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:  Extending the Usage to New Tasks and Features 
1. Abstract of Cited Paper (BIB001):  Recent work on bilingual Word Sense Disambiguation (WSD) has shown that a resource deprived language (L1) can benefit from the annotation work done in a resource rich language (L2) via parameter projection. However, this method assumes the presence of sufficient annotated data in one resource rich language which may not always be possible. Instead, we focus on the situation where there are two resource deprived languages, both having a very small amount of seed annotated data and a large amount of untagged data. We then use bilingual bootstrapping, wherein, a model trained using the seed annotated data of L1 is used to annotate the untagged data of L2 and vice versa using parameter projection. The untagged instances of L1 and L2 which get annotated with high confidence are then added to the seed data of the respective languages and the above process is repeated. Our experiments show that such a bilingual bootstrapping algorithm when evaluated on two different domains with small seed sizes using Hindi (L1) and Marathi (L2) as the language pair performs better than monolingual bootstrapping and significantly reduces annotation cost. 
2. Abstract of Cited Paper (BIB002):  This paper describes a set of exploratory experiments for a multilingual classification-based approach to Word Sense Disambiguation. Instead of using a predefined monolingual sense-inventory such as WordNet, we use a language-independent framework where the word senses are derived automatically from word alignments on a parallel corpus. We built five classifiers with English as an input language and translations in the five supported languages (viz. French, Dutch, Italian, Spanish and German) as classification output. The feature vectors incorporate both the more traditional local context features, as well as binary bag-of-words features that are extracted from the aligned translations. Our results show that the ParaSense multilingual WSD system shows very competitive results compared to the best systems that were evaluated on the SemEval-2010 Cross-Lingual Word Sense Disambiguation task for all five target languages. 
3. Abstract of Cited Paper (BIB003):  Semantic Role Labeling (SRL) has become one of the standard tasks of natural language processing and proven useful as a source of information for a number of other applications. We address the problem of transferring an SRL model from one language to another using a shared feature representation. This approach is then evaluated on three language pairs, demonstrating competitive performance as compared to a state-of-the-art unsupervised SRL system and a cross-lingual annotation projection baseline. We also consider the contribution of different aspects of the feature representation to the performance of the model and discuss practical applicability of this method. 1 Background and Motivation 
4. Abstract of Cited Paper (BIB004):  This article considers the task of automatically inducing role-semantic annotations in the FrameNet paradigm for new languages. We propose a general framework that is based on annotation projection, phrased as a graph optimization problem. It is relatively inexpensive and has the potential to reduce the human effort involved in creating role-semantic resources. Within this framework, we present projection models that exploit lexical and syntactic information. We provide an experimental evaluation on an English-German parallel corpus which demonstrates the feasibility of inducing high-precision German semantic role annotation both for manually and automatically annotated English data. 
5. Abstract of Cited Paper (BIB005):  We propose a cross-lingual framework for fine-grained opinion mining using bitext projection. The only requirements are a running system in a source language and word-aligned parallel data. Our method projects opinion frames from the source to the target language, and then trains a system on the target language using the automatic annotations. Key to our approach is a novel dependency-based model for opinion mining, which we show, as a byproduct, to be on par with the current state of the art for English, while avoiding the need for integer programming or reranking. In cross-lingual mode (English to Portuguese), our approach compares favorably to a supervised system (with scarce labeled data), and to a delexicalized model trained using universal tags and bilingual word embeddings. 
6. Abstract of Cited Paper (BIB006):  We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on typological information about the language to be predicted. We apply these to the problem of modeling phone sequences---a domain in which universal symbol inventories and cross-linguistically shared feature representations are a natural fit. Intrinsic evaluation on held-out perplexity, qualitative analysis of the learned representations, and extrinsic evaluation in two downstream applications that make use of phonetic features show (i) that polyglot models better generalize to held-out data than comparable monolingual models and (ii) that polyglot phonetic feature representations are of higher quality than those learned monolingually. 
