Multilingual embeddings are useful because they provide meaning representations of source and target in the same space in machine translation and because they are a basis for transfer learning.
In contrast to prior multilingual work Zeman and Resnik (2008); McDonald et al. (2011); Tsvetkov et al. (2014), automatically learned embeddings potentially perform as well but are more efficient and easier to use Klementiev et al. (2012); Hermann and Blunsom (2014b); Guo et al. (2016). Thus, multilingual word embedding learning is important for NLP.
The quality of multilingual embeddings is driven by the underlying feature set more than the type of algorithm used for training the embeddings Upadhyay et al. (2016); Ruder et al. (2017). Most embedding learners build on using context information as feature. Dufter et al. (2018) recently showed that using concept information can be effective for multilingual embedding learning as well.
Here, we propose the method SC-ID. SC-ID combines the concept identification method “anymalign” Lardilleux and Lepage (2009) and S-ID Levy et al. (2017) into an embedding learning method that is based on both concept (C-ID) and context (S-ID). We show below that SC-ID is effective. On a massively parallel corpus, the Parallel Bible Corpus (PBC) covering 1000+ languages, SC-ID outperforms S-ID in a word translation task. In addition, we show that SC-ID outperforms S-ID on EuroParl, a corpus with characteristics quite different from PBC.
For both corpora there are embedding learners that perform better. However, SC-ID is the only one that scales easily across 1000+ languages and at the same time exhibits a stable performance across datasets. In summary, we make the following contributions in this paper: i) We demonstrate that using concept IDs equivalently to Levy et al. (2017)’s sentence IDs works well. ii) We show that combining C-IDs and S-IDs yields higher quality embeddings than either by itself. iii)
In extensive experiments investigating hyperparameters we find that, despite the large number of languages, lower dimensional spaces work better.iv) We demonstrate that our method works on very different datasets and yields competitive performance on a EuroParl based corpus.
Throughout this section we describe how we identify concepts and write out text corpora that are then used as input to the embedding learning algorithm.
2.1 Concept Induction
Lardilleux and Lepage (2009) propose a word alignment algorithm which we use for concept induction. They argue that hapax legomena (i.e., words which occur exactly once in a corpus) are easy to align in a sentence-aligned corpus. If hapax legomena across multiple languages occur in the same sentence, their meanings are likely the same. Similarly, words across multiple languages that occur more than once, but strictly in the same sentences, can be considered translations. We call words that occur strictly in the same sentences perfectly aligned. Further we define a concept as a set of words that are perfectly aligned.
By this definition one expects the number of identified concepts to be low. Coverage can be increased by not only considering the original parallel corpus, but by sampling subcorpora from the parallel corpus. As the number of sentences is smaller in each sample and there is a high number of sampled subcorpora, the number of perfect alignments is much higher. In addition to words, word ngrams are also considered to increase coverage. The complement of an ngram (i.e., the sentence in one language without the perfectly aligned words) is also treated as a perfect alignment as their meaning can be assumed to be equivalent as well.
For example, if for a particular subsample, the English trigram “mount of olives” occurs exactly in the same sentences as “montagne des oliviers” this gives rise to a concept, even if “olives” or “mountain” might not perfectly aligned in this particular subsample.
Figure 1 shows Lardilleux and Lepage (2009)’s anymalign algorithm. Given a sentence aligned parallel corpus, “alingual” sentences are created by concatenating each sentence across all languages. We then consider the set of all alingual sentences , which Lardilleux and Lepage (2009) call an alingual corpus. The core of the algorithm iterates the following loop: (i) draw a random sample of alingual sentences ; (ii) extract perfect alignments in . The perfect alignments are then added to the set of concepts.
Anymalign’s hyperparameters include the minimum number of languages a perfect alignment should cover (MINL) and the maximum ngram length (MAXN). The size of a subsample is adjusted automatically to maximize the probability that each sentence is sampled at least once. Obviously this probability depends on the number of samples drawn and thus on the runtime of the algorithm. Thus the runtime (T) is another hyperparameter. Note that T only affects the number of distinct concepts, not the quality of an individual concept. For details seeLardilleux and Lepage (2009).
