Data-driven Language Typology

In this thesis we use statistical n-gram language models and the perplexity measure for language typology tasks. We interpret the perplexity of a language model as a distance measure when the model is applied on a phonetic transcript of a language the model wasn't originally trained on. We use these distance measures for detecting language families, detecting closely related languages, and for language family tree reproduction. We also study the sample sizes required to train the language models and make estimations on how large corpora are needed for the successful use of these methods. We find that trigram language models trained from automatically transcribed phonetic transcripts and the perplexity measure can be used for both detecting language families and for detecting closely related languages

External Lexical Information for Multilingual Part-of-Speech Tagging

Morphosyntactic lexicons and word vector representations have both proven useful for improving the accuracy of statistical part-of-speech taggers. Here we compare the performances of four systems on datasets covering 16 languages, two of these systems being feature-based (MEMMs and CRFs) and two of them being neural-based (bi-LSTMs). We show that, on average, all four approaches perform similarly and reach state-of-the-art results. Yet better performances are obtained with our feature-based models on lexically richer datasets (e.g. for morphologically rich languages), whereas neural-based results are higher on datasets with less lexical variability (e.g. for English). These conclusions hold in particular for the MEMM models relying on our system MElt, which benefited from newly designed features. This shows that, under certain conditions, feature-based approaches enriched with morphosyntactic lexicons are competitive with respect to neural methods

Reading Polish with Czech Eyes: Distance and Surprisal in Quantitative, Qualitative, and Error Analyses of Intelligibility

In CHAPTER I, I first introduce the thesis in the context of the project workflow in section 1. I then summarise the methods and findings from the project publications about the languages in focus. There I also introduce the relevant concepts and terminology viewed in the literature as possible predictors of intercomprehension and processing difficulty. CHAPTER II presents a quantitative (section 4) and a qualitative (section 5) analysis of the results of the cooperative translation experiments. The focus of this thesis – the language pair PL-CS – is explained and the hypotheses are introduced in section 6. The experiment website is introduced in section 7 with an overview over participants, the different experiments conducted and in which section they are discussed. In CHAPTER IV, free translation experiments are discussed in which two different sets of individual word stimuli were presented to Czech readers: (i) Cognates that are transformable with regular PL-CS correspondences (section 12) and (ii) the 100 most frequent PL nouns (section 13). CHAPTER V presents the findings of experiments in which PL NPs in two different linearisation conditions were presented to Czech readers (section 14.1-14.6). A short digression is made when I turn to experiments with PL internationalisms which were presented to German readers (14.7). CHAPTER VI discusses the methods and results of cloze translation experiments with highly predictable target words in sentential context (section 15) and random context with sentences from the cooperative translation experiments (section 16). A final synthesis of the findings, together with an outlook, is provided in CHAPTER VII.In KAPITEL I stelle ich zunächst die These im Kontext des Projektablaufs in Abschnitt 1 vor. Anschließend fasse ich die Methoden und Erkenntnisse aus den Projektpublikationen zu den untersuchten Sprachen zusammen. Dort stelle ich auch die relevanten Konzepte und die Terminologie vor, die in der Literatur als mögliche Prädiktoren für Interkomprehension und Verarbeitungsschwierigkeiten angesehen werden. KAPITEL II enthält eine quantitative (Abschnitt 4) und eine qualitative (Abschnitt 5) Analyse der Ergebnisse der kooperativen Übersetzungsexperimente. Der Fokus dieser Arbeit - das Sprachenpaar PL-CS - wird erläutert und die Hypothesen werden in Abschnitt 6 vorgestellt. Die Experiment-Website wird in Abschnitt 7 mit einer Übersicht über die Teilnehmer, die verschiedenen durchgeführten Experimente und die Abschnitte, in denen sie besprochen werden, vorgestellt. In KAPITEL IV werden Experimente zur freien Übersetzung besprochen, bei denen tschechischen Lesern zwei verschiedene Sätze einzelner Wortstimuli präsentiert wurden: (i) Kognaten, die mit regulären PL-CS-Korrespondenzen umgewandelt werden können (Abschnitt 12) und (ii) die 100 häufigsten PL-Substantive (Abschnitt 13). KAPITEL V stellt die Ergebnisse von Experimenten vor, in denen tschechischen Lesern PL-NP in zwei verschiedenen Linearisierungszuständen präsentiert wurden (Abschnitt 14.1-14.6). Einen kurzen Exkurs mache ich, wenn ich mich den Experimenten mit PL-Internationalismen zuwende, die deutschen Lesern präsentiert wurden (14.7). KAPITEL VI erörtert die Methoden und Ergebnisse von Lückentexten mit hochgradig vorhersehbaren Zielwörtern im Satzkontext (Abschnitt 15) und Zufallskontext mit Sätzen aus den kooperativen Übersetzungsexperimenten (Abschnitt 16). Eine abschließende Synthese der Ergebnisse und ein Ausblick finden sich in KAPITEL VII

