Understanding Semantic Implicit Learning through distributional linguistic patterns: A computational perspective

The research presented in this PhD dissertation provides a computational perspective on Semantic Implicit Learning (SIL). It puts forward the idea that SIL does not depend on semantic knowledge as classically conceived but upon semantic-like knowledge gained through distributional analysis of massive linguistic input. Using methods borrowed from the machine learning and artificial intelligence literature, we construct computational models, which can simulate the performance observed during behavioural tasks of semantic implicit learning in a human-like way. We link this methodology to the current literature on implicit learning, arguing that this behaviour is a necessary by-product of efficient language processing. Chapter 1 introduces the computational problem posed by implicit learning in general, and semantic implicit learning, in particular, as well as the computational framework, used to tackle them. Chapter 2 introduces distributional semantics models as a way to learn semantic-like representations from exposure to linguistic input. Chapter 3 reports two studies on large datasets of semantic priming which seek to identify the computational model of semantic knowledge that best fits the data under conditions that resemble SIL tasks. We find that a model which acquires semantic-like knowledge gained through distributional analysis of massive linguistic input provides the best fit to the data. Chapter 4 generalises the results of the previous two studies by looking at the performance of the same models in languages other than English. Chapter 5 applies the results of the two previous Chapters on eight datasets of semantic implicit learning. Crucially, these datasets use various semantic manipulations and speakers of different L1s enabling us to test the predictions of different models of semantics. Chapter 6 examines more closely two assumptions which we have taken for granted throughout this thesis. Firstly, we test whether a simpler model based on phonological information can explain the generalisation patterns observed in the tasks. Secondly, we examine whether our definition of the computational problem in Chapter 5 is reasonable. Chapter 7 summarises and discusses the implications for implicit language learning and computational models of cognition. Furthermore, we offer one more study that seeks to bridge the literature on distributional models of semantics to `deeper' models of semantics by learning semantic relations. There are two main contributions of this dissertation to the general field of implicit learning research. Firstly, we highlight the superiority of distributional models of semantics in modelling unconscious semantic knowledge. Secondly, we question whether `deep' semantic knowledge is needed to achieve above chance performance in SIIL tasks. We show how a simple model that learns through distributional analysis of the patterns found in the linguistic input can match the behavioural results in different languages. Furthermore, we link these models to more general problems faced in psycholinguistics such as language processing and learning of semantic relations.Alexandros Onassis Foundatio

Constructing and modeling text-rich information networks: a phrase mining-based approach

A lot of digital ink has been spilled on "big data" over the past few years, which is often characterized by an explosion of information. Most of this surge owes its origin to the unstructured data in the wild like words, images and video as comparing to the structured information stored in fielded form in databases. The proliferation of text-heavy data is particularly overwhelming, reflected in everyone's daily life in forms of web documents, business reviews, news, social posts, etc. In the mean time, textual data and structured entities often come in intertwined, such as authors/posters, document categories and tags, and document-associated geo locations. With this background, a core research challenge presents itself as how to turn massive, (semi-)unstructured data into structured knowledge. One promising paradigm studied in this dissertation is to integrate structured and unstructured data, constructing an organized heterogeneous information network, and developing powerful modeling mechanisms on such organized network. We name it text-rich information network, since it is an integrated representation of both structured and unstructured textual data. To thoroughly develop the construction and modeling paradigm, this dissertation will focus on forming a scalable data-driven framework and propose a new line of techniques relying on the idea of phrase mining to bridge textual documents and structured entities. We will first introduce the phrase mining method named SegPhrase+ to globally discover semantically meaningful phrases from massive textual data, providing a high quality dictionary for text structuralization. Clearly distinct from previous works that mostly focused on raw statistics of string matching, SegPhrase+ looks into the phrase context and effectively rectifies raw statistics to significantly boost the performance. Next, a novel algorithm based on latent keyphrases is developed and adopted to largely eliminate irregularities in massive text via providing an consistent and interpretable document representation. As a critical process in constructing the network, it uses the quality phrases generated in the previous step as candidates. From them a set of keyphrases are extracted to represent a particular document with inferred strength through a statistical model. After this step, documents become more structured and are consistently represented in the form of a bipartite network connecting documents with quality keyphrases. A more heterogeneous text-rich information network can be constructed by incorporating different types of document-associated entities as additional nodes. Lastly, a general and scalable framework, Tensor2vec, are to be added to trational data minining machanism, as the latter cannot readily solve the problem when the organized heterogeneous network has nodes with different types. Tensor2vec is expected to elegantly handle relevance search, entity classification, summarization and recommendation problems, by making use of higher-order link information and projecting multi-typed nodes into a shared low-dimensional vectorial space such that node proximity can be easily computed and accurately predicted