Note that most members of a concept are neither hapax legomena nor perfect alignments in . A word can be part of multiple concepts within and obviously also within (it can be found in multiple iterations).
One can interpret the concept identification as a form of data augmentation. From an existing parallel corpus new parallel “sentences” are extracted by considering perfectly aligned subsequences.
2.2 Corpus Creation
Method S-ID. We adopt Levy and Goldberg (2014)’s framework; it formalizes the basic information that is passed to the embedding learner as a set of pairs. In the monolingual case each pair consists of two words that occur in the same context. A successful approach to multilingual embedding learning for parallel corpora is to use a corpus of pairs (one per line) of a word and a sentence ID Levy et al. (2017). We refer to this method as S-ID. Note that we use S-ID for the sentence identifier, for the corpus creation method that is based on these identifiers, for the embedding learning method based on such corpora and for the embeddings produced by the method. The same applies to C-ID. Which sense is meant should be clear from context.
Method C-ID. We use the same method for writing a corpus using our identified concepts and call this method C-ID. Figure 2 gives examples of the generated corpora that are passed to the embedding learner.
Method SC-ID. We combine S-IDs and C-IDs by simply concatenating their corpora before learning embeddings. However, we apply different frequency thresholds when learning embeddings from this corpus: for S-ID we investigate a frequency threshold on a development set whereas for C-ID we always set it to 1. As argued before each word in a concept carries a strong multilingual signal, which is why we do not apply any frequency filtering here. In the implementation we simply delete words in the S-ID part of the corpus with frequency lower than our threshold and set the minimum count parameter in word2vec during embedding learning to 1. Note that this is in line with how word2vec applies its minimum count threshold: words below the frequency threshold are simply ignored.
2.3 Embedding learning
We use word2vec skipgram111We use code.google.com/archive/p/word2vec Mikolov et al. (2013a) with default hyperparameters, except for three: number of iterations (ITER), minimum frequency of a word (MINC) and embedding dimension (DIM). For details see Table 2.
|English King James Version (KJV)||German Elberfelder 1905||Spanish Americas|
|For the earth bringeth forth fruit of herself ; first the blade , then the ear , after that the full corn in the ear||Die Erde bringt von selbst Frucht hervor , zuerst Gras , dann eine Ähre , dann vollen Weizen in der Ähre||La tierra produce fruto por sí misma ; primero la hoja , luego la espiga , y después el grano maduro en la espiga|
We work on PBC, the Parallel Bible Corpus Mayer and Cysouw (2014), a verse-aligned corpus of 1000+ translations of the New Testament. For the sake of comparability we use the same 1664 Bible editions across 1259 languages (distinct ISO 639-3 codes) and the same 6458 training verses as in Dufter et al. (2018).222See http://cistern.cis.lmu.de/comult We follow their terminology and refer to “translations” as “editions”. PBC is a good model for resource-poverty; e.g., the training set of KJV contains fewer than 150,000 tokens in 6458 verses. KJV spans a vocabulary of 6162 words while all 32 English editions together cover 23772 unique words. See Table 1 for an example verse. We use the tokenization provided in the data, which is erroneous for some hard-to-tokenize languages (e.g., Khmer, Japanese), and do not apply additional preprocessing.
woman women widows
marry wife woman
marriage virgin daughters
Example for a roundtrip translation with the query “woman”. Intermediates and predictions are ordered by cosine similarity. Intermediate edition: Spanish Americas. Figure taken fromDufter et al. (2018).
Dufter et al. (2018) introduce roundtrip translation as a multilingual embedding evaluation for languages for which no gold standard is available. We use this method for evaluating our methods on PBC. A query word in language is translated to its closest (with respect to embedding similarity) neighbor in and then backtranslated to its closest neighbor in . Roundtrip translation is successful if . As a measure of similarity we use cosine similarity. The above roundtrip is extended by considering intermediate neighbors and, for each intermediate neighbor, predictions in the query language.
