[pt] No contexto de comércio eletrônico, descrições de produtos exercem grande influência na experiência de compra. Descrições bem feitas devem idealmente informar um potencial consumidor sobre detalhes relevantes do produto, esclarecendo potenciais dúvidas e facilitando a compra. Gerar boas descrições, entretanto, é uma atividade custosa, que tradicionalmente exige esforço humano. Ao mesmo tempo, existe uma grande quantidade de produtos sendo lançados a cada dia. Nesse contexto, este trabalho apresenta uma nova metodologia para a geração automatizada de descrições de produtos, usando as avaliações deixadas por usuários como fonte de informações. O método proposto é composto por três etapas: (i) a extração de sentenças adequadas para uma descrição a partir das avaliações (ii) a seleção de sentenças dentre as candidatas (iii) a geração da descrição de produto a partir das sentenças selecionadas usando um Large Language Model (LLM) de forma zero-shot. Avaliamos a qualidade das descrições geradas pelo nosso método comparando-as com descrições de produto reais postadas pelos próprios anunciantes. Nessa avaliação, contamos com a colaboração de 30 avaliadores, e verificamos que nossas descrições são preferidas mais vezes do que as descrições originais, sendo consideradas mais informativas, legíveis e relevantes. Além disso, nessa mesma avaliação replicamos um método da literatura recente e executamos um teste estatístico comparando seus resultados com o nosso método, e dessa comparação verificamos que nosso método gera descrições mais informativas e preferidas no geral. / [en] In the context of e-commerce, product descriptions have a great influence on the shopping experience. Well-made descriptions should ideally inform a potential consumer about relevant product details, clarifying potential doubt sand facilitating the purchase. Generating good descriptions, however, is a costly activity, which traditionally requires human effort. At the same time, there are a large number of products being launched every day. In this context, this work presents a new methodology for the automated generation of product descriptions, using reviews left by users as a source of information. The proposed method consists of three steps: (i) the extraction of suitable sentences for a description from the reviews (ii) the selection of sentences among the candidates (iii) the generation of the product description from the selected sentences using a Large Language Model (LLM) in a zero-shot way. We evaluate the quality of descriptions generated by our method by comparing them to real product descriptions posted by sellers themselves. In this evaluation, we had the collaboration of 30 evaluators, and we verified that our descriptions are preferred more often than the original descriptions, being considered more informative, readable and relevant. Furthermore, in this same evaluation we replicated a method from recent literature and performed a statistical test comparing its results with our method, and from this comparison we verified that our method generates more informative and preferred descriptions overall.

L'analogie, c'est-à-dire une correspondance entre deux entités, est considérée une capacité de raisonnement importante. L'analogie proportionnelle, écrite $a:b::c:d$ et qui se lit ``$a$ est à $b$ ce que $c$ est à $d$'', en est un cas particulier où la correspondance tient de par la relation entre les éléments de deux paires d'objets. Le mémoire évalue certaines méthodes issues de l'usage de représentations distributionnelles vectorielles dans la résolution d'analogies proportionnelles verbales et les mène à leur prolongement naturel, la phrase. Nous ciblons la compétence de modèles de langue et des représentations qui peuvent en être extraites à la résolution d'analogies proportionnelles formées sur la base de relations syntaxiques, sémantiques, ou de connaissance encyclopédique. Peu d'ensembles de données existent pour les analogies de phrase et sinon comprennent pour la plupart des analogies au niveau de la forme, composées de phrases construites à partir de gabarits, ou bien variant peu dans les relations sémantiques qui tiennent entre les phrases. Nous construisons donc un ensemble de données contenant des phrases en paires relationnelles qui nous permet de construire des analogies en appariant deux paires. Nous essayons différentes variations de méthodes qui comportent un objectif de recouvrement par un modèle vectoriel. D'autres méthodes de résolution d'analogies proportionnelles sont explorées par voie de génération de texte. Nous expérimentons par le peaufinement du modèle de langue Flan-T5, pré-entraîné sur des paires instruction-réponse, sur nos analogies par une tâche séquence à séquence, ainsi que par l'incitation avec peu d'exemples en utilisant des versions de ce modèle en variant la capacité jusque dans la gamme des milliards de paramètres. En somme, la performance observée est faible pour toutes les tâches. Nous concluons, de l'utilisation de plongements de phrase, quelques mises en garde similaires à celles que l'on trouve avec la résolution d'analogies verbales par plongements lexicaux. Nos expérimentations génératives démontrent l'importance de données à la fois de bonne qualité et de bonne quantité, ainsi que le potentiel de l'apprentissage en contexte. Nous ajoutons à cela un aperçu qualitatif de la disparité entre l'habileté de modèles probabilistes entraînés pour prédire, à partir d'une instruction, la séquence correcte, et celle d'un modèle peaufiné par la méthode d'apprentissage par renforcement avec commentaires humains, à savoir ChatGPT. / Analogy, the correspondence between two things, has been hailed as an important reasoning capability. Proportional analogy, denoted $a:b::c:d$, read ``$a$ is to $b$ as $c$ is to $d$'' is a special case of this where a correspondence is made in the relation that holds between the elements of two pairs. This thesis evaluates methods originating in the recent use of distributional vector representations for solving four-part word analogies, bringing them to their natural extension, sentences. Few datasets of proportional sentence analogies exist, typically comprising purely formal analogies or sentences constructed by templates, and where semantic relations are typically limited in the variety we would hope to capture. Thus, for the purposes of our experiments, we curate a dataset of pairs of sentences for which a given relation holds and from which analogies can be constructed by matching pairs within a relation together. We target the analogy-solving ability of language models and representations derived therefrom, specifically as regards proportional sentence analogies formed on the basis of syntax, semantics, or encyclopedic knowledge. Different variations on previous methods are explored, all based on retrieval of the solution in a vector space model. Other methods of solving proportional sentence analogies by generation are attempted. We experiment with finetuning the instruction-trained Flan-T5 language model on sentence analogies as a sequence-to-sequence task, as well as prompting model checkpoints up into the billion-parameter range with few-shot examples. Overall performance at the task is poor in both settings. We find that similar caveats which apply to analogical reasoning with word vectors apply to sentence embeddings as well. Our generative experiments show the importance of data of suitable quality and quantity, as well the potential of in-context learning. Some qualitative insights are shown as to the disparity in task ability of instruction-trained probabilistic language models and one finetuned by reinforcement learning with human feedback, namely ChatGPT.

