Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing

Deep Learning Methods for Dialogue Act Recognition using Visual Information

Rozpoznávání dialogových aktů (DA) je důležitým krokem v řízení a porozumění dialogu. Tato úloha spočívá v automatickém přiřazení třídy k výroku/promluvě (nebo jeho části) na základě jeho funkce v dialogu (např. prohlášení, otázka, potvrzení atd.). Takováto klasifikace pak pomáhá modelovat a identifikovat strukturu spontánních dialogů. I když je rozpoznávání DA obvykle realizováno na zvukovém signálu (řeči) pomocí modelů pro automatické rozpoznávání řeči, dialogy existují rovněž ve formě obrázků (např. komiksy). Tato práce se zabývá automatickým rozpoznáváním dialogových aktů z obrazových dokumentů. Dle nás se jedná o první pokus o navržení přístupu rozpoznávání DA využívající obrázky jako vstup. Pro tento úkol je nutné extrahovat text z obrázků. Využíváme proto algoritmy z oblasti počítačového vidění a~zpracování obrazu, jako je prahování obrazu, segmentace textu a optické rozpoznávání znaků (OCR). Hlavním přínosem v této oblasti je návrh a implementace OCR modelu založeného na konvolučních a rekurentních neuronových sítích. Také prozkoumáváme různé strategie pro trénování tohoto modelu, včetně generování syntetických dat a technik rozšiřování dat (tzv. augmentace). Dosahujeme vynikajících výsledků OCR v případě, kdy je malé množství trénovacích dat. Mezi naše přínosy tedy patří to, jak vytvořit efektivní OCR systém s~minimálními náklady na ruční anotaci. Dále se zabýváme vícejazyčností v oblasti rozpoznávání DA. Úspěšně jsme použili a nasadili obecný model, který byl trénován všemi dostupnými jazyky, a také další modely, které byly trénovány pouze na jednom jazyce, a vícejazyčnosti je dosaženo pomocí transformací sémantického prostoru. Také zkoumáme techniku přenosu učení (tzv. transfer learning) pro tuto úlohu tam, kde je k dispozici malý počet anotovaných dat. Používáme příznaky jak na úrovni slov, tak i vět a naše modely hlubokých neuronových sítí (včetně architektury Transformer) dosáhly výborných výsledků v oblasti vícejazyčného rozpoznávání dialogových aktů. Pro rozpoznávání DA z obrazových dokumentů navrhujeme nový multimodální model založený na konvoluční a rekurentní neuronové síti. Tento model kombinuje textové a obrazové vstupy. Textová část zpracovává text z OCR, zatímco vizuální část extrahuje obrazové příznaky, které tvoří další vstup do modelu. Text z OCR obsahuje často překlepy nebo jiné lexikální chyby. Demonstrujeme na experimentech, že tento multimodální model využívající dva vstupy dokáže částečně vyvážit ztrátu informace způsobenou chybovostí OCR systému.ObhájenoDialogue act (DA) recognition is an important step of dialogue management and understanding. This task is to automatically assign a label to an utterance (or its part) based on its function in a dialogue (e.g. statement, question, backchannel, etc.). Such utterance-level classification thus helps to model and identify the structure of spontaneous dialogues. Even though DA recognition is usually realized on audio data using an automatic speech recognition engine, the dialogues exist also in a form of images (e.g. comic books). This thesis deals with automatic dialogue act recognition from image documents. To the best of our knowledge, this is the first attempt to propose DA recognition approaches using the images as an input. For this task, it is necessary to extract the text from the images. Therefore, we employ algorithms from the field of computer vision and image processing such as image thresholding, text segmentation, and optical character recognition (OCR). The main contribution in this field is to design and implement a custom OCR model based on convolutional and recurrent neural networks. We also explore different strategies for training such a~model, including synthetic data generation and data augmentation techniques. We achieve new state-of-the-art OCR results in the constraints when only a few training data are available. Summing up, our contribution is hence also presenting an overview of how to create an efficient OCR system with minimal costs. We further deal with the multilinguality in the DA recognition field. We successfully employ one general model that was trained by data from all available languages, as well as several models that are trained on a single language, and cross-linguality is achieved by using semantic space transformations. Moreover, we explore transfer learning for DA recognition where there is a small number of annotated data available. We use word-level and utterance-level features and our models contain deep neural network architectures, including Transformers. We obtain new state-of-the-art results in multi- and cross-lingual DA regonition field. For DA recognition from image documents, we propose and implement a novel multimodal model based on convolutional and recurrent neural network. This model combines text and image inputs. A text part is fed by text tokens from OCR, while the visual part extracts image features that are considered as an auxiliary input. Extracted text from dialogues is often erroneous and contains typos or other lexical errors. We show that the multimodal model deals with the erroneous text and visual information partially balance this loss of information

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

A survey on knowledge-enhanced multimodal learning

Multimodal learning has been a field of increasing interest, aiming to combine various modalities in a single joint representation. Especially in the area of visiolinguistic (VL) learning multiple models and techniques have been developed, targeting a variety of tasks that involve images and text. VL models have reached unprecedented performances by extending the idea of Transformers, so that both modalities can learn from each other. Massive pre-training procedures enable VL models to acquire a certain level of real-world understanding, although many gaps can be identified: the limited comprehension of commonsense, factual, temporal and other everyday knowledge aspects questions the extendability of VL tasks. Knowledge graphs and other knowledge sources can fill those gaps by explicitly providing missing information, unlocking novel capabilities of VL models. In the same time, knowledge graphs enhance explainability, fairness and validity of decision making, issues of outermost importance for such complex implementations. The current survey aims to unify the fields of VL representation learning and knowledge graphs, and provides a taxonomy and analysis of knowledge-enhanced VL models

Automatic Image Captioning with Style

This thesis connects two core topics in machine learning, vision and language. The problem of choice is image caption generation: automatically constructing natural language descriptions of image content. Previous research into image caption generation has focused on generating purely descriptive captions; I focus on generating visually relevant captions with a distinct linguistic style. Captions with style have the potential to ease communication and add a new layer of personalisation. First, I consider naming variations in image captions, and propose a method for predicting context-dependent names that takes into account visual and linguistic information. This method makes use of a large-scale image caption dataset, which I also use to explore naming conventions and report naming conventions for hundreds of animal classes. Next I propose the SentiCap model, which relies on recent advances in artificial neural networks to generate visually relevant image captions with positive or negative sentiment. To balance descriptiveness and sentiment, the SentiCap model dynamically switches between two recurrent neural networks, one tuned for descriptive words and one for sentiment words. As the first published model for generating captions with sentiment, SentiCap has influenced a number of subsequent works. I then investigate the sub-task of modelling styled sentences without images. The specific task chosen is sentence simplification: rewriting news article sentences to make them easier to understand. For this task I design a neural sequence-to-sequence model that can work with limited training data, using novel adaptations for word copying and sharing word embeddings. Finally, I present SemStyle, a system for generating visually relevant image captions in the style of an arbitrary text corpus. A shared term space allows a neural network for vision and content planning to communicate with a network for styled language generation. SemStyle achieves competitive results in human and automatic evaluations of descriptiveness and style. As a whole, this thesis presents two complete systems for styled caption generation that are first of their kind and demonstrate, for the first time, that automatic style transfer for image captions is achievable. Contributions also include novel ideas for object naming and sentence simplification. This thesis opens up inquiries into highly personalised image captions; large scale visually grounded concept naming; and more generally, styled text generation with content control