abstract: Question Answering has been under active research for decades, but it has recently taken the spotlight following IBM Watson's success in Jeopardy! and digital assistants such as Apple's Siri, Google Now, and Microsoft Cortana through every smart-phone and browser. However, most of the research in Question Answering aims at factual questions rather than deep ones such as ``How'' and ``Why'' questions. In this dissertation, I suggest a different approach in tackling this problem. We believe that the answers of deep questions need to be formally defined before found. Because these answers must be defined based on something, it is better to be more structural in natural language text; I define Knowledge Description Graphs (KDGs), a graphical structure containing information about events, entities, and classes. We then propose formulations and algorithms to construct KDGs from a frame-based knowledge base, define the answers of various ``How'' and ``Why'' questions with respect to KDGs, and suggest how to obtain the answers from KDGs using Answer Set Programming. Moreover, I discuss how to derive missing information in constructing KDGs when the knowledge base is under-specified and how to answer many factual question types with respect to the knowledge base. After having the answers of various questions with respect to a knowledge base, I extend our research to use natural language text in specifying deep questions and knowledge base, generate natural language text from those specification. Toward these goals, I developed NL2KR, a system which helps in translating natural language to formal language. I show NL2KR's use in translating ``How'' and ``Why'' questions, and generating simple natural language sentences from natural language KDG specification. Finally, I discuss applications of the components I developed in Natural Language Understanding. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2015

abstract: Internet memes have become a widespread tool used by people for interacting and exchanging ideas over social media, blogs, and open messengers. Internet memes most commonly take the form of an image which is a combination of image, text, and humor, making them a powerful tool to deliver information. Image memes are used in viral marketing and mass advertising to propagate any ideas ranging from simple commercials to those that can cause changes and development in the social structures like countering hate speech. This work proposes to treat automatic image meme generation as a translation process, and further present an end to end neural and probabilistic approach to generate an image-based meme for any given sentence using an encoder-decoder architecture. For a given input sentence, a meme is generated by combining a meme template image and a text caption where the meme template image is selected from a set of popular candidates using a selection module and the meme caption is generated by an encoder-decoder model. An encoder is used to map the selected meme template and the input sentence into a meme embedding space and then a decoder is used to decode the meme caption from the meme embedding space. The generated natural language caption is conditioned on the input sentence and the selected meme template. The model learns the dependencies between the meme captions and the meme template images and generates new memes using the learned dependencies. The quality of the generated captions and the generated memes is evaluated through both automated metrics and human evaluation. An experiment is designed to score how well the generated memes can represent popular tweets from Twitter conversations. Experiments on Twitter data show the efficacy of the model in generating memes capable of representing a sentence in online social interaction. / Dissertation/Thesis / Masters Thesis Computer Science 2020

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22799号 / 情博第729号 / 新制||情||125(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 河原 達也, 教授 黒橋 禎夫, 教授 森 信介 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

