. Often, these embeddings are only point estimates and do not capture any uncertainty in the estimates. Thus, any error in the estimated embeddings is propagated to the downstream tasks. This is especially important when the training data for the downstream classification task is scarce. In this paper, we present a Bayesian model that learns to represent document embeddings in the form of Gaussian distributions, thereby encoding the uncertainty within its covariance. Furthermore, the uncertainty is propagated to the classifier which exploits it for topic identification (ID) in a low-resource scenario. More specifically, we learn language-independent document embeddings which are used for zero-shot cross-lingual topic ID.
A closed-set monolingual topic ID or document classification in resource-rich scenarios is usually done with the help of discriminative models such as end-to-end neural network classifiers[Zhang et al.2015, Yang et al.2016] or pre-trained language models fine-tuned for classification [Howard and Ruder2018, Yang et al.2019]. In case of cross-lingual topic ID, where target data has little or no labels, learning a common embedding space for multiple (say, number of) languages is beneficial [Ammar et al.2016, Schwenk and Li2018, Ruder et al.2019]. This common embedding space is learnt by exploiting parallel dictionary or parallel sentences (translations) among the languages. Such a parallel data is not required to have any topic labels. A classifier is then trained on the embeddings from a source (src) language (one from the languages) that has topic labels. The same classifier is then used to classify the embeddings extracted for test data, which can be from any of the target (tar) languages. The underlying assumption here is that the embeddings carry semantic concept(s), independent of language, enabling cross-lingual transferability (src tar). Hence, the reliability of this scheme solely depends on quality of the embedding space. Note that the amount of available training data for the classifier could be limited and different from the parallel data, which is also the case for the experiments presented in this paper. To summarize:
We propose a Bayesian multilingual topic model (§ 2), which aims to learn a common low-dimensional subspace for document-specific unigram distributions from multiple languages. Moreover, the proposed model represents the document embeddings in the form of Gaussian distributions, thereby encoding the uncertainty in its covariance. We present two classifiers for zero-shot cross-lingual topic identification that exploit these uncertainties, (a) generative Gaussian linear classifier (§ 3.1
), and (b) discriminative multi-class logistic regression (§3.2).
The experiments on 5 European (EU) language subset of Reuters multi-lingual corpora (MLDoc) show that the proposed system outperforms: (a) multilingual word embedding based method (Multi-CCA
), and (b) neural machine translation based sequence-to-sequence bi-directional long short-term memory network (BiLSTM-EU) systems [Schwenk and Li2018]. We also show that our system, even when using relatively low amount of the parallel training data, performs competitively against the state-of-the-art universal sentence encoder trained on 93 languages (BiLSTM-93) [Artetxe and Schwenk2019].
Our experimental analysis (§ 6.2) shows that increasing the amount of parallel data improves the overall performance of the cross-lingual transfers. Nonetheless, exploiting the uncertainties during classification is always beneficial.
Like majority of the probabilistic topic models [Blei2012, Miao et al.2016], our model also relies on bag-of-words representation of documents. Let represent the vocabulary size in language . Let represent the language-specific model parameters, where is a low-rank matrix of size defines the subspace of document specific unigram distributions. Our multilingual model assumes that the -way parallel data (translations of bag-of-words) are generated according to the following process:
First, sample a -dimensional () language-independent, document-specific embedding from isotropic Gaussian prior distribution with precision :
can be interpreted as vector representing higher-level semantic concepts (topic alike) of a document, independent of any language. For each language, a vector of word counts is generated by the following two steps:
Compute the document-specific unigram distribution using the language-specific parameters:
Sample a vector of word counts :
where are the number of trials (word tokens in document ), i.e., .
represent -way parallel bag-of-words statistics.
The above steps describe the generative process of the proposed multilingual topic model. However, in reality, we do not generate any data, instead we invert the generative process: given the training (observed) data , we estimate the language-specific model parameters and also the posterior distributions of language-independent document embeddings . Moreover, given an unseen document from any of the languages, we infer the corresponding posterior distribution of the document embedding . Note that such a posterior distribution also carries the uncertainty about the estimate.
Although we describe the model assuming -way parallel data, in practice the model can be trained with parallel text (translations) between language pairs covering all the languages.
