You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing 
Chapter Title:  Multilingual Representation Learning 
1. Abstract of Cited Paper (BIB001):  Distributed representations of words have proven extremely useful in numerous natural language processing tasks. Their appeal is that they can help alleviate data sparsity problems common to supervised learning. Methods for inducing these representations require only unlabeled language data, which are plentiful for many natural languages. In this work, we induce distributed representations for a pair of languages jointly. We treat it as a multitask learning problem where each task corresponds to a single word, and task relatedness is derived from co-occurrence statistics in bilingual parallel data. These representations can be used for a number of crosslingual learning tasks, where a learner can be trained on annotations present in one language and applied to test data in another. We show that our representations are informative by using them for crosslingual document classification, where classifiers trained on these representations substantially outperform strong baselines (e.g. machine translation) when applied to a new language. 
2. Abstract of Cited Paper (BIB002):  Dictionaries and phrase tables are the basis of modern statistical machine translation systems. This paper develops a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures based on large monolingual data and mapping between languages from small bilingual data. It uses distributed representation of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs. 
3. Abstract of Cited Paper (BIB003):  Distributed representations of meaning are a natural way to encode covariance relationships between words and phrases in NLP. By overcoming data sparsity problems, as well as providing information about semantic relatedness which is not available in discrete representations, distributed representations have proven useful in many NLP tasks. Recent work has shown how compositional semantic representations can successfully be applied to a number of monolingual applications such as sentiment analysis. At the same time, there has been some initial success in work on learning shared word-level representations across languages. We combine these two approaches by proposing a method for learning distributed representations in a multilingual setup. Our model learns to assign similar embeddings to aligned sentences and dissimilar ones to sentence which are not aligned while not requiring word alignments. We show that our representations are semantically informative and apply them to a cross-lingual document classification task where we outperform the previous state of the art. Further, by employing parallel corpora of multiple language pairs we find that our model learns representations that capture semantic relationships across languages for which no parallel data was used. 
4. Abstract of Cited Paper (BIB004):  This paper proposes to learn languageindependent word representations to address cross-lingual dependency parsing, which aims to predict the dependency parsing trees for sentences in the target language by training a dependency parser with labeled sentences from a source language. We first combine all sentences from both languages to induce real-valued distributed representation of words under a deep neural network architecture, which is expected to capture semantic similarities of words not only within the same language but also across different languages. We then use the induced interlingual word representation as augmenting features to train a delexicalized dependency parser on labeled sentences in the source language and apply it to the target sentences. To investigate the effectiveness of the proposed technique, extensive experiments are conducted on cross-lingual dependency parsing tasks with nine different languages. The experimental results demonstrate the superior cross-lingual generalizability of the word representation induced by the proposed approach, comparing to alternative comparison methods. 
5. Abstract of Cited Paper (BIB005):  Recent work on learning multilingual word representations usually relies on the use of word-level alignements (e.g. infered with the help of GIZA++) between translated sentences, in order to align the word embeddings in different languages. In this workshop paper, we investigate an autoencoder model for learning multilingual word representations that does without such word-level alignements. The autoencoder is trained to reconstruct the bag-of-word representation of given sentence from an encoded representation extracted from its translation. We evaluate our approach on a multilingual document classification task, where labeled data is available only for one language (e.g. English) while classification must be performed in a different language (e.g. French). In our experiments, we observe that our method compares favorably with a previously proposed method that exploits word-level alignments to learn word representations. 
6. Abstract of Cited Paper (BIB006):  Cross-language learning allows one to use training data from one language to build models for a different language. Many approaches to bilingual learning require that we have word-level alignment of sentences from parallel corpora. In this work we explore the use of autoencoder-based methods for cross-language learning of vectorial word representations that are coherent between two languages, while not relying on word-level alignments. We show that by simply learning to reconstruct the bag-of-words representations of aligned sentences, within and between languages, we can in fact learn high-quality representations and do without word alignments. We empirically investigate the success of our approach on the problem of cross-language text classification, where a classifier trained on a given language (e.g., English) must learn to generalize to a different language (e.g., German). In experiments on 3 language pairs, we show that our approach achieves state-of-the-art performance, outperforming a method exploiting word alignments and a strong machine translation baseline. 
7. Abstract of Cited Paper (BIB007):  We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple and computationally-efficient model for learning bilingual distributed representations of words which can scale to large monolingual datasets and does not require word-aligned parallel training data. Instead it trains directly on monolingual data and extracts a bilingual signal from a smaller set of raw-text sentence-aligned data. This is achieved using a novel sampled bag-of-words cross-lingual objective, which is used to regularize two noise-contrastive language models for efficient cross-lingual feature learning. We show that bilingual embeddings learned using the proposed model outperform state-of-the-art methods on a cross-lingual document classification task as well as a lexical translation task on WMT11 data. 
8. Abstract of Cited Paper (BIB008):  This paper investigates the problem of cross-lingual dependency parsing, aiming at inducing dependency parsers for low-resource languages while using only training data from a resource-rich language (e.g. English). Existing approaches typically don’t include lexical features, which are not transferable across languages. In this paper, we bridge the lexical feature gap by using distributed feature representations and their composition. We provide two algorithms for inducing cross-lingual distributed representations of words, which map vocabularies from two different languages into a common vector space. Consequently, both lexical features and non-lexical features can be used in our model for cross-lingual transfer. Furthermore, our framework is able to incorporate additional useful features such as cross-lingual word clusters. Our combined contributions achieve an average relative error reduction of 10.9% in labeled attachment score as compared with the delexicalized parser, trained on English universal treebank and transferred to three other languages. It also significantly outperforms McDonald et al. (2013) augmented with projected cluster features on identical data. 
