Abstract Syntax Networks for Code Generation and Semantic Parsing

Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree. On the benchmark Hearthstone dataset for code generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy, compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with no task-specific engineering.Comment: ACL 2017. MR and MS contributed equall

Dissecting Deep Language Models: The Explainability and Bias Perspective

L'abstract è presente nell'allegato / the abstract is in the attachmen

Natural Language Processing using Deep Learning in Social Media

[ES] En los últimos años, los modelos de aprendizaje automático profundo (AP) han revolucionado los sistemas de procesamiento de lenguaje natural (PLN). Hemos sido testigos de un avance formidable en las capacidades de estos sistemas y actualmente podemos encontrar sistemas que integran modelos PLN de manera ubicua. Algunos ejemplos de estos modelos con los que interaccionamos a diario incluyen modelos que determinan la intención de la persona que escribió un texto, el sentimiento que pretende comunicar un tweet o nuestra ideología política a partir de lo que compartimos en redes sociales. En esta tesis se han propuestos distintos modelos de PNL que abordan tareas que estudian el texto que se comparte en redes sociales. En concreto, este trabajo se centra en dos tareas fundamentalmente: el análisis de sentimientos y el reconocimiento de la personalidad de la persona autora de un texto. La tarea de analizar el sentimiento expresado en un texto es uno de los problemas principales en el PNL y consiste en determinar la polaridad que un texto pretende comunicar. Se trata por lo tanto de una tarea estudiada en profundidad de la cual disponemos de una vasta cantidad de recursos y modelos. Por el contrario, el problema del reconocimiento de personalidad es una tarea revolucionaria que tiene como objetivo determinar la personalidad de los usuarios considerando su estilo de escritura. El estudio de esta tarea es más marginal por lo que disponemos de menos recursos para abordarla pero que no obstante presenta un gran potencial. A pesar de que el enfoque principal de este trabajo fue el desarrollo de modelos de aprendizaje profundo, también hemos propuesto modelos basados en recursos lingüísticos y modelos clásicos del aprendizaje automático. Estos últimos modelos nos han permitido explorar las sutilezas de distintos elementos lingüísticos como por ejemplo el impacto que tienen las emociones en la clasificación correcta del sentimiento expresado en un texto. Posteriormente, tras estos trabajos iniciales se desarrollaron modelos AP, en particular, Redes neuronales convolucionales (RNC) que fueron aplicadas a las tareas previamente citadas. En el caso del reconocimiento de la personalidad, se han comparado modelos clásicos del aprendizaje automático con modelos de aprendizaje profundo, pudiendo establecer una comparativa bajo las mismas premisas. Cabe destacar que el PNL ha evolucionado drásticamente en los últimos años gracias al desarrollo de campañas de evaluación pública, donde múltiples equipos de investigación comparan las capacidades de los modelos que proponen en las mismas condiciones. La mayoría de los modelos presentados en esta tesis fueron o bien evaluados mediante campañas de evaluación públicas, o bien emplearon la configuración de una campaña pública previamente celebrada. Siendo conscientes, por lo tanto, de la importancia de estas campañas para el avance del PNL, desarrollamos una campaña de evaluación pública cuyo objetivo era clasificar el tema tratado en un tweet, para lo cual recogimos y etiquetamos un nuevo conjunto de datos. A medida que avanzabamos en el desarrollo del trabajo de esta tesis, decidimos estudiar en profundidad como las RNC se aplicaban a las tareas de PNL. En este sentido, se exploraron dos líneas de trabajo. En primer lugar, propusimos un método de relleno semántico para RNC, que plantea una nueva manera de representar el texto para resolver tareas de PNL. Y en segundo lugar, se introdujo un marco teórico para abordar una de las críticas más frecuentes del aprendizaje profundo, el cual es la falta de interpretabilidad. Este marco busca visualizar qué patrones léxicos, si los hay, han sido aprendidos por la red para clasificar un texto.[CA] En els últims anys, els models d'aprenentatge automàtic profund (AP) han revolucionat els sistemes de processament de llenguatge natural (PLN). Hem estat testimonis d'un avanç formidable en les capacitats d'aquests sistemes i actualment podem trobar sistemes que integren models PLN de manera ubiqua. Alguns exemples d'aquests models amb els quals interaccionem diàriament inclouen models que determinen la intenció de la persona que va escriure un text, el sentiment que pretén comunicar un tweet o la nostra ideologia política a partir del que compartim en xarxes socials. En aquesta tesi s'han proposats diferents models de PNL que aborden tasques que estudien el text que es comparteix en xarxes socials. En concret, aquest treball se centra en dues tasques fonamentalment: l'anàlisi de sentiments i el reconeixement de la personalitat de la persona autora d'un text. La tasca d'analitzar el sentiment expressat en un text és un dels problemes principals en el PNL i consisteix a determinar la polaritat que un text pretén comunicar. Es tracta per tant d'una tasca estudiada en profunditat de la qual disposem d'una vasta quantitat de recursos i models. Per contra, el problema del reconeixement de la personalitat és una tasca revolucionària que té com a objectiu determinar la personalitat dels usuaris considerant el seu estil d'escriptura. L'estudi d'aquesta tasca és més marginal i en conseqüència disposem de menys recursos per abordar-la però no obstant i això presenta un gran potencial. Tot i que el fouc principal d'aquest treball va ser el desenvolupament de models d'aprenentatge