<div><div>With the growth of the popularity of e-commerce and mobile apps during the past decade, people rely on online reviews more than ever before for purchasing products, booking hotels, and choosing all kinds of services. Users share their opinions by posting product reviews on merchant sites or online review websites (e.g., Yelp, Amazon, TripAdvisor). Although online reviews are valuable information for people who are interested in products and services, many reviews are manipulated by spammers to provide untruthful information for business competition. Since deceptive reviews can damage the reputation of brands and mislead customers’ buying behaviors, the identification of fake reviews has become an important topic for online merchants. Among the computational approaches proposed for fake review identification, network-based fake review analysis jointly considers the information from review text, reviewer behaviors, and production information. Researchers have proposed network-based methods (e.g., metapath) on heterogeneous networks, which have shown promising results.</div><div><br></div><div>However, we’ve identified two research gaps in this study: 1) We argue the previous network-based reviewer representations are not sufficient to preserve the relationship of reviewers in networks. To be specific, previous studies only considered first-order proximity, which indicates the observable connection between reviewers, but not second-order proximity, which captures the neighborhood structures where two vertices overlap. Moreover, although previous network-based fake review studies (e.g., metapath) connect reviewers through feature nodes across heterogeneous networks, they ignored the multi-view nature of reviewers. A view is derived from a single type of proximity or relationship between the nodes, which can be characterized by a set of edges. In other words, the reviewers could form different networks with regard to different relationships. 2) The text embeddings of reviews in previous network-based fake review studies were not considered with reviewer embeddings.</div><div><br></div><div>To tackle the first gap, we generated reviewer embeddings via MVE (Qu et al., 2017), a framework for multi-view network representation learning, and conducted spammer classification experiments to examine the effectiveness of the learned embeddings for distinguishing spammers and non-spammers. In addition, we performed unsupervised hierarchical clustering to observe the clusters of the reviewer embeddings. Our results show the clusters generated based on reviewer embeddings capture the difference between spammers and non-spammers better than those generated based on reviewers’ features.</div><div><br></div><div>To fill the second gap, we proposed hybrid embeddings that combine review text embeddings with reviewer embeddings (i.e., the vector that represents a reviewer’s characteristics, such as writing or behavioral patterns). We conducted fake review classification experiments to compare the performance between using hybrid embeddings (i.e., text+reviewer) as features and using text-only embeddings as features. Our results suggest that hybrid embedding is more effective than text-only embedding for fake review identification. Moreover, we compared the prediction performance of the hybrid embeddings with baselines and showed our approach outperformed others on fake review identification experiments.</div><div><br></div><div>The contributions of this study are four-fold: 1) We adopted a multi-view representation learning approach for reviewer embedding learning and analyze the efficacy of the embeddings used for spammer classification and fake review classification. 2) We proposed a hybrid embedding that considers the characteristics of both review text and the reviewer. Our results are promising and suggest hybrid embedding is very effective for fake review identification. 3) We proposed a heuristic network construction approach that builds a user network based on user features. 4) We evaluated how different spammer thresholds impact the performance of fake review classification. Several studies have used the same datasets as we used in this study, but most of them followed the spammer definition mentioned by Jindal and Liu (2008). We argued that the spammer definition should be configurable based on different datasets. Our findings showed that by carefully choosing the spammer thresholds for the target datasets, hybrid embeddings have higher efficacy for fake review classification.</div></div>