The predictions are then compared to a groundtruth set . There is a strict and a relaxed groundtruth. The former only contains the query word whereas the latter contains all words with the same lemma and part-of-speech as . This accommodates the fact that for a single query multiple translations can be considered correct (e.g., an inflected form of a query). We average the binary results (per query and intermediate edition) over editions and report the mean and median over queries. Inspired by the precision@k evaluation measure for word translation we vary as follows: “S1” , “R1” , “S4” , and “S16” , where S stands for using the strict groundtruth, R for using the relaxed groundtruth.
If is not contained in the embedding space, then the predictions are counted as incorrect. The number of queries contained in the embedding space is denoted by . We use the same queries as in Dufter et al. (2018), which are based on Swadesh (1946)’s 100 universal words. As we perform hyperparameter selection we introduce a development set of queries: an earlier list by Swadesh that contains 215 words.333See concepticon.clld.org/contributions/Swadesh-1950-215 After excluding the queries from the test set, there are 151 queries in the development set. Due to this large number of queries we do not compute the relaxed measure as this requires manual creation of the groundtruth. We work on the level of Bible editions, i.e., two editions in the same language are considered different “languages”. We use KJV as the query edition if KJV contains the query word. Else we randomly choose another English edition.
We compute a diverse set of baselines to pinpoint reasons for performance changes as much as possible.
5.1 Context Based Embedding Space
We use Levy et al. (2017)’s method and call it S-ID.
5.2 Transformation Based Embedding Space
The baseline LINEAR follows Duong et al. (2017). We pick one edition as the “embedding space defining edition”, in our case we pick English KJV. We then create 1664 bilingual embedding spaces using S-ID; in each case the two editions covered are KJV and one of the other 1664 languages. We then use Mikolov et al. (2013b)
’s linear transformation method to map all embedding spaces to the KJV embedding space. More specifically, let, be two embedding spaces, with , number of words, and be the embedding dimension. We then select transformation words that are contained in both embedding spaces. This gives us , . The transformation matrix is then given by
where denotes the Frobenius norm. The closed form solution for the transformation is given by
where is the Moore-Penrose Pseudo inverse Penrose (1956). In our case and are bilingual embedding spaces where both contain the vocabulary of the English KJV edition.
5.3 Bilingual Embedding Spaces
In BILING we use the same bilingual embedding spaces as in LINEAR. However, we perform roundtrip translation in each embedding space separately, i.e., there is no transformation to a common space. This baselines allows us to assess the effect of a massively multilingual compared to having only bilingual embedding spaces.
5.4 Unsupervised Embedding Learning
We apply the recent unsupervised embedding learning method by Lample et al. (2018) and call it MUSE
. Given unaligned corpora in two languages, MUSE learns two separate embedding spaces that are subsequently unified by a linear transformation. This transformation is learned using a discriminator neural network that tries to identify the original language of a word vector. Subsequently a refinement step based on the Procrustes algorithm is performed. Note that this method only yields bilingual embedding spaces. As in BILING, we perform roundtrip translation in each embedding space separately.
Very recently, Chen and Cardie (2018) extended MUSE multilingually. We will include this in future work.
5.5 Non-Embedding-Space-Based Baseline
To show that the embedding space provides some advantages over just using the concepts as is, we introduce C-SIMPLE, a non-embedding baseline that follows the idea of roundtrip translation. Given a query word and an intermediate edition we consider all words that share a concept ID with the query word as possible intermediate words. We then choose randomly (probability weights according to their relative frequency across concept) intermediate words. For the back translation we apply the same procedure. This baseline is inspired by Dufter et al. (2018)’s RTSIMPLE baseline.