profund, també hem proposat models basats en recursos lingüístics i models clàssics de l'aprenentatge automàtic. Aquests últims models ens han permès explorar les subtileses de diferents elements lingüístics com ara l'impacte que tenen les emocions en la classificació correcta del sentiment expressat en un text. Posteriorment, després d'aquests treballs inicials es van desenvolupar models AP, en particular, Xarxes neuronals convolucionals (XNC) que van ser aplicades a les tasques prèviament esmentades. En el cas de el reconeixement de la personalitat, s'han comparat models clàssics de l'aprenentatge automàtic amb models d'aprenentatge profund la qual cosa a permet establir una comparativa de les dos aproximacions sota les mateixes premisses. Cal remarcar que el PNL ha evolucionat dràsticament en els últims anys gràcies a el desenvolupament de campanyes d'avaluació pública on múltiples equips d'investigació comparen les capacitats dels models que proposen sota les mateixes condicions. La majoria dels models presentats en aquesta tesi van ser o bé avaluats mitjançant campanyes d'avaluació públiques, o bé s'ha emprat la configuració d'una campanya pública prèviament celebrada. Sent conscients, per tant, de la importància d'aquestes campanyes per a l'avanç del PNL, vam desenvolupar una campanya d'avaluació pública on l'objectiu era classificar el tema tractat en un tweet, per a la qual cosa vam recollir i etiquetar un nou conjunt de dades. A mesura que avançàvem en el desenvolupament del treball d'aquesta tesi, vam decidir estudiar en profunditat com les XNC s'apliquen a les tasques de PNL. En aquest sentit, es van explorar dues línies de treball.En primer lloc, vam proposar un mètode d'emplenament semàntic per RNC, que planteja una nova manera de representar el text per resoldre tasques de PNL. I en segon lloc, es va introduir un marc teòric per abordar una de les crítiques més freqüents de l'aprenentatge profund, el qual és la falta de interpretabilitat. Aquest marc cerca visualitzar quins patrons lèxics, si n'hi han, han estat apresos per la xarxa per classificar un text.[EN] In the last years, Deep Learning (DL) has revolutionised the potential of automatic systems that handle Natural Language Processing (NLP) tasks. We have witnessed a tremendous advance in the performance of these systems. Nowadays, we found embedded systems ubiquitously, determining the intent of the text we write, the sentiment of our tweets or our political views, for citing some examples. In this thesis, we proposed several NLP models for addressing tasks that deal with social media text. Concretely, this work is focused mainly on Sentiment Analysis and Personality Recognition tasks. Sentiment Analysis is one of the leading problems in NLP, consists of determining the polarity of a text, and it is a well-known task where the number of resources and models proposed is vast. In contrast, Personality Recognition is a breakthrough task that aims to determine the users' personality using their writing style, but it is more a niche task with fewer resources designed ad-hoc but with great potential. Despite the fact that the principal focus of this work was on the development of Deep Learning models, we have also proposed models based on linguistic resources and classical Machine Learning models. Moreover, in this more straightforward setup, we have explored the nuances of different language devices, such as the impact of emotions in the correct classification of the sentiment expressed in a text. Afterwards, DL models were developed, particularly Convolutional Neural Networks (CNNs), to address previously described tasks. In the case of Personality Recognition, we explored the two approaches, which allowed us to compare the models under the same circumstances. Noteworthy, NLP has evolved dramatically in the last years through the development of public evaluation campaigns, where multiple research teams compare the performance of their approaches under the same conditions. Most of the models here presented were either assessed in an evaluation task or either used their setup. Recognising the importance of this effort, we curated and developed an evaluation campaign for classifying political tweets. In addition, as we advanced in the development of this work, we decided to study in-depth CNNs applied to NLP tasks. Two lines of work were explored in this regard. Firstly, we proposed a semantic-based padding method for CNNs, which addresses how to represent text more appropriately for solving NLP tasks. Secondly, a theoretical framework was introduced for tackling one of the most frequent critics of Deep Learning: interpretability. This framework seeks to visualise what lexical patterns, if any, the CNN is learning in order to classify a sentence. In summary, the main achievements presented in this thesis are: - The organisation of an evaluation campaign for Topic Classification from texts gathered from social media. - The proposal of several Machine Learning models tackling the Sentiment Analysis task from social media. Besides, a study of the impact of linguistic devices such as figurative language in the task is presented. - The development of a model for inferring the personality of a developer provided the source code that they have written. - The study of Personality Recognition tasks from social media following two different approaches, models based on machine learning algorithms and handcrafted features, and models based on CNNs were proposed and compared both approaches. - The introduction of new semantic-based paddings for optimising how the text was represented in CNNs. - The definition of a theoretical framework to provide interpretable information to what CNNs were learning internally.Giménez Fayos, MT. (2021). Natural Language Processing using Deep Learning in Social Media [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172164TESI