9. Abstract of Cited Paper (BIB009):  We introduce a simple wrapper method that uses off-the-shelf word embedding algorithms to learn task-specific bilingual word embeddings. We use a small dictionary of easily-obtainable task-specific word equivalence classes to produce mixed context-target pairs that we use to train off-the-shelf embedding models. Our model has the advantage that it (a) is independent of the choice of embedding algorithm, (b) does not require parallel data, and (c) can be adapted to specific tasks by re-defining the equivalence classes. We show how our method outperforms off-the-shelf bilingual embeddings on the task of unsupervised cross-language partof-speech (POS) tagging, as well as on the task of semi-supervised cross-language super sense (SuS) tagging. 
10. Abstract of Cited Paper (BIB010):  We propose a simple yet effective approach to learning bilingual word embeddings (BWEs) from non-parallel document-aligned data (based on the omnipresent skip-gram model), and its application to bilingual lexicon induction (BLI). We demonstrate the utility of the induced BWEs in the BLI task by reporting on benchmarking BLI datasets for three language pairs: (1) We show that our BWE-based BLI models significantly outperform the MuPTM-based and context-counting models in this setting, and obtain the best reported BLI results for all three tested language pairs; (2) We also show that our BWE-based BLI models outperform other BLI models based on recently proposed BWEs that require parallel data for bilingual training. 
11. Abstract of Cited Paper (BIB011):  Recent work in learning bilingual representations tend to tailor towards achieving good performance on bilingual tasks, most often the crosslingual document classification (CLDC) evaluation, but to the detriment of preserving clustering structures of word representations monolingually. In this work, we propose a joint model to learn word representations from scratch that utilizes both the context coocurrence information through the monolingual component and the meaning equivalent signals from the bilingual constraint. Specifically, we extend the recently popular skipgram model to learn high quality bilingual representations efficiently. Our learned embeddings achieve a new state-of-the-art accuracy of 80.3 for the German to English CLDC task and a highly competitive performance of 90.7 for the other classification direction. At the same time, our models outperform best embeddings from past bilingual representation work by a large margin in the monolingual word similarity evaluation. 1 
12. Abstract of Cited Paper (BIB012):  Despite interest in using cross-lingual knowledge to learn word embeddings for various tasks, a systematic comparison of the possible approaches is lacking in the literature. We perform an extensive evaluation of four popular approaches of inducing cross-lingual embeddings, each requiring a different form of supervision, on four typographically different language pairs. Our evaluation setup spans four different tasks, including intrinsic evaluation on mono-lingual and cross-lingual similarity, and extrinsic evaluation on downstream semantic and syntactic applications. We show that models which require expensive cross-lingual knowledge almost always perform better, but cheaply supervised models often prove competitive on certain tasks. 
13. Abstract of Cited Paper (BIB013):  Crosslingual word embeddings represent lexical items from different languages in the same vector space, enabling transfer of NLP tools. However, previous attempts had expensive resource requirements, difficulty incorporating monolingual data or were unable to handle polysemy. We address these drawbacks in our method which takes advantage of a high coverage dictionary in an EM style training algorithm over monolingual corpora in two languages. Our model achieves state-of-the-art performance on bilingual lexicon induction task exceeding models using large bilingual corpora, and competitive results on the monolingual word similarity and cross-lingual document classification task. 
14. Abstract of Cited Paper (BIB014):  State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or English-Chinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation.

Literature Review:
The multilingual algorithms reviewed in § 3.1 and § 3.2 are facilitated by dense realvalued vector representations of words, known as multilingual word embeddings. These can be learned from corpora and provide pivotal lexical features to several downstream NLP applications. In multilingual word embeddings, similar words (regardless of the actual language) obtain similar representations. Various methods to generate multilingual word embeddings have been developed. We follow the classification proposed by BIB001 , and we refer the reader to BIB013 for an empirical comparison. Monolingual mapping generates independent monolingual representations and subsequently learns a linear map between a source language and a target language based on a bilingual lexicon BIB003 or in an unsupervised fashion through adversarial networks BIB015 . Alternatively, both spaces can be cast into a new, lower-dimensional space through canonical correlation analysis based on dictionaries  or word alignments BIB009 . Pseudo-cross-lingual approaches merge words with contexts of other languages and generate representations based on this mixed corpus. Substitutions are based on Wiktionary BIB005 or machine translation BIB010 BIB014 . Moreover, the mixed corpus can be produced by randomly shuffling words between aligned documents in two languages BIB011 . Cross-lingual training approaches jointly learn embeddings from parallel corpora and enforce cross-lingual constraints. This involves minimizing the distance of the hidden sentence representations of the two languages BIB004 or decoding one from the other BIB006 , possibly adding a correlation term to the loss BIB007 . Joint optimization typically involves learning distinct monolingual embeddings, while enforcing cross-lingual constraints. These can be based on alignment-based translations BIB002 , cross-lingual word contexts BIB012 , the average representations of parallel sentences BIB008 , or images  . In this section, we have briefly outlined the most widely used methods in multilingual NLP. Although they offer a solution to data scarcity, cross-lingual variation remains a challenge for transferring knowledge across languages or learning from several languages simultaneously. Typological information offers promising ways to address this problem. In particular, we have noted that it can support model transfer, parameter sharing, and input biasing through language vectors. In the next two sections, we elaborate on these solutions. In particular, we review the development of typological information and the specific features that are selected for various NLP tasks ( § 4). Afterward, we discuss ways in which these features are integrated in NLP algorithms, for which applications they have been harnessed, and whether they truly benefit system performance ( § 5).