Word2vec. Since a grid search for optimal values for the parameters ITER, MINC and DIM would take too long, we search greedily instead. More iterations yield better performance. ITER = 100 is a good efficiency-performance trade-off. For MINC (minimum frequency of a word) the best performance is found using 2. This is mainly due to increased coverage. Surprisingly, smaller embedding dimensions work better to some degree. A highly multilingual embedding space is expected to suffer more from ambiguity and that is an argument for higher dimensionality; cf. Li and Jurafsky (2015)
. But this effect seems to be counteracted by the low-resource properties of PBC for which the increased number of parameters of higher dimensionalities cannot be estimated reliably. We choose embedding size 100.
C-ID. We use the tuned word2vec settings to investigate the hyperparameters for C-ID. As mentioned we set MINC to 1 whenever we learn on a concept based corpus. MINL is the minimum number of editions. We find the best performance when setting MINL to 100. As expected coverage worsens when chosing higher values for MINL. MAXN is the maximum ngram length. We see the best performance when setting MAXN to 3. This seems intuitive, as this allows for – at least in European languages – common word compounds. T is the time in hours, i.e., for how long to sample (which inherently steers the size and number of sample subcorpora). For T, 10 and 15 differ only slightly. We set T to 15 to get slightly higher coverage. See Table 3 for basic statistics about the identified concepts.
6.2 Roundtrip Translation Results
Table 4 presents roundtrip translation results on the test set.
C-Simple performs reasonably well, but is outperformed by almost all embedding spaces. This indicates that learning embeddings augments the information stored in concepts. LINEAR performs similar to C-Simple (but outperforms it for S16). This supports the hypothesis that PBC offers too little data for training mono-/bilingual embeddings. As expected BILING works better than LINEAR. However, keep in mind that this is not a universal embedding space and has fewer constraints than the other embedding spaces. Thus it is not directly comparable. MUSE works surprisingly well given that this method does not exploit the sentence alignment information in the corpus. While this method is ranked last in S1 and R1 () it gains traction on S4 and S16 where it ranks sixth in S16. However, the numbers are not directly comparable as no multilingual embedding space is created by MUSE (only 1664 bilingual ones). Thus there are fewer constraints overall. The performance drop between BILING and LINEAR can be considered as rough proxy for how much we would expect MUSE to worsen if considering a true multilingual embedding space. In future work we want to investigate this effect using Chen and Cardie (2018). Note that MUSE is quite inefficient: it takes around 1 hour to train a bilingual embedding space for a language pair on a standard GPU.
N(t) by Dufter et al. (2018) shows consistently the best performance. However, we will see later that this method only works in a massively multilingual setting and fails on EuroParl. SAMPLE by Dufter et al. (2018) is based on sampling like C-ID. However, it induces concepts only on a small set of pivot languages, not on all 1664 editions. This does not only work worse (C-ID beats SAMPLE except for S16), but it also requires additional word alignment information and is thus computationally more expensive.
As has been observed frequently, S-ID is highly effective. S-ID ranks consistently third. Representing a word as its binary vector of verse occurrence provides a clear signal to the embedding learner. Concept-based methods can be considered complementary to this feature set because they can exploit aggregated information across languages as well as across verses. C-ID alone has slightly lower performance. SC-ID, the combination, outperforms C-ID and S-ID and seems to be the “best of both worlds”. It yields consistently the best performance with 3% to 6% relative performance increase (for ) compared to S-ID alone and always ranks on the second place. In short: N(t) is the best method on this corpus followed by SC-ID and subsequently S-ID.
