What do you think of when you hear the word chatbot? A helpful assistant when booking flight tickets? Maybe a frustrating encounter with a company’s customer support, or smart technologies that will eventually take over your job? The field of chatbots is under constant development and bots are more and more taking a place in our everyday life, but how well do they really understand us humans?  The objective of this thesis is to investigate how capable two open source chatbots are in understanding human language when given input containing spelling errors, synonyms or faulty syntax. The study will further investigate if the bots get better at identifying what the user’s intention is when supplied with more training data to base their analysis on.  Two different chatbot frameworks, Botpress and Rasa, were consulted to execute this experiment. The two bots were created with basic configurations and trained using the same data. The chatbots underwent three rounds of training and testing, where they were given additional training and asked control questions to see if they managed to interpret the correct intent. All tests were documented and scores were calculated to create comparable data. The results from these tests showed that both chatbots performed well when it came to simpler spelling errors and syntax variations. Their understanding of more complex spelling errors were lower in the first testing phase but increased with more training data. Synonyms followed a similar pattern, but showed a minor tendency towards becoming overconfident and producing incorrect results with a high confidence in the last phase. The scores pointed to both chatbots getting better at understanding the input when receiving additional training. In conclusion, both chatbots showed signs of understanding language variations when given minimal training, but got significantly better results when provided with more data. The potential to create a bot with a substantial understanding of human language is evident with these results, even for developers who are previously not experienced with creating chatbots, also taking into consideration the vast possibilities to customise your chatbot.

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There is an overwhelming variation in the ways an intelligent agent can rationalize communication with a conversational partner. This variation presents many incompatibilities that lead to the specialization of conversational capabilities. This has produced a plethora of models and ideas on how an intelligent agent should understand, interact with, and incorporate communication from a human conversational participant. This dissertation approaches this problem with the thesis that there exists a language between that of human natural language and the behavioral reasoning of an intelligent agent, and that this language is capable of not only unifying the various models used in literature, but also provides the foundation for a theoretical framework for an engineering methodology for building such models. A theory of practical communication language is developed, including the introduction of the meaning-action concept, an expressive and powerful representation based on speech-act and dialogue-act theories, but extended with notions of behavioral operators as well as signatures that allow the operators to incorporate structured and well-defined concepts. An engineering methodology is presented for the construction of concepts, operators and rules that create the language and model of a specific domain, including methodology for the verification and validation of that language and model. The resultant practical communication language methodology, based on the combination of rational communication and meaning-action concepts, will introduce several major enhancements to dialogue management. These enhancements include the use of meaning-action concepts as a shared medium and the introduction of a shared concept graph. This methodology will be used along with various dialogue models from human-human, human-agent and agent-agent communication to construct a task-oriented language and model called the task communication language framework. This framework is then implemented within an intelligent agent in a real-time resource management simulation. A sample output listing from actual human interaction with that implementation is used to demonstrate that the resulting framework does indeed incorporate many of the disparate models of communication and their corresponding capabilities including command and control, information seeking, notification and bother, clarification, explanation, discussion, negotiation, mutual planning, interruption, feedback, adjustable autonomy and corrective dialogues.

Generating computer animation from natural language instructions is a complex task that encompasses several key aspects of artificial intelligence including natural language understanding, computer graphics and knowledge representation. Traditionally, this task has been approached using rule based systems which were highly successful on their respective domains, but were difficult to generalise to other domains. In this thesis, I describe the key theories and principles behind a domain-independent machine learning framework for constructing natural language based animation systems, and show how this framework can be more flexible and more powerful than the prevalent rule based approach. / I begin this thesis with a thorough introduction to the goals of the research. I then review the most relevant literature to put this research into perspective. After the literature review, I provide brief descriptions to the most relevant technologies in both natural language processing and computer graphics. I then report original research in semantic role labelling and verb sense disambiguation, followed by a detailed description and analysis of the machine learning framework for natural language based animation generation. / The key contributions of this thesis are: a novel method for performing semantic role labelling of prepositional phrases, a novel method for performing verb sense disambiguation, and a novel machine learning framework for grounding linguistic information in virtual worlds and converting verb-semantic information to computer graphics commands to create computer animation.

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.

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.