7. Abstract of Cited Paper (BIB007):  Abstract This paper explores the ways in which the field of natural language processing (NLP) can and does benefit from work in linguistic typology. I describe the recent increase in interest in multilingual natural language processing and give a high-level overview of the field. I then turn to a discussion of how linguistic knowledge in general is incorporated in NLP technology before describing how typological results in particular are used. I consider both rule-based and machine learning approaches to NLP and review literature on predicting typological features as well as that which leverages such features. 
8. Abstract of Cited Paper (BIB008):  This work focuses on the rapid development of linguistic annotation tools for resource-poor languages. We experiment several cross-lingual annotation projection methods using Recurrent Neural Networks (RNN) models. The distinctive feature of our approach is that our multilingual word representation requires only a parallel corpus between the source and target language. More precisely, our method has the following characteristics: (a) it does not use word alignment information, (b) it does not assume any knowledge about foreign languages, which makes it applicable to a wide range of resource-poor languages, (c) it provides truly multilingual taggers. We investigate both uni- and bi-directional RNN models and propose a method to include external information (for instance low level information from POS) in the RNN to train higher level taggers (for instance, super sense taggers). We demonstrate the validity and genericity of our model by using parallel corpora (obtained by manual or automatic translation). Our experiments are conducted to induce cross-lingual POS and super sense taggers. 
9. Abstract of Cited Paper (BIB009):  Domain Adaptation (DA) techniques aim at enabling machine learning methods learn effective classifiers for a "target" domain when the only available training data belongs to a different "source" domain. In this paper we present the Distributional Correspondence Indexing (DCI) method for domain adaptation in sentiment classification. DCI derives term representations in a vector space common to both domains where each dimension re ects its distributional correspondence to a pivot, i.e., to a highly predictive term that behaves similarly across domains. Term correspondence is quantified by means of a distributional correspondence function (DCF). We propose a number of efficient DCFs that are motivated by the distributional hypothesis, i.e., the hypothesis according to which terms with similar meaning tend to have similar distributions in text. Experiments show that DCI obtains better performance than current state-of-the-art techniques for cross-lingual and cross-domain sentiment classification. DCI also brings about a significantly reduced computational cost, and requires a smaller amount of human intervention. As a final contribution, we discuss a more challenging formulation of the domain adaptation problem, in which both the cross-domain and cross-lingual dimensions are tackled simultaneously. 
10. Abstract of Cited Paper (BIB010):  Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors. However, it is not clear how much information such representations retain about the polarity of sentences. To study this question, we decode sentiment from unsupervised sentence representations learned with different architectures (sensitive to the order of words, the order of sentences, or none) in 9 typologically diverse languages. Sentiment results from the (recursive) composition of lexical items and grammatical strategies such as negation and concession. The results are manifold: we show that there is no `one-size-fits-all' representation architecture outperforming the others across the board. Rather, the top-ranking architectures depend on the language and data at hand. Moreover, we find that in several cases the additive composition model based on skip-gram word vectors may surpass supervised state-of-art architectures such as bidirectional LSTMs. Finally, we provide a possible explanation of the observed variation based on the type of negative constructions in each language. 
11. Abstract of Cited Paper (BIB011):  We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT using a single model. On the WMT’14 benchmarks, a single multilingual model achieves comparable performance for English→French and surpasses state-of-the-art results for English→German. Similarly, a single multilingual model surpasses state-of-the-art results for French→English and German→English on WMT’14 and WMT’15 benchmarks, respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. Our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and show some interesting examples when mixing languages. 
12. Abstract of Cited Paper (BIB012):  In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs. There have been several proposals to alleviate this issue with, for instance, triangulation and semi-supervised learning techniques, but they still require a strong cross-lingual signal. In this work, we completely remove the need of parallel data and propose a novel method to train an NMT system in a completely unsupervised manner, relying on nothing but monolingual corpora. Our model builds upon the recent work on unsupervised embedding mappings, and consists of a slightly modified attentional encoder-decoder model that can be trained on monolingual corpora alone using a combination of denoising and backtranslation. Despite the simplicity of the approach, our system obtains 15.56 and 10.21 BLEU points in WMT 2014 French-to-English and German-to-English translation. The model can also profit from small parallel corpora, and attains 21.81 and 15.24 points when combined with 100,000 parallel sentences, respectively. Our implementation is released as an open source project. 