7 Application to a High-Resource Corpus
|Document||Word Similarity||QVEC||QVEC-CCA||Word Translation|
|multiCluster*||92.11 48.16||38.07 57.57||57.45 73.89||10.36 98.62||9.32 82.01||62.46 98.62||43.34 82.01||43.94 45.22|
|multiCCA*||92.18 62.81||43.09 71.09||69.99 77.94||10.75 99.09||8.78 87.03||63.50 99.09||41.52 87.03||36.21 54.87|
|multiSkip*||90.46 45.73||33.94 55.41||60.24 67.55||8.41 98.09||7.21 75.69||58.98 98.09||36.34 75.69||46.71 39.55|
|INVARIANCE*||91.10 31.35||51.08 23.06||59.13 62.50||8.11 91.71||5.38 74.78||65.83 91.71||46.21 74.78||63.91 30.36|
|S-ID||86.47 38.89||47.12 36.04||53.26 56.46||12.59 94.19||11.82 71.00||34.63 94.19||25.86 71.00||61.64 33.89|
|SC-ID||86.99 46.43||45.71 46.41||53.94 66.27||12.36 97.05||11.07 75.34||34.67 97.05||26.04 75.34||64.99 38.72|
LEFT: Results on several tasks on the test set. We downloaded their embedding spaces and performed the evaluation using their code. While almost all results are reproduced (except for rounding) we get slightly different results in the multilingual word similarity task and for multilingual QVEC (multiskip only).
PBC is a good model for learning multilingual embeddings in a low-resource, highly multilingual scenario. We now provide experimental evidence that SC-ID is broadly applicable and also works in a high-resource, mildly multilingual scenario. We test the three best performing methods (based on PBC S1 ): S-ID, SC-ID and N(t).
We choose a dataset published by Ammar et al. (2016),444See http://188.8.131.52/multilingual/data/ a parallel corpus covering 12 languages (Bulgarian, Czech, Danish, English, Finnish, French, German , Greek, Hungarian, Italian, Spanish, Swedish) from the proceedings of the European Parliament, Wikipedia titles and parallel news commentary. We refer to this corpus as EuroParl.
Ammar et al. (2016) provide an extensive evaluation framework covering one extrinsic (their dependency parsing models are not available for download, so we omit this part of the evaluation) and four intrinsic tasks that we use in this work. The tasks are document classification, word translation, word similarity, QVEC Tsvetkov et al. (2015) and QVEC-CCA Ammar et al. (2016). In addition there are 3 monolingual tasks (word similarity, QVEC and QVEC-CCA) that are all performed in English. Our main focus remains on word translation (precision@1 is reported). For all tasks there is a development and test set available. For a detailed description of the evaluation data and tasks see Ammar et al. (2016).
To ensure comparability with previous approaches we follow Ammar et al. (2016) in evaluating only on words that are contained in the embedding space and then simultaneously reporting the coverage (i.e., how many words of the translation task are actually contained in the embedding space). Note that this is different from our procedure on PBC. Throughout, subscript numbers in tables indicate coverage in percent.
We optimize corpus specific hyperparameters (e.g., number of languages a concept should contain) on the development set of the word translation task.
SC-ID: we vary MINC and the minimum number of languages that need to be covered by the concept identification algorithm. For S-ID we only vary MINC. N(t) requires pivot languages. Following Dufter et al. (2018) we choose as pivot languages those with the lowest type-token ratio (these are Greek, Danish, Spanish, French, Italian, English) and vary the number between 2, 4 and 6.
Table 6 gives an overview of our hyperparameter selection. For SC-ID, we choose MINL 6 and MINC 5 (compared to 2 in PBC). Given that this corpus provides much more information per language it seems intuitive that MINC should be higher (again note that for the C-ID part we always use MINC 1). This is confirmed for S-ID where we find the best performance with MINC 10. For N(t) the best result is obtained when using 4 pivot languages. N(t) exhibits an exceptionally low coverage, which obviously varies with the number of pivot languages: more pivot languages make it less likely that N(t) will find two words that have exactly the same neighbors in the dictionary graph.See Dufter et al. (2018) for details on N(t). Even for 2 pivot languages the coverage is too low to be useful for any application. We computed the coverage on the test set, which is only 0.56 when using 4 pivot languages. Still the precision is quite good. This indicates that N(t) is an effective method, but is only applicable to a corpus covering a large number of languages. Thus we do not report results on the test set.
Table 5 (RIGHT) gives results on the word translation test set. SC-ID performs best followed by INVARIANCE Huang et al. (2015). While INVARIANCE is theoretically applicable to PBC, there are strong indications why this is not possible in practice. INVARIANCE considers the full cooccurence matrix across all languages, a matrix that would be in the size of terabytes. In addition, word alignment matrices would need to be stored. The methods proposed by Ammar et al. (2016) yield reasonable, but clearly lower performance.