Étant donnée la masse toujours croissante de texte publié, la compréhension automatique des langues naturelles est à présent l'un des principaux enjeux de l'intelligence artificielle. En langue naturelle, les faits exprimés dans le texte ne sont pas nécessairement tous explicites : le lecteur humain infère les éléments manquants grâce à ses compétences linguistiques, ses connaissances de sens commun ou sur un domaine spécifique, et son expérience. Les systèmes de Traitement Automatique des Langues (TAL) ne possèdent naturellement pas ces capacités. Incapables de combler les défauts d'information du texte, ils ne peuvent donc pas le comprendre vraiment. Cette thèse porte sur ce problème et présente notre travail sur la résolution d'inférences pour la compréhension automatique de texte. Une inférence textuelle est définie comme une relation entre deux fragments de texte : un humain lisant le premier peut raisonnablement inférer que le second est vrai. Beaucoup de tâches de TAL évaluent plus ou moins directement la capacité des systèmes à reconnaître l'inférence textuelle. Au sein de cette multiplicité de l'évaluation, les inférences elles-mêmes présentent une grande variété de types. Nous nous interrogeons sur les inférences en TAL d'un point de vue théorique et présentons deux contributions répondant à ces niveaux de diversité : une tâche abstraite contextualisée qui englobe les tâches d'inférence du TAL, et une taxonomie hiérarchique des inférences textuelles en fonction de leur difficulté. La reconnaissance automatique d'inférence textuelle repose aujourd'hui presque toujours sur un modèle d'apprentissage, entraîné à l'usage de traits linguistiques variés sur un jeu d'inférences textuelles étiquetées. Cependant, les données spécifiques aux phénomènes d'inférence complexes ne sont pour le moment pas assez abondantes pour espérer apprendre automatiquement la connaissance du monde et le raisonnement de sens commun nécessaires. Les systèmes actuels se concentrent plutôt sur l'apprentissage d'alignements entre les mots de phrases reliées sémantiquement, souvent en utilisant leur structure syntaxique. Pour étendre leur connaissance du monde, ils incluent des connaissances tirées de ressources externes, ce qui améliore souvent les performances. Mais cette connaissance est souvent ajoutée par dessus les fonctionnalités existantes, et rarement bien intégrée à la structure de la phrase.Nos principales contributions dans cette thèse répondent au problème précédent. En partant de l'hypothèse qu'un lexique plus simple devrait rendre plus facile la comparaison du sens de deux phrases, nous décrivons une méthode de récupération de passage fondée sur une expansion lexicale structurée et un dictionnaire de simplifications. Cette hypothèse est testée à nouveau dans une de nos contributions sur la reconnaissance d'implication textuelle : des paraphrases syntaxiques sont extraites du dictionnaire et appliquées récursivement sur la première phrase pour la transformer en la seconde. Nous présentons ensuite une méthode d'apprentissage par noyaux de réécriture de phrases, avec une notion de types permettant d'encoder des connaissances lexico-sémantiques. Cette approche est efficace sur trois tâches : la reconnaissance de paraphrases, d'implication textuelle, et le question-réponses. Nous résolvons son problème de passage à l'échelle dans une dernière contribution. Des tests de compréhension sont utilisés pour son évaluation, sous la forme de questions à choix multiples sur des textes courts, qui permettent de tester la résolution d'inférences en contexte. Notre système est fondé sur un algorithme efficace d'édition d'arbres, et les traits extraits des séquences d'édition sont utilisés pour construire deux classifieurs pour la validation et l'invalidation des choix de réponses. Cette approche a obtenu la deuxième place du challenge "Entrance Exams" à CLEF 2015. / With the ever-growing mass of published text, natural language understanding stands as one of the most sought-after goal of artificial intelligence. In natural language, not every fact expressed in the text is necessarily explicit: human readers naturally infer what is missing through various intuitive linguistic skills, common sense or domain-specific knowledge, and life experiences. Natural Language Processing (NLP) systems do not have these initial capabilities. Unable to draw inferences to fill the gaps in the text, they cannot truly understand it. This dissertation focuses on this problem and presents our work on the automatic resolution of textual inferences in the context of machine reading. A textual inference is simply defined as a relation between two fragments of text: a human reading the first can reasonably infer that the second is true. A lot of different NLP tasks more or less directly evaluate systems on their ability to recognize textual inference. Among this multiplicity of evaluation frameworks, inferences themselves are not one and the same and also present a wide variety of different types. We reflect on inferences for NLP from a theoretical standpoint and present two contributions addressing these levels of diversity: an abstract contextualized inference task encompassing most NLP inference-related tasks, and a novel hierchical taxonomy of textual inferences based on their difficulty.Automatically recognizing textual inference currently almost always involves a machine learning model, trained to use various linguistic features on a labeled dataset of samples of textual inference. However, specific data on complex inference phenomena is not currently abundant enough that systems can directly learn world knowledge and commonsense reasoning. Instead, systems focus on learning how to use the syntactic structure of sentences to align the words of two semantically related sentences. To extend what systems know of the world, they include external background knowledge, often improving their results. But this addition is often made on top of other features, and rarely well integrated to sentence structure. The main contributions of our thesis address the previous concern, with the aim of solving complex natural language understanding tasks. With the hypothesis that a simpler lexicon should make easier to compare the sense of two sentences, we present a passage retrieval method using structured lexical expansion backed up by a simplifying dictionary. This simplification hypothesis is tested again in a contribution on textual entailment: syntactical paraphrases are extracted from the same dictionary and repeatedly applied on the first sentence to turn it into the second. We then present a machine learning kernel-based method recognizing sentence rewritings, with a notion of types able to encode lexical-semantic knowledge. This approach is effective on three tasks: paraphrase identification, textual entailment and question answering. We address its lack of scalability while keeping most of its strengths in our last contribution. Reading comprehension tests are used for evaluation: these multiple-choice questions on short text constitute the most practical way to assess textual inference within a complete context. Our system is founded on a efficient tree edit algorithm, and the features extracted from edit sequences are used to build two classifiers for the validation and invalidation of answer candidates. This approach reaches second place at the "Entrance Exams" CLEF 2015 challenge.

abstract: Languages, specially gestural and sign languages, are best learned in immersive environments with rich feedback. Computer-Aided Language Learning (CALL) solu- tions for spoken languages have successfully incorporated some feedback mechanisms, but no such solution exists for signed languages. Computer Aided Sign Language Learning (CASLL) is a recent and promising field of research which is made feasible by advances in Computer Vision and Sign Language Recognition(SLR). Leveraging existing SLR systems for feedback based learning is not feasible because their decision processes are not human interpretable and do not facilitate conceptual feedback to learners. Thus, fundamental research is needed towards designing systems that are modular and explainable. The explanations from these systems can then be used to produce feedback to aid in the learning process. In this work, I present novel approaches for the recognition of location, movement and handshape that are components of American Sign Language (ASL) using both wrist-worn sensors as well as webcams. Finally, I present Learn2Sign(L2S), a chat- bot based AI tutor that can provide fine-grained conceptual feedback to learners of ASL using the modular recognition approaches. L2S is designed to provide feedback directly relating to the fundamental concepts of ASL using an explainable AI. I present the system performance results in terms of Precision, Recall and F-1 scores as well as validation results towards the learning outcomes of users. Both retention and execution tests for 26 participants for 14 different ASL words learned using learn2sign is presented. Finally, I also present the results of a post-usage usability survey for all the participants. In this work, I found that learners who received live feedback on their executions improved their execution as well as retention performances. The average increase in execution performance was 28% points and that for retention was 4% points. / Dissertation/Thesis / Doctoral Dissertation Engineering 2020