13. Abstract of Cited Paper (BIB013):  Neural architectures are prominent in the construction of language models (LMs). However, word-level prediction is typically agnostic of subword-level information (characters and character sequences) and operates over a closed vocabulary, consisting of a limited word set. Indeed, while subword-aware models boost performance across a variety of NLP tasks, previous work did not evaluate the ability of these models to assist next-word prediction in language modeling tasks. Such subword-level informed models should be particularly effective for morphologically-rich languages (MRLs) that exhibit high type-to-token ratios. In this work, we present a large-scale LM study on 50 typologically diverse languages covering a wide variety of morphological systems, and offer new LM benchmarks to the community, while considering subword-level information. The main technical contribution of our work is a novel method for injecting subword-level information into semantic word vectors, integrated into the neural language modeling training, to facilitate word-level prediction. We conduct experiments in the LM setting where the number of infrequent words is large, and demonstrate strong perplexity gains across our 50 languages, especially for morphologically-rich languages. Our code and data sets are publicly available.

Literature Review:
The trends observed in § 5 reveal that typology is integrated into NLP models mostly in the context of morphosyntactic tasks, and particularly syntactic parsing. Some exceptions include other levels of linguistic structure, such as phonology BIB007  and semantics BIB008  . As a consequence, the set of selected typological features is mostly limited to a handful of word-order features from a single database, WALS. Nonetheless, the array of tasks that pertain to polyglot NLP is broad, and other typological data sets that have thus far been neglected may be relevant for them. For example, typological frame semantics might benefit semantic role labeling, as it specifies the valency patterns of predicates across languages, including the number of arguments, their morphological markers, and their order. This information can be cast in the form of priors for unsupervised syntax-based Bayesian models , guidance for alignments in annotation projection BIB005 Van der Plas, Merlo, and Henderson 2011) , or regularizers for model transfer in order to tailor the source model to the grammar of the target language BIB004 . Cross-lingual information about frame semantics can be extracted, for example, from the Valency Patterns Leipzig database (ValPaL). Typological information regarding lexical semantics patterns can further assist various NLP tasks by providing information about translationally equivalent words across languages. Such information is provided in databases such as the World Loanword Database (WOLD), the Intercontinental Dictionary Series (IDS), and the Automated Similarity Judgment Program (ASJP). One example task is word sense disambiguation, as senses can be propagated from multilingual word graphs BIB001 by bootstrapping from a few pivot pairs BIB002 , by imposing constraints in sentence alignments and harvesting bag-of-words features from these BIB003 , or by providing seeds for multilingual Word-Embedding-based lexicalized model transfer BIB009 . Another task where lexical semantics is crucial is sentiment analysis, for similar reasons: Bilingual lexicons constrain word alignments for annotation projection BIB006 and provide pivots for shared multilingual representations in model transfer BIB010  . Moreover, sentiment analysis can leverage morphosyntactic typological information about constructions that alter polarity, such as negation BIB011 . Finally, morphological information was shown to aid interpreting the intrinsic difficulty of texts for language modeling and neural machine translation, both in supervised BIB012 ) and in unsupervised BIB013 ) set-ups. In fact, the degree of fusion between roots and inflectional/derivative morphemes impacts the type/token ratio of texts, and consequently their rate of infrequent words. Moreover, the ambiguity of mapping between form and meaning of morphemes determines the usefulness of injecting character-level information BIB014  . This variation has to be taken into account in both language transfer and multilingual joint learning. As a final note, we stress that the addition of new features does not concern just future work, but also the existing typology-savvy methods, which can widen their scope. For instance, the parsing experiments grounded on selective sharing ( § 5.2) could also take into consideration WALS features about Nominal Categories, Nominal Syntax, Verbal Categories, Simple Clauses, and Complex Sentences, as well as features from other databases such as SSWL, APiCS, and AUTOTYP. Likewise, models for phonological tasks BIB007  could also extract features from typological databases such as LAPSyD and StressTyp2.