Generation of realistic human behaviour

As the use of computers and robots in our everyday lives increases so does the need for better interaction with these devices. Human-computer interaction relies on the ability to understand and generate human behavioural signals such as speech, facial expressions and motion. This thesis deals with the synthesis and evaluation of such signals, focusing not only on their intelligibility but also on their realism. Since these signals are often correlated, it is common for methods to drive the generation of one signal using another. The thesis begins by tackling the problem of speech-driven facial animation and proposing models capable of producing realistic animations from a single image and an audio clip. The goal of these models is to produce a video of a target person, whose lips move in accordance with the driving audio. Particular focus is also placed on a) generating spontaneous expression such as blinks, b) achieving audio-visual synchrony and c) transferring or producing natural head motion. The second problem addressed in this thesis is that of video-driven speech reconstruction, which aims at converting a silent video into waveforms containing speech. The method proposed for solving this problem is capable of generating intelligible and accurate speech for both seen and unseen speakers. The spoken content is correctly captured thanks to a perceptual loss, which uses features from pre-trained speech-driven animation models. The ability of the video-to-speech model to run in real-time allows its use in hearing assistive devices and telecommunications. The final work proposed in this thesis is a generic domain translation system, that can be used for any translation problem including those mapping across different modalities. The framework is made up of two networks performing translations in opposite directions and can be successfully applied to solve diverse sets of translation problems, including speech-driven animation and video-driven speech reconstruction.Open Acces

Machine Learning Models for Efficient and Robust Natural Language Processing

Natural language processing (NLP) has come of age. For example, semantic role labeling (SRL), which automatically annotates sentences with a labeled graph representing who did what to whom, has in the past ten years seen nearly 40% reduction in error, bringing it to useful accuracy. As a result, a myriad of practitioners now want to deploy NLP systems on billions of documents across many domains. However, state-of-the-art NLP systems are typically not optimized for cross-domain robustness nor computational efficiency. In this dissertation I develop machine learning methods to facilitate fast and robust inference across many common NLP tasks. First, I describe paired learning and inference algorithms for dynamic feature selection which accelerate inference in linear classifiers, the heart of the fastest NLP models, by 5-10 times. I then present iterated dilated convolutional neural networks (ID-CNNs), a distinct combination of network structure, parameter sharing and training procedures that increase inference speed by 14-20 times with accuracy matching bidirectional LSTMs, the most accurate models for NLP sequence labeling. Finally, I describe linguistically-informed self-attention (LISA), a neural network model that combines multi-head self-attention with multi-task learning to facilitate improved generalization to new domains. We show that incorporating linguistic structure in this way leads to substantial improvements over the previous state-of-the-art (syntax-free) neural network models for SRL, especially when evaluating out-of-domain. I conclude with a brief discussion of potential future directions stemming from my thesis work

A review on deep-learning-based cyberbullying detection

Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented

A Review of Deep Learning Techniques for Speech Processing

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field

Exploiting Cross-Lingual Representations For Natural Language Processing

Traditional approaches to supervised learning require a generous amount of labeled data for good generalization. While such annotation-heavy approaches have proven useful for some Natural Language Processing (NLP) tasks in high-resource languages (like English), they are unlikely to scale to languages where collecting labeled data is di cult and time-consuming. Translating supervision available in English is also not a viable solution, because developing a good machine translation system requires expensive to annotate resources which are not available for most languages. In this thesis, I argue that cross-lingual representations are an effective means of extending NLP tools to languages beyond English without resorting to generous amounts of annotated data or expensive machine translation. These representations can be learned in an inexpensive manner, often from signals completely unrelated to the task of interest. I begin with a review of different ways of inducing such representations using a variety of cross-lingual signals and study algorithmic approaches of using them in a diverse set of downstream tasks. Examples of such tasks covered in this thesis include learning representations to transfer a trained model across languages for document classification, assist in monolingual lexical semantics like word sense induction, identify asymmetric lexical relationships like hypernymy between words in different languages, or combining supervision across languages through a shared feature space for cross-lingual entity linking. In all these applications, the representations make information expressed in other languages available in English, while requiring minimal additional supervision in the language of interest