2.1 Variational Bayes training
The proposed model is trained using the variational Bayes framework, i.e., we approximate the intractable true posterior with the variational distribution:
and, optimize the evidence lower-bound [Bishop2006]. Further, we use Monte Carlo samples via the re-parametrization trick [Kingma and Welling2014, Rezende et al.2014] to approximate the expectation over -- term which appears in the lower-bound [Miao et al.2016, Kesiraju et al.2019]. The resulting lower-bound for a single set of -parallel documents in given by:
is the Kullback-Leibler divergence from variational distribution (4) to the prior (1) and, with . are the number of Monte Carlo samples used for empirically approximating the expectation over --. The derivation of the lower-bound for a monolingual case is given in [Kesiraju et al.2019].
The complete lower-bound is just the summation over all the documents. Additionally, we use regularization term with weight for language-specific model parameters . Thus, the final objective is
In practice, we follow batch-wise stochastic optimization of (6) using adam [Kingma and Ba2015]. In each iteration, we update the all model parameters and the corresponding posterior distributions of document embeddings .
2.2 Extracting embeddings for unseen documents
Given a bag-of-word statistics from an unseen document from any of the languages, we can infer (extract) the corresponding document embedding along with its uncertainty. This is done by keeping the language-specific model parameters fixed, and iteratively optimizing the objective in (5) with respect to the parameters of the variational distribution. In the resulting , the mean
represents the (most likely) document embedding, and varianceencodes the uncertainty around the mean .
3 Classification exploiting uncertainties
In a traditional scenario, where we have only point estimates of embeddings, all the embeddings are considered equally important by a classifier. This may not be true all the time. For example, shorter and ambiguous documents can result in poor estimates of the embeddings, which can affect the classifier during training and the performance during prediction. Since our proposed model yields document embeddings represented by Gaussian distributions, with the uncertainty about the embedding encoded in the covariance, we use two linear classifiers that can exploit this uncertainty. The first one is the generative Gaussian linear classifier with uncertainty (GLCU) [Kesiraju et al.2019]. The second one is the discriminative multi-class logistic regression with uncertainty (MCLRU).
3.1 Generative classifier
In general, for any classification task, we estimate the posterior probability of class label () given a feature vector (embedding)
where, is the likelihood function parametrized by , and is the class prior. In case of generative classifiers, the likelihood function is assumed to have a known parametric form (e.g. Gaussian, Multinomial).
For Gaussian linear classifier (GLC), the likelihood function is , where is the input feature (point estimate of the embedding), is the mean of class , and is the precision matrix shared across all the classes.
Given that the our input features (embeddings) come in the form of Gaussian distributions, i.e., , we can integrate out (exploit) the uncertainty in the input while evaluating the likelihood function. In case of generative Gaussian classifier, where the likelihood function (LABEL:eq:lh_glc) is also Gaussian, the expected likelihood has an analytical form [Cumani et al.2015, Kesiraju et al.2019]:
GLC with likelihood function replaced by (8) is called GLCU. Both are essentially the same classifiers, i.e., they have the same assumptions about the underlying data and hence the same model parameters. The only difference lies in the evaluation of likelihood function.
3.2 Discriminative classifier
For discriminative classifier such as multi-class logistic regression (MCLR), the posterior probability of class label () given an input feature vector is
where are the parameters of the classifier. Unlike in GLC, we cannot analytically compute the expectation over (9) with-respect-to the input features (Gaussian distributions). Instead we approximate the expectation using Monte Carlo samples [Kendall and Gal2017, Xiao and Wang2019]:
Eq. (10) represents the posterior probability computation for MCLRU.
Theoretically, given the true uncertainties in the training examples, GLCU and MCLRU can better estimate the model parameters of the classifier. Similarly, it can also exploit the uncertainties in the test examples during classification. See Appendix A for an illustration on synthetic data. However, in our case, the uncertainties are estimated using our Bayesian multilingual topic model as described in § 2.2. The underlying assumption here is that uncertainties extracted using our model are close enough to the true uncertainties as expected by the classifiers. This assumption is empirically supported through our experimental results presented in § 6.