Natural Language (NL) mechanics are seemingly underutilized within modern game development and may be capable of inducing unexpected levels of agency within its users. This study focuses specifically on NL Input (NLI) and examines its capability of inducing an experience of agency, control, and freedom through an interactive fiction with a database searching context. To get a more nuanced result, a version of the artefact but with an NL Understanding (NLU) system will also be tested to create a baseline. Due to the limited time and resources, the NLU version will be employing the Wizard of Oz (WOZ) method. In total, five NLI tests and four NLU tests were performed and interview results indicated full experience of control and mixed experience of freedom and agency in both versions. Possible causes include the participants‘ genre preference and the limited content in the artefact.

abstract: Reasoning with commonsense knowledge is an integral component of human behavior. It is due to this capability that people know that a weak person may not be able to lift someone. It has been a long standing goal of the Artificial Intelligence community to simulate such commonsense reasoning abilities in machines. Over the years, many advances have been made and various challenges have been proposed to test their abilities. The Winograd Schema Challenge (WSC) is one such Natural Language Understanding (NLU) task which was also proposed as an alternative to the Turing Test. It is made up of textual question answering problems which require resolution of a pronoun to its correct antecedent. In this thesis, two approaches of developing NLU systems to solve the Winograd Schema Challenge are demonstrated. To this end, a semantic parser is presented, various kinds of commonsense knowledge are identified, techniques to extract commonsense knowledge are developed and two commonsense reasoning algorithms are presented. The usefulness of the developed tools and techniques is shown by applying them to solve the challenge. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

abstract: In this thesis, I present two new datasets and a modification to the existing models in the form of a novel attention mechanism for Natural Language Inference (NLI). The new datasets have been carefully synthesized from various existing corpora released for different tasks. The task of NLI is to determine the possibility of a sentence referred to as “Hypothesis” being true given that another sentence referred to as “Premise” is true. In other words, the task is to identify whether the “Premise” entails, contradicts or remains neutral with regards to the “Hypothesis”. NLI is a precursor to solving many Natural Language Processing (NLP) tasks such as Question Answering and Semantic Search. For example, in Question Answering systems, the question is paraphrased to form a declarative statement which is treated as the hypothesis. The options are treated as the premise. The option with the maximum entailment score is considered as the answer. Considering the applications of NLI, the importance of having a strong NLI system can't be stressed enough. Many large-scale datasets and models have been released in order to advance the field of NLI. While all of these models do get good accuracy on the test sets of the datasets they were trained on, they fail to capture the basic understanding of “Entities” and “Roles”. They often make the mistake of inferring that “John went to the market.” from “Peter went to the market.” failing to capture the notion of “Entities”. In other cases, these models don't understand the difference in the “Roles” played by the same entities in “Premise” and “Hypothesis” sentences and end up wrongly inferring that “Peter drove John to the stadium.” from “John drove Peter to the stadium.” The lack of understanding of “Roles” can be attributed to the lack of such examples in the various existing datasets. The reason for the existing model’s failure in capturing the notion of “Entities” is not just due to the lack of such examples in the existing NLI datasets. It can also be attributed to the strict use of vector similarity in the “word-to-word” attention mechanism being used in the existing architectures. To overcome these issues, I present two new datasets to help make the NLI systems capture the notion of “Entities” and “Roles”. The “NER Changed” (NC) dataset and the “Role-Switched” (RS) dataset contains examples of Premise-Hypothesis pairs that require the understanding of “Entities” and “Roles” respectively in order to be able to make correct inferences. This work shows how the existing architectures perform poorly on the “NER Changed” (NC) dataset even after being trained on the new datasets. In order to help the existing architectures, understand the notion of “Entities”, this work proposes a modification to the “word-to-word” attention mechanism. Instead of relying on vector similarity alone, the modified architectures learn to incorporate the “Symbolic Similarity” as well by using the Named-Entity features of the Premise and Hypothesis sentences. The new modified architectures not only perform significantly better than the unmodified architectures on the “NER Changed” (NC) dataset but also performs as well on the existing datasets. / Dissertation/Thesis / Masters Thesis Computer Science 2019

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