Unsupervised object candidate discovery for activity recognition

Die automatische Interpretation menschlicher Bewegungsabläufe auf Basis von Videos ist ein wichtiger Bestandteil vieler Anwendungen im Bereich des Maschinellen Sehens, wie zum Beispiel Mensch-Roboter Interaktion, Videoüberwachung, und inhaltsbasierte Analyse von Multimedia Daten. Anders als die meisten Ansätze auf diesem Gebiet, die hauptsächlich auf die Klassifikation von einfachen Aktionen, wie Aufstehen, oder Gehen ausgerichtet sind, liegt der Schwerpunkt dieser Arbeit auf der Erkennung menschlicher Aktivitäten, d.h. komplexer Aktionssequenzen, die meist Interaktionen des Menschen mit Objekten beinhalten. Gemäß der Aktionsidentifikationstheorie leiten menschliche Aktivitäten ihre Bedeutung nicht nur von den involvierten Bewegungsmustern ab, sondern vor allem vom generellen Kontext, in dem sie stattfinden. Zu diesen kontextuellen Informationen gehören unter anderem die Gesamtheit aller vorher furchgeführter Aktionen, der Ort an dem sich die aktive Person befindet, sowie die Menge der Objekte, die von ihr manipuliert werden. Es ist zum Beispiel nicht möglich auf alleiniger Basis von Bewegungsmustern und ohne jeglicher Miteinbeziehung von Objektwissen zu entschieden ob eine Person, die ihre Hand zum Mund führt gerade etwas isst oder trinkt, raucht, oder bloß die Lippen abwischt. Die meisten Arbeiten auf dem Gebiet der computergestützten Aktons- und Aktivitätserkennung ignorieren allerdings jegliche durch den Kontext bedingte Informationen und beschränken sich auf die Identifikation menschlicher Aktivitäten auf Basis der beobachteten Bewegung. Wird jedoch Objektwissen für die Klassifikation miteinbezogen, so geschieht dies meist unter Zuhilfenahme von überwachten Detektoren, für deren Einrichtung widerum eine erhebliche Menge an Trainingsdaten erforderlich ist. Bedingt durch die hohen zeitlichen Kosten, die die Annotation dieser Trainingsdaten mit sich bringt, wird das Erweitern solcher Systeme, zum Beispiel durch das Hinzufügen neuer Typen von Aktionen, zum eigentlichen Flaschenhals. Ein weiterer Nachteil des Hinzuziehens von überwacht trainierten Objektdetektoren, ist deren Fehleranfälligkeit, selbst wenn die verwendeten Algorithmen dem neuesten Stand der Technik entsprechen. Basierend auf dieser Beobachtung ist das Ziel dieser Arbeit die Leistungsfähigkeit computergestützter Aktivitätserkennung zu verbessern mit Hilfe der Hinzunahme von Objektwissen, welches im Gegensatz zu den bisherigen Ansätzen ohne überwachten Trainings gewonnen werden kann. Wir Menschen haben die bemerkenswerte Fähigkeit selektiv die Aufmerksamkeit auf bestimmte Regionen im Blickfeld zu fokussieren und gleichzeitig nicht relevante Regionen auszublenden. Dieser kognitive Prozess erlaubt es uns unsere beschränkten Bewusstseinsressourcen unbewusst auf Inhalte zu richten, die anschließend durch das Gehirn ausgewertet werden. Zum Beispiel zur Interpretation visueller Muster als Objekte eines bestimmten Typs. Die Regionen im Blickfeld, die unsere Aufmerksamkeit unbewusst anziehen werden als Proto-Objekte bezeichnet. Sie sind definiert als unbestimmte Teile des visuellen Informationsspektrums, die zu einem späteren Zeitpunkt durch den Menschen als tatsächliche Objekte wahrgenommen werden können, wenn er seine Aufmerksamkeit auf diese richtet. Einfacher ausgedrückt: Proto-Objekte sind Kandidaten für Objekte, oder deren Bestandteile, die zwar lokalisiert aber noch nicht identifiziert wurden. Angeregt durch die menschliche Fähigkeit solche visuell hervorstechenden (salienten) Regionen zuverlässig vom Hintergrund zu unterscheiden, haben viele Wissenschaftler Methoden entwickelt, die es erlauben