4 Related works
4.1 Gaussian embeddings: modelling uncertainties
Recent works in NLP [Vilnis and McCallum2015, Sun et al.2018] represent word embeddings in the form of Gaussian distributions. Using the asymmetric KL divergence or the symmetric Wasserstein Distance, the uncertainty is exploited for word similarity, entailment and document classification tasks. Similar to the presented paper, [Xiao and Wang2019]
quantifies the uncertainties in the data and exploits it for sentiment analysis, named entity recognition, etc.
Gaussian embeddings extracted from spoken utterance, popularly known as i-vectors [Dehak et al.2011] were used for speaker identification, and verification tasks; and have been the state-of-the-art for several years [Kenny et al.2013]
. Ondel et al Ondel:2019:SHMM proposed a fully Bayesian subspace hidden Markov model for acoustic unit discovery from speech; where phone-like (acoustic) units from an unseen language are represented by Gaussian embeddings living in a subspace that was learnt using labelled data from other languages. Brümmer et al Brummer:2018:GE developed a theoretical framework around Gaussian embeddings for various classification and verification scenarios.
Kendall and Gal Kendall:2017:Uncert argued the importance of modelling uncertainty of safety critical applications in computer vision, and applied it for semantic segmentation and depth regression tasks.
4.2 Multilingual embeddings in NLP
Multilingualism in machine learning models can be achieved using word embeddings, or joint sentence (document) embeddings or pre-trained language models sharing a common vocabulary.
Ammar et al Ammar:2016:MMWE showed that word embeddings trained using monolingual corpora in several languages can be mapped to a common space (EN
) by exploiting parallel dictionaries. The authors used canonical correlation analysis (CCA) to learn these mappings. The mapped embeddings are used in a convolutional neural network for cross-lingual topic ID[Schwenk and Li2018].
Using parallel data (Europarl), Schwenk and Li Schwenk:2018:MLDoc trained a sequence-to-sequence (seq2seq) model comprising of BiLSTM layers to learn a common embedding space for sentences from multiple languages. In their model, each language has a separate encoder and decoder. A similar seq2seq model was proposed [Artetxe and Schwenk2019], where the authors used a joint byte-pair-encoding vocabulary over 93 languages. Further the encoder and decoder is shared across all the languages. The encoder is BiLSTM
with 5 layers, where as the decoder is a single LSTM layer, which additionally takes language ID (embedding) as input. Embeddings for new test data are obtained by forward propagating through the encoder. This is followed by a two hidden layered feed-forward neural network classifier for cross-lingual topic ID.
BERT [Devlin et al.2019] is a transformer based pre-trained language model. Multi-lingual BERT (mBERT) [Wu and Dredze2019] uses shared word piece vocabulary from 104 languages and aims to learn cross-lingual representations without any parallel data. On the other hand multilingual translation encoder (MMTE) [Siddhant et al.2020] uses the transformer architecture for neural machine translation, whose encoder is fine tuned for classification tasks.
5 Experimental setup
Europarl (v7) contains numerous parallel sentences between several European language pairs [Koehn2005]. We considered 5 languages namely, English (EN), German (DE), French (FR), Italian (IT) and Spanish (ES) and constructed multi-aligned sentences. Using English as reference, we retained sentences that are at least 40 words in length; which resulted in k multi-aligned sentences. These were used to train the proposed multi-lingual document embedding model. The maximum number of sentences are kk. In reality, not every sentence has a translation in all 5 languages. Later in § 6.2, we present the comparison of our systems with various amounts of parallel data.
MLDoc (Reuters multilingual corpus vols 1, and 2) is a collection of more than 800k news stories covering 4 topics in 13 languages including EN,DE, FR, IT and ES. Using the standardized data preparation framework [Schwenk and Li2018], we created 5 class-balanced splits, where each split has 1000 training, 1000 development and 4000 test documents. We report the average classification accuracy of the 5 splits.
The vocabulary was built using only the multi-aligned Europarl corpus. Table 2 presents the vocabulary statistics. All the words were lower-cased and punctuation was stripped. Further, words that do not occur in at least two sentences were removed.