Twitter Analysis to Predict the Satisfaction of Saudi Telecommunication Companies’ Customers

The flexibility in mobile communications allows customers to quickly switch from one service provider to another, making customer churn one of the most critical challenges for the data and voice telecommunication service industry. In 2019, the percentage of post-paid telecommunication customers in Saudi Arabia decreased; this represents a great deal of customer dissatisfaction and subsequent corporate fiscal losses. Many studies correlate customer satisfaction with customer churn. The Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. Current methods of analysing churn rates are inadequate and faced some issues, particularly in the Saudi market. This research was conducted to realize the relationship between customer satisfaction and customer churn and how to use social media mining to measure customer satisfaction and predict customer churn. This research conducted a systematic review to address the churn prediction models problems and their relation to Arabic Sentiment Analysis. The findings show that the current churn models lack integrating structural data frameworks with real-time analytics to target customers in real-time. In addition, the findings show that the specific issues in the existing churn prediction models in Saudi Arabia relate to the Arabic language itself, its complexity, and lack of resources. As a result, I have constructed the first gold standard corpus of Saudi tweets related to telecom companies, comprising 20,000 manually annotated tweets. It has been generated as a dialect sentiment lexicon extracted from a larger Twitter dataset collected by me to capture text characteristics in social media. I developed a new ASA prediction model for telecommunication that fills the detected gaps in the ASA literature and fits the telecommunication field. The proposed model proved its effectiveness for Arabic sentiment analysis and churn prediction. This is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies, which has not been attempted before. Different fields, such as education, have different features, making applying the proposed model is interesting because it based on text-mining

On Learning Meaningful Assert Statements for Unit Test Cases

Software testing is an essential part of the software lifecycle and requires a substantial amount of time and effort. It has been estimated that software developers spend close to 50% of their time on testing the code they write. For these reasons, a long standing goal within the research community is to (partially) automate software testing. While several techniques and tools have been proposed to automatically generate test methods, recent work has criticized the quality and usefulness of the assert statements they generate. Therefore, we employ a Neural Machine Translation (NMT) based approach called Atlas(AuTomatic Learning of Assert Statements) to automatically generate meaningful assert statements for test methods. Given a test method and a focal method (i.e.,the main method under test), Atlas can predict a meaningful assert statement to assess the correctness of the focal method. We applied Atlas to thousands of test methods from GitHub projects and it was able to predict the exact assert statement manually written by developers in 31% of the cases when only considering the top-1 predicted assert. When considering the top-5 predicted assert statements, Atlas is able to predict exact matches in 50% of the cases. These promising results hint to the potential usefulness ofour approach as (i) a complement to automatic test case generation techniques, and (ii) a code completion support for developers, whocan benefit from the recommended assert statements while writing test code