Целью данной магистерской диссертации является разработка и оценка модели глубокого обучения для автоматизированного повествования на основе последовательностей изображений. В работе рассматриваются современные методы компьютерного зрения и алгоритмы машинного обучения, которые позволяют генерировать текстовые описания на основе визуальных данных. Проведен анализ существующих подходов, разработана и настроена модель для генерации текстов, реализована и протестирована ее работа на реальных данных. Полученные результаты сравниваются с существующими решениями, что позволяет сделать выводы о преимуществе предложенной модели. / The aim of this master's thesis is to develop and evaluate deep learning model for automated storytelling based on image sequences. The thesis explores modern computer vision methods and machine learning algorithms that enable the generation of textual descriptions from visual data. An analysis of existing approaches was conducted, a model for text generation was developed and configured, and its performance was implemented and tested on real data. The obtained results are compared with existing solutions, allowing conclusions to be drawn about the advantages of the proposed model.

This diploma thesis explores subjectivity in the domain of so-called digital writing, that is, in texts of largely experimental nature generated by computer algorithms (or with their assistance). In order to do so, the thesis briefly covers the history of digital writing, its mediatic specificities, poetics as well as various theoretical and philosophical conceptualizations. Most importantly, it undertakes an analysis of a post-lyrical subject, a concept devised by Janez Strehovec, that is common to all cases of generative writing under focus. For its comparative analysis, the thesis deals with the recent works from contemporary creators who approach algorithmic textuality from variegated perspectives, incl. Nick Montfort, Allison Parish, Stephanie Strickland, Li Zilles, and Jörg Piringer. Texts generated by programs are conceived of as expressing a new, parasitic and prosthetic, genus of cyber-textual subjectivity that defies the traditional lyric and expands its pool "by other means," as Marjorie Perloff would say. Such a tendency results in conceptually as well as formally complex literary corpus "infected" by - to further exploit the suggested metaphor - parasitic voices and prosthetic selves. Unlike in generic lyric, the post- lyrical subject surpasses the confines of poetry as genre; it is...

Diagnostic Captioning is described as “the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination” [59] and it can assist inexperienced doctors and radiologists to reduce clinical errors or help experienced professionals increase their productivity. In this context, tools that would help medical doctors produce higher quality reports in less time could be of high interest for medical imaging departments, as well as significantly impact deep learning research within the biomedical domain, which makes it particularly interesting for people involved in industry and researchers all along. In this work, we attempted to develop Diagnostic Captioning systems, based on novel Deep Learning approaches, to investigate to what extent Neural Networks are capable of performing medical image tagging, as well as automatically generating a diagnostic text from a set of medical images. Towards this objective, the first step is concept detection, which boils down to predicting the relevant tags for X-RAY images, whereas the ultimate goal is caption generation. To this end, we further participated in ImageCLEFmedical 2022 evaluation campaign, addressing both the concept detection and the caption prediction tasks by developing baselines based on Deep Neural Networks; including image encoders, classifiers and text generators; in order to get a quantitative measure of my proposed architectures’ performance [28]. My contribution to the evaluation campaign, as part of this work and on behalf of NeuralDynamicsLab¹ group at KTH Royal Institute of Technology, within the school of Electrical Engineering and Computer Science, ranked 4th in the former and 5th in the latter task [55, 68] among 12 groups included within the top-10 best performing submissions in both tasks. / Diagnostisk textning avser automatisk generering från en diagnostisk text från en uppsättning medicinska bilder av en patient som samlats in under en undersökning och den kan hjälpa oerfarna läkare och radiologer, minska kliniska fel eller hjälpa erfarna yrkesmän att producera diagnostiska rapporter snabbare [59]. Därför kan verktyg som skulle hjälpa läkare och radiologer att producera rapporter av högre kvalitet på kortare tid vara av stort intresse för medicinska bildbehandlingsavdelningar, såväl som leda till inverkan på forskning om djupinlärning, vilket gör den domänen särskilt intressant för personer som är involverade i den biomedicinska industrin och djupinlärningsforskare. I detta arbete var mitt huvudmål att utveckla system för diagnostisk textning, med hjälp av nya tillvägagångssätt som används inom djupinlärning, för att undersöka i vilken utsträckning automatisk generering av en diagnostisk text från en uppsättning medi-cinska bilder är möjlig. Mot detta mål är det första steget konceptdetektering som går ut på att förutsäga relevanta taggar för röntgenbilder, medan slutmålet är bildtextgenerering. Jag deltog i ImageCLEF Medical 2022-utvärderingskampanjen, där jag deltog med att ta itu med både konceptdetektering och bildtextförutsägelse för att få ett kvantitativt mått på prestandan för mina föreslagna arkitekturer [28]. Mitt bidrag, där jag representerade forskargruppen NeuralDynamicsLab² , där jag arbetade som ledande forskningsingenjör, placerade sig på 4:e plats i den förra och 5:e i den senare uppgiften [55, 68] bland 12 grupper som ingår bland de 10 bästa bidragen i båda uppgifterna.