5.3 Hyper-parameters and model configurations
The proposed Bayesian multilingual topic model has 2 important hyper-parameters, i.e., latent (embedding) dimension and regularization weight corresponding to the model parameters . Table 2 presents the list of hyper-parameters we explored in our experiments. The prior distribution (1) was set to and the variational distribution (4) was initialized to be the same as prior. This enabled us to same learning rate for both mean and variance parameters. A batch size of was used during training. A constant learning rate of was used both during training and inference. The model is trained for epochs and inference is done for iterations to obtain the posterior distributions.
The Gaussian linear classifier with uncertainty (GLCU) has no hyper-parameters to tune. We added regularization term with weight (Table 2) for the parameters of multi-class logistic regression (MCLR). The classifier was trained for a maximum 100 epochs using adam with a constant learning rate of . For multi-class logistic regression with uncertainty (MCLRU), we used for the empirical approximation (10). did not affect the classification performance significantly but, lower values degraded the performance for about 5%.
5.4 Proposed topic ID systems
The two linear classifiers GLC and MCLR use only the point estimates of the embeddings, i.e., they cannot exploit uncertainty during training and test. In the experiments we used only the mean parameter () as the point estimate of document embedding. Contrastingly, GLCU and MCLRU are trained with the full posterior distribution .
5.5 Baseline systems
Our baseline systems for comparison are based on multilingual word embeddings + CNN classifier (Multi-CCA) and BiLSTM based seq2seq models [Schwenk and Li2018]. We denote BiLSTM-EU [Schwenk and Li2018] as the system trained on 5 European languages similar to our systems.
Further, we also compare with the seq2seq BiLSTM trained on 93 languages sharing a common encoder [Artetxe and Schwenk2019]. We represent this as BiLSTM-93. Since the published work [Artetxe and Schwenk2019] only reports results from EN XX, we took the full matrix of results from the corresponding github repository maintained by the authors111https://github.com/facebookresearch/LASER/tree/master/tasks/mldoc. These are the improved results since the publication. BiLSTM-93 was trained on 16 NVIDIA V100 GPUs which took about 5 days [Artetxe and Schwenk2019].
Although all of these models use the same MLDoc corpus for cross-lingual topic ID, the multi-lingual embedding models are trained on different amounts of data comprising of various languages, hence we cannot directly compare all the models. However, we can compare BiLSTM-EU with our primary system, since both models use the same 5 European languages from Europarl.
6 Results and discussion
We present full matrix of results, i.e., all possible training-test combinations among the 5 languages. It shows the cross-lingual performance in all transfer directions, enabling a detailed understanding. Fig. 2 shows accuracy on the development for various regularization weights . We split the results into two parts: in language represents same source and target language pair, where as zero-shot transfer implies different source and target language pairs. Note that MCLR performs best on in language setting, whereas GLCU and MCLRU perform the best in zero-shot transfer setting. However, model selection was based only on the in language performance. For MCLRU, was found to give best results on the development set (in language average = ). Similarly, for GLCU, was found to give best results on the development set (in language average = ). These two are our primary systems; each of which has about 56 million parameters and took about 22 hours to train on a single NVIDIA Tesla P-100 GPU. Since the language-specific model parameters are independent inferring the embeddings can be easily parallelized.
6.1 Zero-shot cross-lingual transfer
Table 3 presents the zero-shot classification results of our primary system with GLCU and MCLRU respectively. These are the average accuracies from 5 test splits (§ 5.1). All the further comparisons are made with-respect-to these primary systems.
Table 4 shows the absolute differences in classification accuracy between our primary systems and each of the baseline systems. The positive bold value indicate the absolute improvement of our system as compared the respective baseline system. Note that the first two baseline systems are slightly better when training and test language are same, but significantly worse in transfer directions. This suggests that these models over-fit on the source language and generalizes poorly to the target languages.