Transfomer Models: From Model Inspection to Applications in Patents

L'elaborazione del linguaggio naturale viene utilizzata per affrontare diversi compiti, sia di tipo linguistico, come ad esempio l'etichettatura della parte del discorso, il parsing delle dipendenze, sia più specifiche, come ad esempio la traduzione automatica e l'analisi del sentimento. Per affrontare questi compiti, nel tempo sono stati sviluppati approcci dedicati.Una metodologia che aumenta le prestazioni in tutti questi casi in modo unificato è la modellazione linguistica, che consiste nel preaddestrare un modello per sostituire i token mascherati in grandi quantità di testo, in modo casuale all'interno di pezzi di testo o in modo sequenziale uno dopo l'altro, per sviluppare rappresentazioni di uso generale che possono essere utilizzate per migliorare le prestazioni in molti compiti contemporaneamente.L'architettura di rete neurale che attualmente svolge al meglio questo compito è il transformer, inoltre, le dimensioni del modello e la quantità dei dati sono essenziali per lo sviluppo di rappresentazioni ricche di informazioni. La disponibilità di insiemi di dati su larga scala e l'uso di modelli con miliardi di parametri sono attualmente il percorso più efficace verso una migliore rappresentazione del testo.Tuttavia, i modelli di grandi dimensioni comportano una maggiore difficoltà nell'interpretazione dell'output che forniscono. Per questo motivo, sono stati condotti diversi studi per indagare le rappresentazioni fornite da modelli di transformers.In questa tesi indago questi modelli da diversi punti di vista, studiando le proprietà linguistiche delle rappresentazioni fornite da BERT, per capire se le informazioni che codifica sono localizzate all'interno di specifiche elementi della rappresentazione vettoriale. A tal fine, identifico pesi speciali che mostrano un'elevata rilevanza per diversi compiti di sondaggio linguistico. In seguito, analizzo la causa di questi particolari pesi e li collego alla distribuzione dei token e ai token speciali.Per completare questa analisi generale ed estenderla a casi d'uso più specifici, studio l'efficacia di questi modelli sui brevetti. Utilizzo modelli dedicati, per identificare entità specifiche del dominio, come le tecnologie o per segmentare il testo dei brevetti. Studio sempre l'analisi delle prestazioni integrandola con accurate misurazioni dei dati e delle proprietà del modello per capire se le conclusioni tratte per i modelli generici valgono anche in questo contesto.Natural Language Processing is used to address several tasks, linguistic related ones, e.g. part of speech tagging, dependency parsing, and downstream tasks, e.g. machine translation, sentiment analysis. To tackle these tasks, dedicated approaches have been developed over time.A methodology that increases performance on all tasks in a unified manner is language modeling, this is done by pre-training a model to replace masked tokens in large amounts of text, either randomly within chunks of text or sequentially one after the other, to develop general purpose representations that can be used to improve performance in many downstream tasks at once.The neural network architecture currently best performing this task is the transformer, moreover, model size and data scale are essential to the development of information-rich representations. The availability of large scale datasets and the use of models with billions of parameters is currently the most effective path towards better representations of text.However, with large models, comes the difficulty in interpreting the output they provide. Therefore, several studies have been carried out to investigate the representations provided by transformers models trained on large scale datasets.In this thesis I investigate these models from several perspectives, I study the linguistic properties of the representations provided by BERT, a language model mostly trained on the English Wikipedia, to understand if the information it codifies is localized within specific entries of the vector representation. Doing this I identify special weights that show high relevance to several distinct linguistic probing tasks. Subsequently, I investigate the cause of these special weights, and link them to token distribution and special tokens.To complement this general purpose analysis and extend it to more specific use cases, given the wide range of applications for language models, I study their effectiveness on technical documentation, specifically, patents. I use both general purpose and dedicated models, to identify domain-specific entities such as users of the inventions and technologies or to segment patents text. I always study performance analysis complementing it with careful measurements of data and model properties to understand if the conclusions drawn for general purpose models hold in this context as well