Proto-Objekte zu lokalisieren. Allen diesen Algorithmen ist gemein, dass möglichst wenig statistisches Wissens über tatsächliche Objekte vorausgesetzt wird. Visuelle Aufmerksamkeit und Objekterkennung sind sehr eng miteinander vernküpfte Prozesse im visuellen System des Menschen. Aus diesem Grund herrscht auf dem Gebiet des Maschinellen Sehens ein reges Interesse an der Integration beider Konzepte zur Erhöhung der Leistung aktueller Bilderkennungssysteme. Die im Rahmen dieser Arbeit entwickelten Methoden gehen in eine ähnliche Richtung: wir demonstrieren, dass die Lokalisation von Proto-Objekten es erlaubt Objektkandidaten zu finden, die geeignet sind als zusätzliche Modalität zu dienen für die bewegungsbasierte Erkennung menschlicher Aktivitäten. Die Grundlage dieser Arbeit bildet dabei ein sehr effizienter Algorithmus, der die visuelle Salienz mit Hilfe von quaternionenbasierten DCT Bildsignaturen approximiert. Zur Extraktion einer Menge geeigneter Objektkandidaten (d.h. Proto-Objekten) aus den resultierenden Salienzkarten, haben wir eine Methode entwickelt, die den kognitiven Mechanismus des Inhibition of Return implementiert. Die auf diese Weise gewonnenen Objektkandidaten nutzen wir anschliessend in Kombination mit state-of-the-art Bag-of-Words Methoden zur Merkmalsbeschreibung von Bewegungsmustern um komplexe Aktivitäten des täglichen Lebens zu klassifizieren. Wir evaluieren das im Rahmen dieser Arbeit entwickelte System auf diversen häufig genutzten Benchmark-Datensätzen und zeigen experimentell, dass das Miteinbeziehen von Proto-Objekten für die Aktivitätserkennung zu einer erheblichen Leistungssteigerung führt im Vergleich zu rein bewegungsbasierten Ansätzen. Zudem demonstrieren wir, dass das vorgestellte System bei der Erkennung menschlicher Aktivitäten deutlich weniger Fehler macht als eine Vielzahl von Methoden, die dem aktuellen Stand der Technik entsprechen. Überraschenderweise übertrifft unser System leistungsmäßig sogar Verfahren, die auf Objektwissen aufbauen, welches von überwacht trainierten Detektoren, oder manuell erstellten Annotationen stammt. Benchmark-Datensätze sind ein sehr wichtiges Mittel zum quantitativen Vergleich von computergestützten Mustererkennungsverfahren. Nach einer Überprüfung aller öffentlich verfügbaren, relevanten Benchmarks, haben wir jedoch festgestellt, dass keiner davon geeignet war für eine detaillierte Evaluation von Methoden zur Erkennung komplexer, menschlicher Aktivitäten. Aus diesem Grund bestand ein Teil dieser Arbeit aus der Konzeption und Aufnahme eines solchen Datensatzes, des KIT Robo-kitchen Benchmarks. Wie der Name vermuten lässt haben wir uns dabei für ein Küchenszenario entschieden, da es ermöglicht einen großen Umfang an Aktivitäten des täglichen Lebens einzufangen, von denen viele Objektmanipulationen enthalten. Um eine möglichst umfangreiche Menge natürlicher Bewegungen zu erhalten, wurden die Teilnehmer während der Aufnahmen kaum eingeschränkt in der Art und Weise wie die diversen Aktivitäten auszuführen sind. Zu diesem Zweck haben wir den Probanden nur die Art der auszuführenden Aktivität mitgeteilt, sowie wo die benötigten Gegenstände zu finden sind, und ob die jeweilige Tätigkeit am Küchentisch oder auf der Arbeitsplatte auszuführen ist. Dies hebt KIT Robo-kitchen deutlich hervor gegenüber den meisten existierenden Datensätzen, die sehr unrealistisch gespielte Aktivitäten enthalten, welche unter Laborbedingungen aufgenommen wurden. Seit seiner Veröffentlichung wurde der resultierende Benchmark mehrfach verwendet zur Evaluation von Algorithmen, die darauf abzielen lang andauerne, realistische, komplexe, und quasi-periodische menschliche Aktivitäten zu erkennen