As a specific example, by examining the results of Multi-CCA (Table 4 from [Schwenk and Li2018], alternatively, we can infer the same in Table 4 of this paper), it can be observed that the system performs better when training and testing on the same language. Moreover Multi-CCA is slightly better when transferring from EN XX, but relatively worse is other cases such as IT XX, and XX DE, suggesting a language bias in the embedding space. Note that our primary systems out performs Multi-CCA and BiLSTM-EU in majority of the transfer directions with significant margins, and more over performs competitively with the state-of-the-art BiLSTM-93 system. On an average, our primary systems (GLCU, MCLRU) are 9.2% and 5.6% better than Multi-CCAand BiLSTM-EU respectively; and only 1.6% worse than BiLSTM-93 in the zero-shot cross-lingual transfer (off-diagonal). Note that BiLSTM-93 is trained with 223M parallel sentences across 93 languages whereas our primary system is trained on just 730k parallel sentences across 5 languages.
|Test language (GLCU)||Test language (MCLRU)|
|Test language (GLCU)||Test language (MCLRU)|
|Multi-CCA [Schwenk and Li2018]|
|BiLSTM-EU [Schwenk and Li2018]|
|BiLSTM-93 [Artetxe and Schwenk2019]|
6.2 Significance of uncertainties in low-resource scenario
In this section, we compare the zero-shot topic ID performance of various classifiers with the embeddings extracted using our multilingual model. Given that we have only 1000 examples for training the classifiers, we can see the importance of modelling and utilizing uncertainties under such low-resource setting.
To better illustrate the importance of uncertainties, we trained GLC and MCLR with only the mean parameters, but during the test (prediction) time, we used the full posterior distributions (along with uncertainties) of the test document embeddings. This is valid because both GLC and GLCU have exactly the same model parameters (§ 3.1). Similarly MCLR and MCLRU are have exactly the same model parameters (§ 3.2). We represent these two classifiers as GLCU-P and MCLRU-P, where -P denotes uncertainty exploited only during prediction.
The comparisons with GLCU-P and MCLRU-P is presented in conjunction with the amount of parallel data that was used for training our multilingual embedding model. For simplicity, we present results in two parts, in language and zero-shot transfer. Figure 3 shows the average score on development set of all the 6 classifiers for varying amounts of parallel data. The overall performance of the systems increase slightly with the amount of parallel data. Nonetheless, exploiting the uncertainties, only even during the test time (GLCU-P, MCLRU-P) is always beneficial.
|Number of languages||Test language|
|System||in training data||EN||DE||FR||IT||ES|
|mBERT [Wu and Dredze2019]||104||94.20||80.20||72.60||68.90||72.60|
|MMTE [Siddhant et al.2020]||103||94.70||77.40||77.20||64.20||73.00|
|BiLSTM-93 [Artetxe and Schwenk2019]||93||90.73||86.25||78.03||70.20||79.30|
|Multi-CCA [Schwenk and Li2018]||5||92.20||81.20||72.38||69.38||72.50|
|BiLSTM-EU [Schwenk and Douze2017]||5||88.40||71.83||72.80||60.73||66.65|
|Primary system (GLCU)||5||86.99||83.90||80.23||65.14||72.60|
|Primary system (MCLRU)||5||87.04||83.04||78.39||64.40||73.51|
6.3 Results for reference
In Table 5, we present the cross-lingual topic ID results from the recently published works for reference. Note that all the systems were evaluated on MLDoc corpus, but the multilingual representation (embedding) model was trained on different amounts of data from various languages. Only BiLSTM-EU and our primary system are trained on the Europarl corpus with the same 5 languages. Moreover mBERT and BiLSTM-EU are models with relatively huge number of parameters which take enormous computational resources to train; whereas our model can be trained under a day on a single GPU.
In this paper, we presented a Bayesian multilingual topic model, which learns language-independent document embeddings along with their uncertainties. We propagated the uncertainties into a generative and discriminative linear classifier for zero-shot cross-lingual topic ID. Our systems out performed former state-of-the-art BiLSTM, and multilingual word embedding based system in majority of the transfer directions with significant margins. Moreover our systems perform competitively to the state-of-the-art universal sentence encoder, while only requiring fraction of training data and computational resources. Our detailed experiment analysis emphasizes the importance of modelling and exploiting uncertainties for cross-lingual topic ID.
Appendix A Gaussian linear classifier with uncertainty
The following Figure 4 compares Gaussian linear classifier (GLC) with Gaussian linear classifier with uncertainty (GLCU) on two dimensional synthetic data. Both GLC and GLCU are the same classifiers with same model parameters. The difference lies in the evaluation of the likelihood function. Given training data in the form of Gaussian distributions (uncertainty encoded in the covariance), GLCU can exploit this uncertainty to better estimate the model parameters and the corresponding decision boundaries.
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