Comparing sentiment analysis tools on gitHub project discussions

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáThe context of this work is situated in the rapidly evolving sphere of Natural Language Processing (NLP) within the scope of software engineering, focusing on sentiment analysis in software repositories. Sentiment analysis, a subfield of NLP, provides a potent method to parse, understand, and categorize these sentiments expressed in text. By applying sentiment analysis to software repositories, we can decode developers’ opinions and sentiments, providing key insights into team dynamics, project health, and potential areas of conflict or collaboration. However, the application of sentiment analysis in software engineering comes with its unique set of challenges. Technical jargon, code-specific ambiguities, and the brevity of software-related communications demand tailored NLP tools for effective analysis. The study unfolds in two primary phases. In the initial phase, we embarked on a meticulous investigation into the impacts of expanding the training sets of two prominent sentiment analysis tools, namely, SentiCR and SentiSW. The objective was to delineate the correlation between the size of the training set and the resulting tool performance, thereby revealing any potential enhancements in performance. The subsequent phase of the research encapsulates a practical application of the enhanced tools. We employed these tools to categorize discussions drawn from issue tickets within a varied array of Open-Source projects. These projects span an extensive range, from relatively small repositories to large, well-established repositories, thus providing a rich and diverse sampling ground.O contexto deste trabalho situa-se na esfera em rápida evolução do Processamento de Linguagem Natural (PLN) no âmbito da engenharia de software, com foco na análise de sentimentos em repositórios de software. A análise de sentimentos, um subcampo do PLN, fornece um método poderoso para analisar, compreender e categorizar os sentimentos expressos em texto. Ao aplicar a análise de sentimentos aos repositórios de software, podemos decifrar as opiniões e sentimentos dos desenvolvedores, fornecendo informações importantes sobre a dinâmica da equipe, a saúde do projeto e áreas potenciais de conflito ou colaboração. No entanto, a aplicação da análise de sentimentos na engenharia de software apresenta desafios únicos. Jargão técnico, ambiguidades específicas do código e a breviedade das comunicações relacionadas ao software exigem ferramentas de PLN personalizadas para uma análise eficaz. O estudo se desenvolve em duas fases principais. Na fase inicial, embarcamos em uma investigação meticulosa sobre os impactos da expansão dos conjuntos de treinamento de duas ferramentas proeminentes de análise de sentimentos, nomeadamente, SentiCR e SentiSW. O objetivo foi delinear a correlação entre o tamanho do conjunto de treinamento e o desempenho da ferramenta resultante, revelando assim possíveis aprimoramentos no desempenho. A fase subsequente da pesquisa engloba uma aplicação prática das ferramentas aprimoradas. Utilizamos essas ferramentas para categorizar discussões retiradas de bilhetes de problemas em uma variedade diversificada de projetos de código aberto. Esses projetos abrangem uma ampla gama, desde repositórios relativamente pequenos até repositórios grandes e bem estabelecidos, fornecendo assim um campo de amostragem rico e diversificado

A systematic review of natural language processing applied to radiology reports

NLP has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses recent literature in NLP applied to radiology reports. Our automated literature search yields 4,799 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics. We present a comprehensive analysis of the 164 publications retrieved with each categorised into one of 6 clinical application categories. Deep learning use increases but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results. Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process but reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication

Deep Learning Software Repositories

Bridging the abstraction gap between artifacts and concepts is the essence of software engineering (SE) research problems. SE researchers regularly use machine learning to bridge this gap, but there are three fundamental issues with traditional applications of machine learning in SE research. Traditional applications are too reliant on labeled data. They are too reliant on human intuition, and they are not capable of learning expressive yet efficient internal representations. Ultimately, SE research needs approaches that can automatically learn representations of massive, heterogeneous, datasets in situ, apply the learned features to a particular task and possibly transfer knowledge from task to task. Improvements in both computational power and the amount of memory in modern computer architectures have enabled new approaches to canonical machine learning tasks. Specifically, these architectural advances have enabled machines that are capable of learning deep, compositional representations of massive data depots. The rise of deep learning has ushered in tremendous advances in several fields. Given the complexity of software repositories, we presume deep learning has the potential to usher in new analytical frameworks and methodologies for SE research and the practical applications it reaches. This dissertation examines and enables deep learning algorithms in different SE contexts. We demonstrate that deep learners significantly outperform state-of-the-practice software language models at code suggestion on a Java corpus. Further, these deep learners for code suggestion automatically learn how to represent lexical elements. We use these representations to transmute source code into structures for detecting similar code fragments at different levels of granularity—without declaring features for how the source code is to be represented. Then we use our learning-based framework for encoding fragments to intelligently select and adapt statements in a codebase for automated program repair. In our work on code suggestion, code clone detection, and automated program repair, everything for representing lexical elements and code fragments is mined from the source code repository. Indeed, our work aims to move SE research from the art of feature engineering to the science of automated discovery