A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings

Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in more realistic scenarios. This work proposes an alternative approach based on a fully unsupervised initialization that explicitly exploits the structural similarity of the embeddings, and a robust self-learning algorithm that iteratively improves this solution. Our method succeeds in all tested scenarios and obtains the best published results in standard datasets, even surpassing previous supervised systems. Our implementation is released as an open source project at https://github.com/artetxem/vecmapComment: ACL 201

Crosslingual Document Embedding as Reduced-Rank Ridge Regression

There has recently been much interest in extending vector-based word representations to multiple languages, such that words can be compared across languages. In this paper, we shift the focus from words to documents and introduce a method for embedding documents written in any language into a single, language-independent vector space. For training, our approach leverages a multilingual corpus where the same concept is covered in multiple languages (but not necessarily via exact translations), such as Wikipedia. Our method, Cr5 (Crosslingual reduced-rank ridge regression), starts by training a ridge-regression-based classifier that uses language-specific bag-of-word features in order to predict the concept that a given document is about. We show that, when constraining the learned weight matrix to be of low rank, it can be factored to obtain the desired mappings from language-specific bags-of-words to language-independent embeddings. As opposed to most prior methods, which use pretrained monolingual word vectors, postprocess them to make them crosslingual, and finally average word vectors to obtain document vectors, Cr5 is trained end-to-end and is thus natively crosslingual as well as document-level. Moreover, since our algorithm uses the singular value decomposition as its core operation, it is highly scalable. Experiments show that our method achieves state-of-the-art performance on a crosslingual document retrieval task. Finally, although not trained for embedding sentences and words, it also achieves competitive performance on crosslingual sentence and word retrieval tasks.Comment: In The Twelfth ACM International Conference on Web Search and Data Mining (WSDM '19

Do we really need fully unsupervised cross-lingual embeddings?

Recent efforts in cross-lingual word embedding (CLWE) learning have predominantly focused on fully unsupervised approaches that project monolingual embeddings into a shared cross-lingual space without any cross-lingual signal. The lack of any supervision makes such approaches conceptually attractive. Yet, their only core difference from (weakly) supervised projection-based CLWE methods is in the way they obtain a seed dictionary used to initialize an iterative self-learning procedure. The fully unsupervised methods have arguably become more robust, and their primary use case is CLWE induction for pairs of resource-poor and distant languages. In this paper, we question the ability of even the most robust unsupervised CLWE approaches to induce meaningful CLWEs in these more challenging settings. A series of bilingual lexicon induction (BLI) experiments with 15 diverse languages (210 language pairs) show that fully unsupervised CLWE methods still fail for a large number of language pairs (e.g., they yield zero BLI performance for 87/210 pairs). Even when they succeed, they never surpass the performance of weakly supervised methods (seeded with 500-1,000 translation pairs) using the same self-learning procedure in any BLI setup, and the gaps are often substantial. These findings call for revisiting the main motivations behind fully unsupervised CLWE methods

Multilingual word embeddings and their utility in cross-lingual learning

Word embeddings - dense vector representations of a word’s distributional semantics - are an indespensable component of contemporary natural language processing (NLP). Bilingual embeddings, in particular, have attracted much attention in recent years, given their inherent applicability to cross-lingual NLP tasks, such as Part-of-speech tagging and dependency parsing. However, despite recent advancements in bilingual embedding mapping, very little research has been dedicated to aligning embeddings multilingually, where word embeddings for a variable amount of languages are oriented to a single vector space. Given a proper alignment, one potential use case for multilingual embeddings is cross-lingual transfer learning, where a machine learning model trained on resource-rich languages (e.g. Finnish and Estonian) can “transfer” its salient features to a related language for which annotated resources are scarce (e.g. North Sami). The effect of the quality of this alignment on downstream cross-lingual NLP tasks has also been left largely unexplored, however. With this in mind, our work is motivated by two goals. First, we aim to leverage existing supervised and unsupervised methods in bilingual embedding mapping towards inducing high quality multilingual embeddings. To this end, we propose three algorithms (one supervised, two unsupervised) and evaluate them against a completely supervised bilingual system and a commonly employed baseline approach. Second, we investigate the utility of multilingual embeddings in two common cross-lingual transfer learning scenarios: POS-tagging and dependency parsing. To do so, we train a joint POS-tagger/dependency parser on Universal Dependencies treebanks for a variety of Indo-European languages and evaluate it on other, closely related languages. Although we ultimately observe that, in most settings, multilingual word embeddings themselves do not induce a cross-lingual signal, our experimental framework and results offer many insights for future cross-lingual learning experiments