Cross-Lingual and Low-Resource Sentiment Analysis

Identifying sentiment in a low-resource language is essential for understanding opinions internationally and for responding to the urgent needs of locals affected by disaster incidents in different world regions. While tools and resources for recognizing sentiment in high-resource languages are plentiful, determining the most effective methods for achieving this task in a low-resource language which lacks annotated data is still an open research question. Most existing approaches for cross-lingual sentiment analysis to date have relied on high-resource machine translation systems, large amounts of parallel data, or resources only available for Indo-European languages. This work presents methods, resources, and strategies for identifying sentiment cross-lingually in a low-resource language. We introduce a cross-lingual sentiment model which can be trained on a high-resource language and applied directly to a low-resource language. The model offers the feature of lexicalizing the training data using a bilingual dictionary, but can perform well without any translation into the target language. Through an extensive experimental analysis, evaluated on 17 target languages, we show that the model performs well with bilingual word vectors pre-trained on an appropriate translation corpus. We compare in-genre and in-domain parallel corpora, out-of-domain parallel corpora, in-domain comparable corpora, and monolingual corpora, and show that a relatively small, in-domain parallel corpus works best as a transfer medium if it is available. We describe the conditions under which other resources and embedding generation methods are successful, and these include our strategies for leveraging in-domain comparable corpora for cross-lingual sentiment analysis. To enhance the ability of the cross-lingual model to identify sentiment in the target language, we present new feature representations for sentiment analysis that are incorporated in the cross-lingual model: bilingual sentiment embeddings that are used to create bilingual sentiment scores, and a method for updating the sentiment embeddings during training by lexicalization of the target language. This feature configuration works best for the largest number of target languages in both untargeted and targeted cross-lingual sentiment experiments. The cross-lingual model is studied further by evaluating the role of the source language, which has traditionally been assumed to be English. We build cross-lingual models using 15 source languages, including two non-European and non-Indo-European source languages: Arabic and Chinese. We show that language families play an important role in the performance of the model, as does the morphological complexity of the source language. In the last part of the work, we focus on sentiment analysis towards targets. We study Arabic as a representative morphologically complex language and develop models and morphological representation features for identifying entity targets and sentiment expressed towards them in Arabic open-domain text. Finally, we adapt our cross-lingual sentiment models for the detection of sentiment towards targets. Through cross-lingual experiments on Arabic and English, we demonstrate that our findings regarding resources, features, and language also hold true for the transfer of targeted sentiment

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Portuguese-Chinese neural machine translation

