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:  Figure 10 
1. Abstract of Cited Paper (BIB001):  We present a novel algorithm for multilingual dependency parsing that uses annotations from a diverse set of source languages to parse a new unannotated language. Our motivation is to broaden the advantages of multilingual learning to languages that exhibit significant differences from existing resource-rich languages. The algorithm learns which aspects of the source languages are relevant for the target language and ties model parameters accordingly. The model factorizes the process of generating a dependency tree into two steps: selection of syntactic dependents and their ordering. Being largely language-universal, the selection component is learned in a supervised fashion from all the training languages. In contrast, the ordering decisions are only influenced by languages with similar properties. We systematically model this cross-lingual sharing using typological features. In our experiments, the model consistently outperforms a state-of-the-art multi-lingual parser. The largest improvement is achieved on the non Indo-European languages yielding a gain of 14.4%. 
2. Abstract of Cited Paper (BIB002):  Accurate multilingual transfer parsing typically relies on careful feature engineering. In this paper, we propose a hierarchical tensor-based approach for this task. This approach induces a compact feature representation by combining atomic features. However, unlike traditional tensor models, it enables us to incorporate prior knowledge about desired feature interactions, eliminating invalid feature combinations. To this end, we use a hierarchical structure that uses intermediate embeddings to capture desired feature combinations. Algebraically, this hierarchical tensor is equivalent to the sum of traditional tensors with shared components, and thus can be effectively trained with standard online algorithms. In both unsupervised and semi-supervised transfer scenarios, our hierarchical tensor consistently improves UAS and LAS over state-of-theart multilingual transfer parsers and the traditional tensor model across 10 different languages. 1

Literature Review:
Performance of the surveyed algorithms for the tasks detailed in joint multilingual learning setting: Because it does not convey any information, it is of no avail in language transfer. Finally, let us consider the effectiveness of the methods described in §5.2 with respect to incorporating typological features in NLP models. In case of selective sharing, the tensor-based discriminative model BIB003 outperforms the graph-based discriminative model BIB001 , which in turn surpasses the generative model BIB002 . With regard to biasing multilingual models, there is a clear tendency toward letting typological features interact not merely with the input representation, but also with deeper levels of abstraction such as hidden layers. Overall, this comparison supports the claim that typology can potentially aid in designing the architecture of algorithms, engineering their features, and selecting and pre-processing their data. Nonetheless, this discussion also revealed that many challenges lie ahead for each of these goals to be accomplished fully. We discuss them in the next section.