127 research outputs found

    Tracking Typological Traits of Uralic Languages in Distributed Language Representations

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    Although linguistic typology has a long history, computational approaches have only recently gained popularity. The use of distributed representations in computational linguistics has also become increasingly popular. A recent development is to learn distributed representations of language, such that typologically similar languages are spatially close to one another. Although empirical successes have been shown for such language representations, they have not been subjected to much typological probing. In this paper, we first look at whether this type of language representations are empirically useful for model transfer between Uralic languages in deep neural networks. We then investigate which typological features are encoded in these representations by attempting to predict features in the World Atlas of Language Structures, at various stages of fine-tuning of the representations. We focus on Uralic languages, and find that some typological traits can be automatically inferred with accuracies well above a strong baseline.Comment: Finnish abstract included in the pape

    Uncovering Probabilistic Implications in Typological Knowledge Bases

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    The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have post-positions. Uncovering such implications typically amounts to time-consuming manual processing by trained and experienced linguists, which potentially leaves key linguistic universals unexplored. In this paper, we present a computational model which successfully identifies known universals, including Greenberg universals, but also uncovers new ones, worthy of further linguistic investigation. Our approach outperforms baselines previously used for this problem, as well as a strong baseline from knowledge base population.Comment: To appear in Proceedings of ACL 201

    Zero-Shot Cross-Lingual Transfer with Meta Learning

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    Learning what to share between tasks has been a topic of great importance recently, as strategic sharing of knowledge has been shown to improve downstream task performance. This is particularly important for multilingual applications, as most languages in the world are under-resourced. Here, we consider the setting of training models on multiple different languages at the same time, when little or no data is available for languages other than English. We show that this challenging setup can be approached using meta-learning, where, in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first. We experiment using standard supervised, zero-shot cross-lingual, as well as few-shot cross-lingual settings for different natural language understanding tasks (natural language inference, question answering). Our extensive experimental setup demonstrates the consistent effectiveness of meta-learning for a total of 15 languages. We improve upon the state-of-the-art for zero-shot and few-shot NLI (on MultiNLI and XNLI) and QA (on the MLQA dataset). A comprehensive error analysis indicates that the correlation of typological features between languages can partly explain when parameter sharing learned via meta-learning is beneficial.Comment: Accepted as long paper in EMNLP2020 main conferenc

    A Probabilistic Generative Model of Linguistic Typology

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    In the principles-and-parameters framework, the structural features of languages depend on parameters that may be toggled on or off, with a single parameter often dictating the status of multiple features. The implied covariance between features inspires our probabilisation of this line of linguistic inquiry---we develop a generative model of language based on exponential-family matrix factorisation. By modelling all languages and features within the same architecture, we show how structural similarities between languages can be exploited to predict typological features with near-perfect accuracy, outperforming several baselines on the task of predicting held-out features. Furthermore, we show that language embeddings pre-trained on monolingual text allow for generalisation to unobserved languages. This finding has clear practical and also theoretical implications: the results confirm what linguists have hypothesised, i.e.~that there are significant correlations between typological features and languages.Comment: NAACL 2019, 12 page

    Information-theoretic causal inference of lexical flow

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    This volume seeks to infer large phylogenetic networks from phonetically encoded lexical data and contribute in this way to the historical study of language varieties. The technical step that enables progress in this case is the use of causal inference algorithms. Sample sets of words from language varieties are preprocessed into automatically inferred cognate sets, and then modeled as information-theoretic variables based on an intuitive measure of cognate overlap. Causal inference is then applied to these variables in order to determine the existence and direction of influence among the varieties. The directed arcs in the resulting graph structures can be interpreted as reflecting the existence and directionality of lexical flow, a unified model which subsumes inheritance and borrowing as the two main ways of transmission that shape the basic lexicon of languages. A flow-based separation criterion and domain-specific directionality detection criteria are developed to make existing causal inference algorithms more robust against imperfect cognacy data, giving rise to two new algorithms. The Phylogenetic Lexical Flow Inference (PLFI) algorithm requires lexical features of proto-languages to be reconstructed in advance, but yields fully general phylogenetic networks, whereas the more complex Contact Lexical Flow Inference (CLFI) algorithm treats proto-languages as hidden common causes, and only returns hypotheses of historical contact situations between attested languages. The algorithms are evaluated both against a large lexical database of Northern Eurasia spanning many language families, and against simulated data generated by a new model of language contact that builds on the opening and closing of directional contact channels as primary evolutionary events. The algorithms are found to infer the existence of contacts very reliably, whereas the inference of directionality remains difficult. This currently limits the new algorithms to a role as exploratory tools for quickly detecting salient patterns in large lexical datasets, but it should soon be possible for the framework to be enhanced e.g. by confidence values for each directionality decision

    Information-theoretic causal inference of lexical flow

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    This volume seeks to infer large phylogenetic networks from phonetically encoded lexical data and contribute in this way to the historical study of language varieties. The technical step that enables progress in this case is the use of causal inference algorithms. Sample sets of words from language varieties are preprocessed into automatically inferred cognate sets, and then modeled as information-theoretic variables based on an intuitive measure of cognate overlap. Causal inference is then applied to these variables in order to determine the existence and direction of influence among the varieties. The directed arcs in the resulting graph structures can be interpreted as reflecting the existence and directionality of lexical flow, a unified model which subsumes inheritance and borrowing as the two main ways of transmission that shape the basic lexicon of languages

    A typology of questions in Northeast Asia and beyond: An ecological perspective

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    This study investigates the distribution of linguistic and specifically structural diversity in Northeast Asia (NEA), defined as the region north of the Yellow River and east of the Yenisei. In particular, it analyzes what is called the grammar of questions (GQ), i.e., those aspects of any given language that are specialized for asking questions or regularly combine with these. The bulk of the study is a bottom-up description and comparison of GQs in the languages of NEA. The addition of the phrase and beyond to the title of this study serves two purposes. First, languages such as Turkish and Chuvash are included, despite the fact that they are spoken outside of NEA, since they have ties to (or even originated in) the region. Second, despite its focus on one area, the typology is intended to be applicable to other languages as well. Therefore, it makes extensive use of data from languages outside of NEA. The restriction to one category is necessary for reasons of space and clarity, and the process of zooming in on one region allows a higher resolution and historical accuracy than is usually the case in linguistic typology. The discussion mentions over 450 languages and dialects from NEA and beyond and gives about 900 glossed examples. The aim is to achieve both a cross-linguistically plausible typology and a maximal resolution of the linguistic diversity of Northeast Asia
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