Introduction
Neural language models BIBREF0 , BIBREF1 , BIBREF2 have become an essential component in several areas of natural language processing (NLP), such as machine translation, speech recognition and image captioning. They have also become a common benchmarking application in machine learning research on recurrent neural networks (RNN), because producing an accurate probabilistic model of human language is a very challenging task which requires all levels of linguistic analysis, from pragmatics to phonology, to be taken into account.
A typical language model is trained on text in a single language, and if one needs to model multiple languages the standard solution is to train a separate model for each language. This presupposes large quantities of monolingual data in each of the languages that needs to be covered and each model with its parameters is completely independent of any of the other models.
We propose instead to use a single model with real-valued vectors to indicate the language used, and to train this model with a large number of languages. We thus get a language model whose predictive distribution INLINEFORM0 is a continuous function of the language vector INLINEFORM1 , a property that is trivially extended to other neural NLP models. In this paper, we explore the “language space” containing these vectors, and in particular explore what happens when we move beyond the points representing the languages of the training corpus.
The motivation of combining languages into one single model is at least two-fold: First of all, languages are related and share many features and properties, a fact that is ignored when using independent models. The second motivation is data sparseness, an issue that heavily influences the reliability of data-driven models. Resources are scarce for most languages in the world (and also for most domains in otherwise well-supported languages), which makes it hard to train reasonable parameters. By combining data from many languages, we hope to mitigate this issue.
In contrast to related work, we focus on massively multilingual data sets to cover for the first time a substantial amount of the linguistic diversity in the world in a project related to data-driven language modeling. We do not presuppose any prior knowledge about language similarities and evolution and let the model discover relations on its own purely by looking at the data. The only supervision that is giving during training is a language identifier as a one-hot encoding. From that and the actual training examples, the system learns dense vector representations for each language included in our data set along with the character-level RNN parameters of the language model itself.
Related Work
Multilingual language models is not a new idea BIBREF3 , the novelty of our work lies primarily in the use of language vectors and the empirical evaluation using nearly a thousand languages.
Concurrent with this work, Johnson2016zeroshot conducted a study using neural machine translation (NMT), where a sub-word decoder is told which language to generate by means of a special language identifier token in the source sentence. This is close to our model, although beyond a simple interpolation experiment (as in our sec:generating) they did not further explore the language vectors, which would have been challenging to do given the small number of languages used in their study.
Ammar2016manylanguages used one-hot language identifiers as input to a multilingual word-based dependency parser, based on multilingual word embeddings. Given that they report this resulting in higher accuracy than using features from a typological database, it is a reasonable guess that their system learned language vectors which were able to encode syntactic properties relevant to the task. Unfortunately, they also did not look closer at the language vector space, which would have been interesting given the relatively large and diverse sample of languages represented in the Universal Dependencies treebanks.
Our evaluation in sec:clustering calls to mind previous work on automatic language classification, by Wichmann2010evaluating among others. However, our purpose is not to detect genealogical relationships, even though we use the strong correlation between such classifications and our language vectors as evidence that the vector space captures sensible information about languages.
Data
We base our experiments on a large collection of Bible translations crawled from the web, coming from various sources and periods of times. Any other multilingual data collection would work as well, but with the selected corpus we have the advantage that we cover the same genre and roughly the same coverage for each language involved. It is also easy to divide the data into training and test sets by using Bible verse numbers, which allows us to control for semantic similarity between languages in a way that would have been difficult in a corpus that is not multi-parallel. Altogether we have 1,303 translations in 990 languages that we can use for our purposes. These were chosen so that the model alphabet size is below 1000 symbols, which was satisfied by choosing only translations in Latin, Cyrillic or Greek script.
Certainly, there are disadvantages as well, such as the limited size (roughly 500 million tokens in total, with most languages having only one translation of the New Testament each, with roughly 200 thousand tokens), the narrow domain and the high overlap of named entities. The latter can lead to some unexpected effects when using nonsensical language vectors, as the model will then generate a sequence of random names.
The corpus deviates in some ways from an ideal multi-parallel corpus. Most translations are of the complete New Testament, whereas around 300 also contain the Old Testament (thus several times longer), and around ten contain only portions of the New Testament. Additionally, several languages have multiple translations, which are then concatenated. These translations may vary in age and style, but historical versions of languages (with their own ISO 639-3 code) are treated as distinct languages. During training we enforce a uniform distribution between languages when selecting training examples.
Methods
Our model is based on a standard stacked character-based LSTM BIBREF4 with two layers, followed by a hidden layer and a final output layer with softmax activations. The only modification made to accommodate the fact that we train the model with text in nearly a thousand languages, rather than one, is that language embedding vectors are concatenated to the inputs of the LSTMs at each time step and the hidden layer before the softmax. We used three separate embeddings for these levels, in an attempt to capture different types of information about languages. The model structure is summarized in fig:model.