Tese de mestrado, Engenharia Informática (Interação e Conhecimento) Universidade de Lisboa, Faculdade de Ciências, 2019Esta dissertação apresenta um estudo sobre Tradução Automática Neuronal (Neural Machine Translation) para o par de línguas Português (PT) ↔ Chinês (ZH) culminando na criação de um sistema de tradução automática com desempenho ao nível do estado da arte, que tira partido apenas de recursos e ferramentas livremente disponíveis. Este par de línguas foi escolhido devido ao seu impacto a nível global. O Português é a sexta língua mais falada no mundo, com presença em todos os continentes (sendo em particular a língua mais falada no hemisfério sul) e a língua Chinesa, que tem como país de origem a China, é a língua mais falada em todo o mundo. Como super potência emergente, a China tem cada vez mais ligações aos países ocidentais e, como tal, a necessidade de instrumentos de comunicação adequados que possam atravessar as barreiras linguísticas é cada vez mais premente. A tradução automática surge assim como um apoio para o acesso rápido a grandes quantidades de informação. Portugal e a língua portuguesa têm várias ligações à China. Uma destas ligações é Macau, uma região administrativa especial da República Popular da China onde o Português e o Chinês são ambas línguas oficiais e, assim sendo, onde o interesse num sistema que traduza entre as duas é muito grande. Porém, o problema da Tradução Automática entre estas duas línguas ainda não tem sido alvo de suficiente atenção pela comunidade científica. Neste trabalho ambas as direções de tradução são consideradas, isto é, são criados sistemas de tradução para a direção de tradução Português → Chinês e para a direção Chinês → Português. A dificuldade na criação de tais sistemas passa pela aquisição de corpora de qualidade e em quantidade suficiente nas duas línguas, o que para o par de línguas escolhido é um grande desafio; e passa também pela escolha da arquitetura que melhor se adapta a esse corpora. Para a criação destes sistemas de tradução, exploro três abordagens, que são referidas neste documento como: (i) abordagem direta (direct approach), que faz uso apenas de corpora paralelo entre Português e Chinês; (ii) abordagem pivô (pivot approach), que usa uma terceira língua como intermediário para a tradução; e (iii) abordagem muitos-para muitos (many-to-many approach), que tira partido de toda a informação usada nas outras duas abordagens. As várias abordagens são implementadas com recurso a redes neuronais, mais propriamente à arquitetura Transformer (Vaswani et al., 2017), e obtêm desempenho assinalável, com uma das abordagens a alcançar resultados superiores aos do Google Tradutor para o par de línguas escolhido em ambas as direções. Para efeitos de teste e comparação entre as várias abordagens e as traduções do Google Tradutor, o mesmo corpus de teste é usado para avaliar todos os sistemas. Esse corpus de teste é constituído pelas primeiras 1000 frases do News Commentary v11 corpus (Tiedemann, 2012), sendo composto por textos jornalísticos bem curados e com grande qualidade gramatical. A abordagem direta é a solução mais comum usada para a criação de um sistema de tradução automática. No caso deste estudo, um corpus paralelo entre Português e Chinês é usado para a criação de dois modelos, um para cada direção de tradução, isto é um para PT → ZH e outro para ZH → PT. Apesar das dificuldades em encontrar corpora paralelo entre Português e Chinês, foi possível encontrar um corpus com cerca de 1 milhão de frases, o qual é usado para o treino desta abordagem. O artigo que apresenta este corpus (Chao et al., 2018) foi publicado poucos meses antes do início desta dissertação e tanto quanto sei não existem outros trabalhos que usem este corpus além de (Chao et al., 2018). Usando a métrica BLEU (Papineni et al., 2002), a abordagem direta consegue um melhor desempenho que a base dada pelo Google Tradutor para a direção ZH → PT, não conseguindo, contudo, ultrapassar esta base para a direção de tradução PT → ZH. A falta de qualidade e quantidade de corpora paralelos entre Português e Chinês motiva a experimentação com uma abordagem pivô. Numa abordagem pivô, o sistema faz uso de uma língua intermediária escolhida de forma a que haja grande quantidade e qualidade de corpora paralelos entre esta e as outras duas línguas. O sistema começa por traduzir de Português ou Chinês para a língua pivô e de seguida traduz da língua pivô para Chinês ou Português. A ideia por detrás desta abordagem é que as redes neuronais tendem a ter melhor performance quanto maior for o número de exemplos usados para treino da rede, e que esta melhoria será capaz de compensar a degradação da tradução introduzida pela passagem por uma língua intermédia. Usando a métrica BLEU, esta abordagem obtém resultados superiores à base e à abordagem direta em ambas as direções de tradução. Finalmente, a abordagem muitos-para-muitos segue as propostas de Johnson et al. (2017), Lakew et al. (2017) e Aharoni et al. (2019), que permitem o uso dos vários corpora paralelos usados para treino das outras duas abordagens. Usando a métrica BLEU, os resultados deste sistema ficam entre os da abordagem direta e os da abordagem pivô, não conseguindo ultrapassar a base para a direção de tradução PT → ZH. De entre os vários sistemas criados, a abordagem com melhores resultados é a abordagem pivô, que por sua vez foi a única abordagem que não viu qualquer tipo de dados paralelos entre as línguas Portuguesa e Chinesa. Porém, a abordagem muitos-para-muitos é a que demonstra maior potencial de desenvolvimento pois tem a capacidade de facilmente incorporar mais dados e assim melhorar a qualidade de tradução. O trabalho final, para além de uma panorâmica sobre o estado da arte da tradução automática, fornece uma solução prática com boa qualidade para a tradução entre Português e Chinês usando apenas recursos e ferramentas livremente disponíveis. Foi também criado um serviço online de tradução entre Português e Chinês disponível gratuitamente em https://portulanclarin.net/workbench/lx/translator/, resultante do trabalho descrito neste documento. Cabe notar que parte do trabalho apresentado nesta dissertação já foi alvo de revisão por pares (peer review) e aceite para publicação (Santos et al., to appear).This dissertation reports on a study addressing Neural Machine Translation for the language pair Portuguese ↔ Chinese and also on the development of a state of the art Machine Translation system for this pair using only freely available resources. The choice of this particular language pair was due to the fact that China is regarded as an emerging super power whose ties are steadily increasing with western countries, and as such the need for appropriate communication tools that can cross linguistic barriers is becoming a more pressing issue. The use of Machine Translation supports fast access to big quantities of data in another language. Portugal and its language have several ties with China. With Macau being a special administrative region of the People’s Republic of China where the two languages are official languages, a Machine Translation system for this pair is of high importance. In this work, both translation directions are considered. That is, there are systems for the translation direction Chinese → Portuguese, and systems for the direction Portuguese → Chinese. The key issue underlying the creation of such systems is twofold: (i) the gathering of corpora with good enough quality and quantity, which for this pair is a challenge; and (ii) the choice of a suitable architecture to accommodate such corpora. Three approaches are followed to address the problem, with all the implemented systems making use of neural networks, namely the Transformer architecture, and with the performance of one approach surpassing that of the baseline Google Translate for the chosen language pairs in both translation directions. An online translation service was also developed, showcasing one of the three approaches studied in this document for the two translation directions, and is freely available at https://portulanclarin.net/workbench/lx/translator/. Note that part of the work presented in this dissertation already passed peer review, and was accepted for publication (Santos et al., to appear)

