Generating a lexicon of errors in Portuguese to support an error identification system for Spanish native learners

Portuguese is a less resourced language in what concerns foreign language learning. Aiming to inform a module of a system designed to support scientific written production of Spanish native speakers learning Portuguese, we developed an approach to automatically generate a lexicon of wrong words, reproducing language transfer errors made by such foreign learners. Each item of the artificially generated lexicon contains, besides the wrong word, the respective Spanish and Portuguese correct words. The wrong word is used to identify the interlanguage error and the correct Spanish and Portuguese forms are used to generate the suggestions. Keeping control of the correct word forms, we can provide correction or, at least, useful suggestions for the learners. We propose to combine two automatic procedures to obtain the error correction: i) a similarity measure and ii) a translation algorithm based on aligned parallel corpus. The similarity-based method achieved a precision of 52%, where as the alignment-based method achieved a precision of 90%. In this paper we focus only on interlanguage errors involving suffixes that have different forms in both languages. The approach, however, is very promising to tackle other types of errors, such as gender errors.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Computational approaches to semantic change (Volume 6)

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans

Predicting and Manipulating the Difficulty of Text-Completion Exercises for Language Learning

The increasing levels of international communication in all aspects of life lead to a growing demand of language skills. Traditional language courses compete nowadays with a wide range of online offerings that promise higher flexibility. However, most platforms provide rather static educational content and do not yet incorporate the recent progress in educational natural language processing. In the last years, many researchers developed new methods for automatic exercise generation, but the generated output is often either too easy or too difficult to be used with real learners. In this thesis, we address the task of predicting and manipulating the difficulty of text-completion exercises based on measurable linguistic properties to bridge the gap between technical ambition and educational needs. The main contribution consists of a theoretical model and a computational implementation for exercise difficulty prediction on the item level. This is the first automatic approach that reaches human performance levels and is applicable to various languages and exercise types. The exercises in this thesis differ with respect to the exercise content and the exercise format. As theoretical basis for the thesis, we develop a new difficulty model that combines content and format factors and further distinguishes the dimensions of text difficulty, word difficulty, candidate ambiguity, and item dependency. It is targeted at text-completion exercises that are a common method for fast language proficiency tests. The empirical basis for the thesis consists of five difficulty datasets containing exercises annotated with learner performance data. The difficulty is expressed as the ratio of learners who fail to solve the exercise. In order to predict the difficulty for unseen exercises, we implement the four dimensions of the model as computational measures. For each dimension, the thesis contains the discussion and implementation of existing measures, the development of new approaches, and an experimental evaluation on sub-tasks. In particular, we developed new approaches for the tasks of cognate production, spelling difficulty prediction, and candidate ambiguity evaluation. For the main experiments, the individual measures are combined into an machine learning approach to predict the difficulty of C-tests, X-tests and cloze tests in English, German, and French. The performance of human experts on the same task is determined by conducting an annotation study to provide a basis for comparison. The quality of the automatic prediction reaches the levels of human accuracy for the largest datasets. If we can predict the difficulty of exercises, we are able to manipulate the difficulty. We develop a new approach for exercise generation and selection that is based on the prediction model. It reaches high acceptance ratings by human users and can be directly integrated into real-world scenarios. In addition, the measures for word difficulty and candidate ambiguity are used to improve the tasks of content and distractor manipulation. Previous work for exercise difficulty was commonly limited to manual correlation analyses using learner results. The computational approach of this thesis makes it possible to predict the difficulty of text-completion exercises in advance. This is an important contribution towards the goal of completely automated exercise generation for language learning

Studying Evolutionary Change: Transdisciplinary Advances in Understanding and Measuring Evolution