In our experiments we use 1024-dimensional LSTMs, 128-dimensional character embeddings, and 64-dimensional language embeddings. Layer normalization BIBREF5 is used, but no dropout or other regularization since the amount of data is very large (about 3 billion characters) and training examples are seen at most twice. For smaller models early stopping is used. We use Adam BIBREF6 for optimization. Training takes between an hour and a few days on a K40 GPU, depending on the data size.
Results
In this section, we present several experiments with the model described. For exploring the language vector space, we use hierarchical agglomerative clustering for visualization. For measuring performance, we use cross-entropy on held out-data. For this, we use a set of the 128 most commonly translated Bible verses, to ensure that the held-out set is as large and overlapping as possible among languages.
Model capacity
Our first experiment tries to answer what happens when more and more languages are added to the model. There are two settings: adding languages in a random order, or adding the most closely related languages first. Cross-entropy plots for these settings are shown in fig:random and fig:swe.
In both cases, the model degrades gracefully (or even improves) for a number of languages, but then degrades linearly (i.e. exponential growth of perplexity) with exponentially increasing number of languages.
For comparison, fig:swesize compares this to the effect of decreasing the number of parameters in the LSTM by successively halving the hidden state size. Here the behavior is similar, but unlike the Swedish model which got somewhat better when closely related languages were added, the increase in cross-entropy is monotone. It would be interesting to investigate how the number of model parameters needs to be scaled up in order to accommodate the additional languages, but unfortunately the computational resources for such an experiment increases with the number of languages and would not be practical to carry out with our current equipment.
Structure of the language space
We now take a look at the language vectors found during training with the full model of 990 languages. fig:germanic shows a hierarchical clustering of the subset of Germanic languages, which closely matches the established genetic relationships in this language family. While our experiments indicate that finding more remote relationships (say, connecting the Germanic languages to the Celtic) is difficult for the model, it is clear that the language vectors preserves similarity properties between languages.
In additional experiments we found the overall structure of these clusterings to be relatively stable across models, but for very similar languages (such as Danish and the two varieties of Norwegian) the hierarchy might differ, and the some holds for languages or groups that are significantly different from the major groups. An example from fig:germanic is English, which is traditionally classified as a West Germanic language with strong influences from North Germanic as well as Romance languages. In the figure English is (weakly) grouped with the West Germanic languages, but in other experiments it is instead weakly grouped with North Germanic.
Generating Text
Since our language model is conditioned on a language vector, we can gain some intuitive understanding of the language space by generating text from different points in it. These points could be either one of the vectors learned during training, or some arbitrary other point. tab:interpolation shows text samples from different points along the line between Modern English [eng] and Middle English [enm]. Consistent with the results of Johnson2016zeroshot, it appears that the interesting region lies rather close to 0.5. Compare also to our fig:eng-deu, which shows that up until about a third of the way between English and German, the language model is nearly perfectly tuned to English.
Mixing and Interpolating Between Languages
By means of cross-entropy, we can also visualize the relation between languages in the multilingual space. Figure FIGREF12 plots the interpolation results for two relatively dissimilar languages, English and German. As expected, once the language vector moves too close to the German one, model performance drops drastically.
More interesting results can be obtained if we interpolate between two language variants and compute cross-entropy of a text that represents an intermediate form. fig:eng-enm shows the cross-entropy of the King James Version of the Bible (published 1611), when interpolating between Modern English (1500–) and Middle English (1050–1500). The optimal point turns out to be close to the midway point between them.
Language identification
If we have a sample of an unknown language or language variant, it is possible to estimate its language vector by backpropagating through the language model with all parameters except the language vector fixed. We found that a very small set of sentences is enough to give a considerable improvement in cross-entropy on held-out sentences. In this experiment, we used 32 sentences from the King James Version of the Bible. Using the resulting language vector, test set cross-entropy improved from 1.39 (using the Modern English language vector as initial value) to 1.35. This is comparable to the result obtained in sec:interpolation, except that here we do not restrict the search space to points on a straight line between two language vectors.
Conclusions
We have shown that language vectors, dense vector representations of natural languages, can be learned efficiently from raw text and possess several interesting properties. First, they capture language similarity to the extent that language family trees can be reconstructed by clustering the vectors. Second, they allow us to interpolate between languages in a sensible way, and even allow adopting the model using a very small set of text, simply by optimizing the language vector.

Question: Do they explore how their word representations vary across languages?
Answer: Yes

Yes

No

Question: Which neural language model architecture do they use?
Answer: character-level RNN

standard stacked character-based LSTM BIBREF4

LSTM

Question: How do they show genetic relationships between languages?
Answer: hierarchical clustering

By doing hierarchical clustering of word vectors

By applying hierarchical clustering on language vectors found during training