Native and non-native processing of morphologically complex words in Italian

The present work focuses on the organization of the mental lexicon in native and non-native speakers and aims at investigating whether words are connected in the mind in terms of morphological criteria, i.e., through a network of associations establishing when a co-occurrence of form and meaning is found. Psycholinguistic research on native lexical access has demonstrated that morphology indeed underlies the organization of the mental lexicon, even though controversies about the locus of this level of organization remain. On the other hand, research in the field of second language acquisition has only recently turned to investigate such issues and its findings so far have been controversial. Specifically, the debate centers on whether native and non-native speakers share the same processing systems. According to recent proposals (Heyer & Clahsen 2015), this would not be the case and L2 processing would be more affected by formal rather than morphological criteria. In this light, the present work is aimed at verifying the impact of formal characteristics in native and non-native lexical access focusing on the processing of formally transparent versus non-transparent words in Italian. Two morphological phenomena are investigated by means of four psycholinguistic experiments involving a lexical decision task combined with the masked priming paradigm. Experiments 1 & 2 compare the processing of allomorphic vs non-allomorphic derivatives, to investigate whether formal alterations impair the appreciation of the relationship between two morphologically related words. Experiments 3 & 4 are focused on lack of base autonomy found in so-called bound stems, i.e., stems which cannot occur in isolation and are aimed at determining whether the processing of free and bound stems differs. The results of Experiments 1 and 2 indicate that allomorphic variation does not influence the associations established among related words in native speakers, in line with the predictions that can be formulated within usage-based perspectives on language. Non-native speakers, on the other hand, seem to be more pervasively affected by the phonological/orthographical properties of words, but not to the point that transparent morphological relations can be reduced to mere form overlap shared by morphological relatives. Likewise, stem autonomy was not found to affect the way words containing bound and free stems are processed by native speakers, at least under certain conditions, suggesting that boundedness is not an issue influencing the establishment of morphological relationships among words. Non-native speakers, however, were found to be sensitive to the isolability of the stem, in a way that suggests that free bases may be more salient morphological units for them, as opposed to bound stems, which are seemingly more closely associated with orthographic strings resembling each other. Taken together, the findings of the present work suggest a model of the native mental lexicon based on words and morphological schemas emerging from the relationships establishing among them, despite phonological variations and stem boundedness. While it is unclear whether such a system of connections and schemas is equally strong in the non-native lexicon, morphological relationships still appear to drive lexical organization. Crucially, however, such organization is modulated by form, as demonstrated by the effects of phonological variations and lack of base autonomy

Multilingual representations and models for improved low-resource language processing

Word representations are the cornerstone of modern NLP. Representing words or characters using real-valued vectors as static representations that can capture the Semantics and encode the meaning has been popular among researchers. In more recent years, Pretrained Language Models using large amounts of data and creating contextualized representations achieved great performance in various tasks such as Semantic Role Labeling. These large pretrained language models are capable of storing and generalizing information and can be used as knowledge bases. Language models can produce multilingual representations while only using monolingual data during training. These multilingual representations can be beneficial in many tasks such as Machine Translation. Further, knowledge extraction models that only relied on information extracted from English resources, can now benefit from extra resources in other languages. Although these results were achieved for high-resource languages, there are thousands of languages that do not have large corpora. Moreover, for other tasks such as machine translation, if large monolingual data is not available, the models need parallel data, which is scarce for most languages. Further, many languages lack tokenization models, and splitting the text into meaningful segments such as words is not trivial. Although using subwords helps the models to have better coverage over unseen data and new words in the vocabulary, generalizing over low-resource languages with different alphabets and grammars is still a challenge. This thesis investigates methods to overcome these issues for low-resource languages. In the first publication, we explore the degree of multilinguality in multilingual pretrained language models. We demonstrate that these language models can produce high-quality word alignments without using parallel training data, which is not available for many languages. In the second paper, we extract word alignments for all available language pairs in the public bible corpus (PBC). Further, we created a tool for exploring these alignments which are especially helpful in studying low-resource languages. The third paper investigates word alignment in multiparallel corpora and exploits graph algorithms for extracting new alignment edges. In the fourth publication, we propose a new model to iteratively generate cross-lingual word embeddings and extract word alignments when only small parallel corpora are available. Lastly, the fifth paper finds that aggregation of different granularities of text can improve word alignment quality. We propose using subword sampling to produce such granularities