Table 5 (LEFT) gives results for the remaining tasks. Immediately it becomes clear that different embedding spaces have different strengths and weaknesses. The results for SC-ID are competitive throughout all tasks and there is no method which consistently outperforms SC-ID. SC-ID beats S-ID in 4 out of 7 tasks and in 3 out of 4 multilingual tasks. In addition SC-ID provides a significantly higher coverage throughout all tasks.
Overall SC-ID outperforms S-ID clearly on PBC and on word translation on EuroParl. On other tasks it outperforms S-ID in three out of four multilingual tasks and else has a competitive performance. None of the many baselines in this paper consistently outperforms SC-ID on both datasets. Thus, SC-ID is a simple baseline, yet a stronger one than S-ID.
8 Related Work
Much research has been dedicated to identifying multilingual concepts. BabelNet Navigli and Ponzetto (2012) leverages existing resources (mostly manual annotations), including Wikipedia using information extraction methods. While BabelNet could be directly used to learn concept based embeddings, it covers only 284 languages and thus cannot be applied to all PBC languages. Other work induces concepts within a dictionary graph Ammar et al. (2016); Dufter et al. (2018) or with alignment algorithms Östling (2014).
We now review multilingual embedding learning methods for parallel corpora. We have clustered them into three groups.
Group 1 consists of using monolingual algorithms for creating monolingual embedding spaces. Subsequently they are projected into a unified space using a (linear) transformation. We use Mikolov et al. (2013b) together with Duong et al. (2017) in our baseline LINEAR. Zou et al. (2013), Xiao and Guo (2014) and Faruqui and Dyer (2014) use similar approaches (e.g., by computing the transformation using CCA). Recently it has been shown that computing the transformation using discriminator neural networks works well, even in a completely unsupervised setting. See, e.g., Vulić and Moens (2012); Ammar et al. (2016); Lample et al. (2018); Chen and Cardie (2018). We used Lample et al. (2018) as a baseline.
Group 2 is true multilingual embedding learning: it integrates multilingual information in the objective of embedding learning. Klementiev et al. (2012) and Gouws et al. (2015) add a word alignment based term. Luong et al. (2015) introduce BiSkip as a bilingual extension of word2vec. For editions, including bilingual terms does not scale. A slightly different objective function expresses that representation of aligned sentences should be similar. Approaches based on neural networks are Hermann and Blunsom (2014a) (BiCVM), Sarath Chandar et al. (2014)
(autoencoders) andSoyer et al. (2014).
Group 3 creates multilingual corpora and uses monolingual embedding learners. A successful approach is Levy et al. (2017)’s sentence ID (S-ID). Vulić and Moens (2015) create pseudocorpora by merging words from multiple languages into a single corpus. Dufter et al. (2018) found this method to perform poorly on PBC. Søgaard et al. (2015) learn a space by factorizing an interlingual matrix based on Wikipedia concepts. word2vec is roughly equivalent to matrix factorization Levy and Goldberg (2014), so this work fits this category.
S-ID, the method for multilingual embedding learning proposed by Levy et al. (2017), is a surprisingly strong baseline. It exploits sentential context of words. In this paper, we showed that what we call C-ID – concepts learned by Lardilleux and Lepage (2009)’s method – is a competitive alternative. Concepts exploit the conceptual content of words. We demonstrated that SC-ID, the combination of concept information and context information, performs better by a large margin than each by itself. We provided experimental evidence for this conclusion on two datasets with different characteristics: one low-resource and highly multilingual (PBC) and one high-resource and mildly multilingual (EuroParl).
Acknowledgements. We gratefully acknowledge funding for this work by the European Research Council (ERC #740516) and by Zentrum Digitalisierung.Bayern (ZD.B), the digital technology initiative of the State of Bavaria.
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