Evolutionary processes can be found in almost any historical, i.e. evolving, system that erroneously copies from the past. Well studied examples do not only originate in evolutionary biology but also in historical linguistics. Yet an approach that would bind together studies of such evolving systems is still elusive. This thesis is an attempt to narrowing down this gap to some extend. An evolving system can be described using characters that identify their changing features. While the problem of a proper choice of characters is beyond the scope of this thesis and remains in the hands of experts we concern ourselves with some theoretical as well data driven approaches. Having a well chosen set of characters describing a system of different entities such as homologous genes, i.e. genes of same origin in different species, we can build a phylogenetic tree. Consider the special case of gene clusters containing paralogous genes, i.e. genes of same origin within a species usually located closely, such as the well known HOX cluster. These are formed by step- wise duplication of its members, often involving unequal crossing over forming hybrid genes. Gene conversion and possibly other mechanisms of concerted evolution further obfuscate phylogenetic relationships. Hence, it is very difficult or even impossible to disentangle the detailed history of gene duplications in gene clusters. Expanding gene clusters that use unequal crossing over as proposed by Walter Gehring leads to distinctive patterns of genetic distances. We show that this special class of distances helps in extracting phylogenetic information from the data still. Disregarding genome rearrangements, we find that the shortest Hamiltonian path then coincides with the ordering of paralogous genes in a cluster. This observation can be used to detect ancient genomic rearrangements of gene clus- ters and to distinguish gene clusters whose evolution was dominated by unequal crossing over within genes from those that expanded through other mechanisms. While the evolution of DNA or protein sequences is well studied and can be formally described, we find that this does not hold for other systems such as language evolution. This is due to a lack of detectable mechanisms that drive the evolutionary processes in other fields. Hence, it is hard to quantify distances between entities, e.g. languages, and therefore the characters describing them. Starting out with distortions of distances, we first see that poor choices of the distance measure can lead to incorrect phylogenies. Given that phylogenetic inference requires additive metrics we can infer the correct phylogeny from a distance matrix D if there is a monotonic, subadditive function ζ such that ζ^−1(D) is additive. We compute the metric-preserving transformation ζ as the solution of an optimization problem. This result shows that the problem of phylogeny reconstruction is well defined even if a detailed mechanistic model of the evolutionary process is missing. Yet, this does not hinder studies of language evolution using automated tools. As the amount of available and large digital corpora increased so did the possibilities to study them automatically. The obvious parallels between historical linguistics and phylogenetics lead to many studies adapting bioinformatics tools to fit linguistics means. Here, we use jAlign to calculate bigram alignments, i.e. an alignment algorithm that operates with regard to adjacency of letters. Its performance is tested in different cognate recognition tasks. Using pairwise alignments one major obstacle is the systematic errors they make such as underestimation of gaps and their misplacement. Applying multiple sequence alignments instead of a pairwise algorithm implicitly includes more evolutionary information and thus can overcome the problem of correct gap placement. They can be seen as a generalization of the string-to-string edit problem to more than two strings. With the steady increase in computational power, exact, dynamic programming solutions have become feasible in practice also for 3- and 4-way alignments. For the pairwise (2-way) case, there is a clear distinction between local and global alignments. As more sequences are consid- ered, this distinction, which can in fact be made independently for both ends of each sequence, gives rise to a rich set of partially local alignment problems. So far these have remained largely unexplored. Thus, a general formal frame- work that gives raise to a classification of partially local alignment problems is introduced. It leads to a generic scheme that guides the principled design of exact dynamic programming solutions for particular partially local alignment problems

Empirical studies in translation and discourse (Volume 14)

The present volume seeks to contribute some studies to the subfield of Empirical Translation Studies and thus aid in extending its reach within the field of translation studies and thus in making our discipline more rigorous and fostering a reproducible research culture. The Translation in Transition conference series, across its editions in Copenhagen (2013), Germersheim (2015) and Ghent (2017), has been a major meeting point for scholars working with these aims in mind, and the conference in Barcelona (2019) has continued this tradition of expanding the sub-field of empirical translation studies to other paradigms within translation studies. This book is a collection of selected papers presented at that fourth Translation in Transition conference, held at the Universitat Pompeu Fabra in Barcelona on 19–20 September 2019

Empirical studies in translation and discourse

The present volume seeks to contribute some studies to the subfield of Empirical Translation Studies and thus aid in extending its reach within the field of translation studies and thus in making our discipline more rigorous and fostering a reproducible research culture. The Translation in Transition conference series, across its editions in Copenhagen (2013), Germersheim (2015) and Ghent (2017), has been a major meeting point for scholars working with these aims in mind, and the conference in Barcelona (2019) has continued this tradition of expanding the sub-field of empirical translation studies to other paradigms within translation studies. This book is a collection of selected papers presented at that fourth Translation in Transition conference, held at the Universitat Pompeu Fabra in Barcelona on 19–20 September 2019

Information-theoretic causal inference of lexical flow

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

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

Learn Languages, Explore Cultures, Transform Lives

Selected Papers from the 2015 Central States Conference on the Teaching of Foreign Languages Aleidine J. Moeller, Editor 1. Creating a Culture-driven Classroom One Activity at a Time — Sharon Wilkinson, Patricia Calkins, & Tracy Dinesen 2. The Flipped German Classroom — Theresa R. Bell 3. Engaging Learners in Culturally Authentic Virtual Interactions —Diane Ceo-Francesco 4. Jouney to Global Competence: Learning Languages, Exploring Cultures, Transforming Lives — J. S. Orozco-Domoe 5. Strangers in a Strange Land: Perceptions of Culture in a First-year French Class — Rebecca L. Chism 6. 21st Century World Language Classrooms: Technology to Support Cultural Competence — Leah McKeeman & Blanca Oviedo 7. Effective Cloud-based Technologies to Maximize Language Learning — Katya Koubek & John C. Bedward 8. An Alternative to the Language Laboratory: Online and Face-to-face Conversation Groups — Heidy Cuervo Carruthers 9. Free Online Machine Translation: Use and Perceptions by Spanish Students and Instructors —Jason R. Jolley & Luciane Maimone 10. A Corpus-based Pedagogy for German Vocabulary — Colleen Neary-Sundquist 11. Grammar Teaching Approaches for Heritage Learners of Spanish —Clara Burgo 12. Going Online: Research-based Course Design — Elizabeth Harsm