Computational Etymology: Word Formation and Origins

While there are over seven thousand languages in the world, substantial language technologies exist only for a small percentage of these. The large majority of world languages do not have enough bilingual or even monolingual data for developing technologies like machine translation using current approaches. The computational study and modeling of word origins and word formation is a key step in developing comprehensive translation dictionaries for low-resource languages. This dissertation presents novel foundational work in computational etymology, a promising field which this work is pioneering. The dissertation also includes novel models of core vocabulary, dictionary information distillation, and of the diverse linguistic processes of word formation and concept realization between languages, including compounding, derivation, sense-extension, borrowing, and historical cognate relationships, utilizing statistical and neural models trained on the unprecedented scale of thousands of languages. Collectively these are important components in tackling the grand challenges of universal translation, endangered language documentation and revitalization, and supporting technologies for speakers of thousands of underserved languages

Applied Deep Learning: Case Studies in Computer Vision and Natural Language Processing

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover classification. In addition, a super-resolution deep model was implemented to further enhance remote sensing images. In the third study, a deep learning-based framework was proposed for fact-checking on social media to spot fake scientific news. The framework leveraged deep learning, information retrieval, and natural language processing techniques to retrieve pertinent scholarly papers for given scientific news and evaluate the credibility of the news

Supporting Human Cognitive Writing Processes: Towards a Taxonomy of Writing Support Systems

In the field of natural language processing (NLP), advances in transformer architectures and large-scale language models have led to a plethora of designs and research on a new class of information systems (IS) called writing support systems, which help users plan, write, and revise their texts. Despite the growing interest in writing support systems in research, there needs to be more common knowledge about the different design elements of writing support systems. Our goal is, therefore, to develop a taxonomy to classify writing support systems into three main categories (technology, task/structure, and user). We evaluated and refined our taxonomy with seven interviewees with domain expertise, identified three clusters in the reviewed literature, and derived five archetypes of writing support system applications based on our categorization. Finally, we formulate a new research agenda to guide researchers in the development and evaluation of writing support systems

Emotion-aware voice interfaces based on speech signal processing

Voice interfaces (VIs) will become increasingly widespread in current daily lives as AI techniques progress. VIs can be incorporated into smart devices like smartphones, as well as integrated into autos, home automation systems, computer operating systems, and home appliances, among other things. Current speech interfaces, however, are unaware of users’ emotional states and hence cannot support real communication. To overcome these limitations, it is necessary to implement emotional awareness in future VIs. This thesis focuses on how speech signal processing (SSP) and speech emotion recognition (SER) can enable VIs to gain emotional awareness. Following an explanation of what emotion is and how neural networks are implemented, this thesis presents the results of several user studies and surveys. Emotions are complicated, and they are typically characterized using category and dimensional models. They can be expressed verbally or nonverbally. Although existing voice interfaces are unaware of users’ emotional states and cannot support natural conversations, it is possible to perceive users’ emotions by speech based on SSP in future VIs. One section of this thesis, based on SSP, investigates mental restorative effects on humans and their measures from speech signals. SSP is less intrusive and more accessible than traditional measures such as attention scales or response tests, and it can provide a reliable assessment for attention and mental restoration. SSP can be implemented into future VIs and utilized in future HCI user research. The thesis then moves on to present a novel attention neural network based on sparse correlation features. The detection accuracy of emotions in the continuous speech was demonstrated in a user study utilizing recordings from a real classroom. In this section, a promising result will be shown. In SER research, it is unknown if existing emotion detection methods detect acted emotions or the genuine emotion of the speaker. Another section of this thesis is concerned with humans’ ability to act on their emotions. In a user study, participants were instructed to imitate five fundamental emotions. The results revealed that they struggled with this task; nevertheless, certain emotions were easier to replicate than others. A further study concern is how VIs should respond to users’ emotions if SER techniques are implemented in VIs and can recognize users’ emotions. The thesis includes research on ways for dealing with the emotions of users. In a user study, users were instructed to make sad, angry, and terrified VI avatars happy and were asked if they would like to be treated the same way if the situation were reversed. According to the results, the majority of participants tended to respond to these unpleasant emotions with neutral emotion, but there is a difference among genders in emotion selection. For a human-centered design approach, it is important to understand what the users’ preferences for future VIs are. In three distinct cultures, a questionnaire-based survey on users’ attitudes and preferences for emotion-aware VIs was conducted. It was discovered that there are almost no gender differences. Cluster analysis found that there are three fundamental user types that exist in all cultures: Enthusiasts, Pragmatists, and Sceptics. As a result, future VI development should consider diverse sorts of consumers. In conclusion, future VIs systems should be designed for various sorts of users as well as be able to detect the users’ disguised or actual emotions using SER and SSP technologies. Furthermore, many other applications, such as restorative effects assessments, can be included in the VIs system

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields