic word units were extracted but mistranslated. Since monolingual entries are useful in their own
right for language documentation purposes (for instance, asa useful starting point for manual correction) and language
modeling, we assessed entries that were incorrect to determine whether the phonemic component was segmented correctly at the word boundaries.
Table 1 shows the proportion of the total entries that wereannotated as incorrect and the proportion of those entries
that were correct monolingual lexical entries on the phoneme
side. Bayes ITG demonstrates effective inference of lexical items with few boundary errors, outperforming the othermethods regardless of the amount of training data used. This
corroborates past research that indicates that word segmentation can be better informed with bilingual data [20, 21, 22].
Also noteworthy is the outperformance of Model 3P relative to UWS GIZA++ when entries are correct (though having fewer strictly incorrect entries overall). In the approachof UWS GIZA++ it is impossible to break apart phoneme
groups that have been chunked across word boundaries by
the monolingual segmentation phase. However, the othermethods aren’t constrained by early, poorly informed chunking. This allows Model 3P relatively better word segmentation despite lower precision of bilingual lexical entries.
Note that although we are evaluating monolingual entries, the entries of UWS GIZA++ are still informed by thealignments with English, as the entries evaluated are thehighest probability bilingual lexical entries found. This mitigates the problem of the effort required to tweak the hyperparameters of the word segmenter to find the right granularityof phoneme clusters. The granularity is instead informed by
the English. To appreciate this, consider the most occurring
lexical entries of the monolingual supervision without being
informed by the alignments, as shown in Table 2. Of these,
the only one that is an actual word is di: (‘die’). The rest
are common sub-word units. Note though that @n (‘-en’)is a common suffix for infinitive verbs—a particularly usefulmorpheme.

Token
?
@
n
t
@n
d
s
6
f
di:

Occurrences
13,096
8,587
8,138
6,422
6,300
5,929
3,226
3,136
3,099
2,913

Table 2: The most common lexical entries found by the unsupervised word segmentation, without harnessing bilingual
information.
f i: l @ n d a N k
thank youFigure 6: The phonemes of vielen dank as aligned to thankyou by PISA’s Model 3P.

5. Qualitative Evaluation
To appreciate the peculiarities and differences of these approaches, we will now consider some general observations made by examining the lexicons of the various approaches, discussing some representative lexical entries andword alignments.
Model 3P seemed generally more susceptible to offby-one errors at the boundaries of entries. A high confidence, but incorrect, entry that occurred in the lexicon
based on Model 3P alignments was i:l@ndaNk⇔you (‘vielen dank’). The English makes some sense, as vielen dank
can be translated as ‘thank you’ or ‘thank you very much’,although the ‘thank’ component on the English side is missing. Notably, the German side is segmented incorrectly at thephrase boundary, missing the initial phoneme ‘f’ (it shouldbe ‘fi:l@ndaNk’). It turns out that in sentences containing
this German phoneme sequence, the ‘f’ is often aligned toEnglish ‘thank’ (see Figure 6). In the lexicons created byboth Bayes ITG and UWS GIZA++ this entry was correctly
phrase-segmented as ‘fi:l@ndaNk’.
A similar such entry in the Model 3P lexicon was
daspa6la:mEn⇔parliament, where the source side is missing the final ‘t’. In the lexicon constructed using Bayes ITG,
such boundary mistakes were scarce. The equivalent entry
was daspa6la:mEnt⇔parliament (‘das Parlament’). Notethat this entry was not considered strictly correct nor correctly segmented, as it is comprised of two words, with theGerman article being included. However in this case, as inalmost all others, Bayes ITG still segments correctly at theboundaries of multiword units (as distinct from correctly seg-mented individual words). One of the instances of an entry
annotated as incorrect in the top 500 entries of the Bayes
ITG lexicon where the phoneme side was also incorrectly
segmented was tvo6d@n⇔been, where there is a spurious‘t’ prefixing the phonemic representation of ‘worden’. Investigating the alignments highlights the cause of this entry.
Phoneme sequences such as Unt6StYtstvO6d@n (‘unterstützt
worden’) and ?E6RaICtvO6d@n (‘erreicht worden’) include
verbs that often appear inflected with different suffixes elsewhere, but end in ‘t’ when occurring before vO6d@n (‘unterstützen’ and ‘erreichen’ respectively, with the suffix ‘-en’).
High correlation of vO6d@n (‘worden’) and the suffix ‘t’likely caused this entry.
The lexicon constructed using Model 3P demonstrated anapparent bias to shorter units. In that lexicon, the above entry was segmented correctly as vO6d@n. On the other hand,
Bayes ITG tended to learn towards longer multiword units, asa result of the model’s capacity to capture phrases at coarser
granularities. po:Ete:6RIN⇔Mr Poettering was present in
the Bayes ITG lexicon, but not in the others. The title ismissing on the source side. This can be attributed to varying
morphology of the title, which takes the form of both ‘Herr’and ‘Herrn’ depending on context. However, since the English side consistently takes the form of ‘Mr Poettering’, evidence is built up primarily to relate both the title and nameon the English side to only the name on the phoneme side.
For all the alignment approaches, there weremany entries that are justified given only the information present in the corpus.
The above example,
daspa6la:mEn⇔parliament, is one such example and isarguably correct in some contexts (consider the phrase das
Parlament lehnte den Antrag ab⇔Parliament rejected therequest). This entry can be attributed to linguistic differencesthat possibly no alignment algorithm can overcome, withthe article often being optional in English translations. In
general, the entries Bayes ITG presented us with tend to beinterpretable with respect to how phoneme sequences occur
in the corpus.
UWS GIZA++ yielded the high confidence, yet erroneous, entries t?⇔is, n?⇔to, n?⇔of, which didn’t occur inthe other lexicons. This is likely a result of the pipelined
nature of the approach, where monolingual segmentation is
first performed before alignment. The German components
to these entries represent frequently occurring phonemic sequences (many words end with ‘t’ or ‘n’ and many start witha glottal stop, ‘?’, before some vowel). The English sides
represent function words that are so commonly occurringthat the coincidental cooccurrence of these phonemes andEnglish words allowed them to become extracted lexical entries, which were not obtained using Bayes ITG or Model
3P. Entries such as this partly explain why UWS GIZA++
failed to perform as well as Model 3P in segmenting lexical
entries despite outperforming it in bilingual precision. The
other likely reason is that chunks that cross word boundaries
learnt during monolingual segmentation cannot be undone.

6. Conclusion
We compared four representative approaches, evaluating thequality of monolingual and bilingual lexical entries. While
two of the techniques had been previously established for thetask of phoneme–word alignment, we achieved performance
improvements by applying models that had not previously
been considered for this task, demonstrating that hundredsof bilingual lexicon entries can be learnt with as few as 1k
sentences of bilingual data. This can be done despite using
an unsegmented phonemic representation of the source side.
Such approaches may be used to indicate what can beinferred from corpora of interleaved audio in the absence of
reliable segmentation, aid in post-mortem linguistic analysis
of a language, and to bootstrap ASR systems in order to help
improve their phoneme recognition.

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