A cross-lingual adaptation approach for rapid development of speech recognizers for learning disabled users

Building a voice-operated system for learning disabled users is a difficult task that requires a considerable amount of time and effort. Due to the wide spectrum of disabilities and their different related phonopathies, most approaches available are targeted to a specific pathology. This may improve their accuracy for some users, but makes them unsuitable for others. In this paper, we present a cross-lingual approach to adapt a general-purpose modular speech recognizer for learning disabled people. The main advantage of this approach is that it allows rapid and cost-effective development by taking the already built speech recognition engine and its modules, and utilizing existing resources for standard speech in different languages for the recognition of the users’ atypical voices. Although the recognizers built with the proposed technique obtain lower accuracy rates than those trained for specific pathologies, they can be used by a wide population and developed more rapidly, which makes it possible to design various types of speech-based applications accessible to learning disabled users.This research was supported by the project ‘Favoreciendo la vida autónoma de discapacitados intelectuales con problemas de comunicación oral mediante interfaces personalizados de reconocimiento automático del habla’, financed by the Centre of Initiatives for Development Cooperation (Centro de Iniciativas de Cooperación al Desarrollo, CICODE), University of Granada, Spain. This research was supported by the Student Grant Scheme 2014 (SGS) at the Technical University of Liberec

Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge

This paper provides a comprehensive analysis of the first shared task on End-to-End Natural Language Generation (NLG) and identifies avenues for future research based on the results. This shared task aimed to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena. Introducing novel automatic and human metrics, we compare 62 systems submitted by 17 institutions, covering a wide range of approaches, including machine learning architectures -- with the majority implementing sequence-to-sequence models (seq2seq) -- as well as systems based on grammatical rules and templates. Seq2seq-based systems have demonstrated a great potential for NLG in the challenge. We find that seq2seq systems generally score high in terms of word-overlap metrics and human evaluations of naturalness -- with the winning SLUG system (Juraska et al., 2018) being seq2seq-based. However, vanilla seq2seq models often fail to correctly express a given meaning representation if they lack a strong semantic control mechanism applied during decoding. Moreover, seq2seq models can be outperformed by hand-engineered systems in terms of overall quality, as well as complexity, length and diversity of outputs. This research has influenced, inspired and motivated a number of recent studies outwith the original competition, which we also summarise as part of this paper.Comment: Computer Speech and Language, final accepted manuscript (in press

Viseme-based Lip-Reading using Deep Learning

Research in Automated Lip Reading is an incredibly rich discipline with so many facets that have been the subject of investigation including audio-visual data, feature extraction, classification networks and classification schemas. The most advanced and up-to-date lip-reading systems can predict entire sentences with thousands of different words and the majority of them use ASCII characters as the classification schema. The classification performance of such systems however has been insufficient and the need to cover an ever expanding range of vocabulary using as few classes as possible is challenge. The work in this thesis contributes to the area concerning classification schemas by proposing an automated lip reading model that predicts sentences using visemes as a classification schema. This is an alternative schema to using ASCII characters, which is the conventional class system used to predict sentences. This thesis provides a review of the current trends in deep learning- based automated lip reading and analyses a gap in the research endeavours of automated lip-reading by contributing towards work done in the region of classification schema. A whole new line of research is opened up whereby an alternative way to do lip-reading is explored and in doing so, lip-reading performance results for predicting s entences from a benchmark dataset are attained which improve upon the current state-of-the-art. In this thesis, a neural network-based lip reading system is proposed. The system is lexicon-free and uses purely visual cues. With only a limited number of visemes as classes to recognise, the system is designed to lip read sentences covering a wide range of vocabulary and to recognise words that may not be included in system training. The lip-reading system predicts sentences as a two-stage procedure with visemes being recognised as the first stage and words being classified as the second stage. This is such that the second-stage has to both overcome the one-to-many mapping problem posed in lip-reading where one set of visemes can map to several words, and the problem of visemes being confused or misclassified to begin with. To develop the proposed lip-reading system, a number of tasks have been performed in this thesis. These include the classification of continuous sequences of visemes; and the proposal of viseme-to-word conversion models that are both effective in their conversion performance of predicting words, and robust to the possibility of viseme confusion or misclassification. The initial system reported has been testified on the challenging BBC Lip Reading Sentences 2 (LRS2) benchmark dataset attaining a word accuracy rate of 64.6%. Compared with the state-of-the-art works in lip reading sentences reported at the time, the system had achieved a significantly improved performance. The lip reading system is further improved upon by using a language model that has been demonstrated to be effective at discriminating between homopheme words and being robust to incorrectly classified visemes. An improved performance in predicting spoken sentences from the LRS2 dataset is yielded with an attained word accuracy rate of 79.6% which is still better than another lip-reading system trained and evaluated on the the same dataset that attained a word accuracy rate 77.4% and it is to the best of our knowledge the next best observed result attained on LRS2