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Word embeddings are the mechanism that enables the representation of words in a low dimensional vector space. This ability makes it intuitive to work with words and deduce their relationships, as words that are similar in meaning are projected close to each other in the vector space. A natural extension which maps different languages to one common space is what we refer to as multilingual embeddings. Methodologies for devising such embeddings depend on the granularity of the alignment information: word, sentence, documents or their combination and on whether they are trained from scratch or fine-tuned on top of monolingual embeddings.

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The strategies used for evaluating this kind of embeddings fall into two categories: intrinsic and extrinsic. Intrinsic evaluation focuses on directly testing the ability of those embeddings in capturing syntactic and semantic relationships between words in tasks like cross-lingual nearest neighbors and word translation. On the other hand, extrinsic evaluation indirectly examines their performance when used as input features for downstream semantic transfer tasks such as cross-lingual document classification, multilingual chatbot intent detection and so on. Existing benchmarks for extrinsic evaluation focus on the comparison between the different multilingual embeddings when used for training on one language and testing on a different language, while a comparison between the performance using multilingual and monolingual training modes is obviously lacking. One distinct advantage of multilingual over monolingual training is their efficiency, as it is time-consuming to train multiple language-specific models while one single multilingual model can be trained once and do the trick. But, is there any accuracy gain over monolingual models?

Determining the usefulness of those embeddings across different semantic understanding tasks in terms of the gain in accuracy of the multilingual model over language-specific models is the primary focus of this master thesis. Our motivation is that not all languages are equally good by their own for all semantic tasks. For some languages, there are plenty of training resources, while other languages are under-represented. One way to reduce the gap between languages is through the semantic transfer of annotation from rich to low resourced languages. Other interesting use cases are multinational manifestations, where multiple languages can be combined to detect any anomalies in customer behavior around the world. Accurate anomalies are more likely to be detected if a language-agnostic model is used.

In this work, we develop an end-to-end systemic benchmarking system which revisits the previous benchmark for cross-lingual document classification and tackles unexplored applications namely cross-lingual churn detection and event detection. Our aim is to reach clear conclusions regarding for which applications, type of multilingual embeddings and configuration is the gain more pronounced.

We treat cross-lingual document classification and churn detection as two instances of one general framework: text classification. We design different experiments leveraging deep learning with different feature extractors (CNN, GRU with Attention or both), aggregators and different levels of deepness. We pick a representative set of multilingual embeddings trained independently and feed them to our classification pipeline. In a second experiment, we multi-task learning the embeddings alongside with the application at hand. This is a multivariate analysis, as it investigates the transfer learning gain for different languages, multilingual embeddings and text architectures.

We observe a consistent gain across different tasks especially for low resourced languages and less complex text classification architectures regardless of the type of the multilingual embeddings used. We observe a general tendency of multilingual embeddings fined-tuned on top of monolingual embeddings using techniques such as SVD, CCA to perform better for simpler text classification models.

For cross-lingual document classification, our results show that the gain in performance is at its best when a multilingual model is trained over the aggregation of all languages using a simple multi-layer perceptron achieving an average increase per language of 4.47%. The gain is of 7.66%, 6.63% and 3.2% for Italian, French and German respectively which matches the ascending order of language resourcefulness. We also achieve state-of-the-art performance on cross-lingual churn detection using a bidirectional GRU with attention implemented on top of CNN when training multilingually with F1-scores of 85.88% and 78.09% for English and German respectively which accounts for an cross-lingual average increase of 6.52%. In the same manner, we are able to prove the ability of the same model to generalize well to chatbot conversations.

As far as event detection is concerned, the gain in favor of the multilingual approach is well pronounced, as it not only detects more event clusters but also those events are rich in content as the number of tweets to which they are linked is higher. These clusters are also shown to be better correlated with real-world sub-events in world cup 2018 dataset, such as goals, penalties and so on.

Other studies proposed their own algorithms, with some of the already established algorithms discussed above playing an important role in their implementation and/or comparison. Zimmermann et al. proposed a semi-supervised algorithm, the S*3Learner (Zimmermann et al. 2014) which suits changing opinion stream classification environments, where the vector of words evolves over time, with new words appearing and old words disappearing. Severyn et al. (2016) defined a novel and efficient tree kernel function, the Shallow syntactic Tree Kernel, for multi-class supervised sentiment classification of online comments. This study focused on YouTube which is multilingual, multimodal, multidomain and multicultural, with the aim to find whether the polarity of a comment is directed towards the source video, product described in the video or another product. Furthermore, Ignatov and Ignatov (2017) presented a novel DT-based algorithm, a Decision Stream, where Twitter sentiment analysis was one of several common machine learning problems that it was evaluated on. Lastly, Fatyanosa et al. (2018) enhanced the ability of the NB classifier with an optimisation algorithm, the Variable Length Chromosome Genetic Algorithm (VLCGA), thereby proposing VLCGA-NB for Twitter sentiment analysis.

The majority of the studies (354 out of 465) considered for this review analysis support one language in their SOM solutions. A total of 80 studies did not specify whether their proposed solution is language-agnostic or otherwise, or else their modality was not textual-based. Lastly, only 31 studies cater for more than one language, with 18 being bilingual, 1 being trilingual and 12 proposed solutions claiming to be multilingual. Regarding the latter, the majority were tested on a few languages at most, with Castellucci et al. (2015, 2015) on English and Italian, Montejo-Raez et al. (2014) on English and Spanish, Erdmann et al. (2014) on English and Japanese, Radhika and Sankar (2017) on English and MalayalamFootnote 71. Baccouche et al. (2018) on English, French and Arabic, Munezero et al. (2015) on keyword sets for different languages (e.g., Spanish, French), Wehrmann et al. (2017) on English, Spanish, Portuguese and German, Cui et al. (2011) on Basic Latin (English) and Extended Latin (Portuguese, Spanish, German), Teixeira and Laureano (2017) on Spanish, Italian, Portuguese, French, English, and Arabic, Zhang et al. (2017) on 8 languages, namely English, German, Portuguese, Spanish, Polish, Slovak, Slovenian, Swedish, and Gao et al. (2016) on 11 languages, namely English, Dutch, French, German, Italian, Polish, Portuguese, Russian, Spanish, Swedish and Turkish.

Moreover, Table 28 provides a list of the non-English languages identified from the 354 studies that support one language. Chou et al. (2017) claim that their method can be easily applied to any ConceptNetFootnote 72 supported language, with Wang et al. (2016) similarly claiming that their method is language independent, whereas the solution by Wang and Wu (2015) is multilingual given that emoticons are used in the majority of languages.

Social media can be seen as a sub-language that uses emoticons/emojis mixed with text to show emotions (Min et al. 2013). Emoticons/emojis are commonly used in tweets irrespective of the language, therefore are sometimes considered as being domain and language independent (Khan et al. 2014), thus useful for multilingual SOM (Cui et al. 2011).

Different languages and cultures result in various ways of how an opinion is expressed on certain social media platforms. For example, Sina Weibo users prefer to use irony when expressing negative polarity (Wang et al. 2014). Future research is required for the development of cross-lingual/multilingual NLP tools that are able to identify irony and sarcasm (Yan et al. 2014). be457b7860

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