Enhancing knowledge acquisition systems with user generated and crowdsourced resources

This thesis is on leveraging knowledge acquisition systems with collaborative data and crowdsourcing work from internet. We propose two strategies and apply them for building effective entity linking and question answering (QA) systems. The first strategy is on integrating an information extraction system with online collaborative knowledge bases, such as Wikipedia and Freebase. We construct a Cross-Lingual Entity Linking (CLEL) system to connect Chinese entities, such as people and locations, with corresponding English pages in Wikipedia. The main focus is to break the language barrier between Chinese entities and the English KB, and to resolve the synonymy and polysemy of Chinese entities. To address those problems, we create a cross-lingual taxonomy and a Chinese knowledge base (KB). We investigate two methods of connecting the query representation with the KB representation. Based on our CLEL system participating in TAC KBP 2011 evaluation, we finally propose a simple and effective generative model, which achieved much better performance. The second strategy is on creating annotation for QA systems with the help of crowd- sourcing. Crowdsourcing is to distribute a task via internet and recruit a lot of people to complete it simultaneously. Various annotated data are required to train the data-driven statistical machine learning algorithms for underlying components in our QA system. This thesis demonstrates how to convert the annotation task into crowdsourcing micro-tasks, investigate different statistical methods for enhancing the quality of crowdsourced anno- tation, and finally use enhanced annotation to train learning to rank models for passage ranking algorithms for QA.Gegenstand dieser Arbeit ist das Nutzbarmachen sowohl von Systemen zur Wissener- fassung als auch von kollaborativ erstellten Daten und Arbeit aus dem Internet. Es werden zwei Strategien vorgeschlagen, welche für die Erstellung effektiver Entity Linking (Disambiguierung von Entitätennamen) und Frage-Antwort Systeme eingesetzt werden. Die erste Strategie ist, ein Informationsextraktions-System mit kollaborativ erstellten Online- Datenbanken zu integrieren. Wir entwickeln ein Cross-Linguales Entity Linking-System (CLEL), um chinesische Entitäten, wie etwa Personen und Orte, mit den entsprechenden Wikipediaseiten zu verknüpfen. Das Hauptaugenmerk ist es, die Sprachbarriere zwischen chinesischen Entitäten und englischer Datenbank zu durchbrechen, und Synonymie und Polysemie der chinesis- chen Entitäten aufzulösen. Um diese Probleme anzugehen, erstellen wir eine cross linguale Taxonomie und eine chinesische Datenbank. Wir untersuchen zwei Methoden, die Repräsentation der Anfrage und die Repräsentation der Datenbank zu verbinden. Schließlich stellen wir ein einfaches und effektives generatives Modell vor, das auf unserem System für die Teilnahme an der TAC KBP 2011 Evaluation basiert und eine erheblich bessere Performanz erreichte. Die zweite Strategie ist, Annotationen für Frage-Antwort-Systeme mit Hilfe von "Crowd- sourcing" zu erstellen. "Crowdsourcing" bedeutet, eine Aufgabe via Internet an eine große Menge an angeworbene Menschen zu verteilen, die diese simultan erledigen. Verschiedene annotierte Daten sind notwendig, um die datengetriebenen statistischen Lernalgorithmen zu trainieren, die unserem Frage-Antwort System zugrunde liegen. Wir zeigen, wie die Annotationsaufgabe in Mikro-Aufgaben für das Crowdsourcing umgewan- delt werden kann, wir untersuchen verschiedene statistische Methoden, um die Qualität der Annotation aus dem Crowdsourcing zu erweitern, und schließlich nutzen wir die erwei- erte Annotation, um Modelle zum Lernen von Ranglisten von Textabschnitten zu trainieren

In Crowd Veritas: Leveraging Human Intelligence To Fight Misinformation

The spread of online misinformation has important effects on the stability of democracy. The sheer size of digital content on the web and social media and the ability to immediately access and share it has made it difficult to perform timely fact-checking at scale. Truthfulness judgments are usually made by experts, like journalists for political statements. A different approach can be relying on a (non-expert) crowd of human judges to perform fact-checking. This leads to the following research question: can such human judges detect and objectively categorize online (mis)information? Several extensive studies based on crowdsourcing are performed to answer. Thousands of truthfulness judgments over two datasets are collected by recruiting a crowd of workers from crowdsourcing platforms and the expert judgments are compared with the crowd ones. The results obtained allow for concluding that the workers are indeed able to do such. There is a limited understanding of factors that influence worker participation in longitudinal studies across different crowdsourcing marketplaces. A large-scale survey aimed at understanding how these studies are performed using crowdsourcing is run across multiple platforms. The answers collected are analyzed from both a quantitative and a qualitative point of view. A list of recommendations for task requesters to conduct these studies effectively is provided together with a list of best practices for crowdsourcing platforms. Truthfulness is a subtle matter: statements can be just biased, imprecise, wrong, etc. and a unidimensional truth scale cannot account for such differences. The crowd workers are asked to judge seven different dimensions of truthfulness selected based on existing literature. The newly collected crowdsourced judgments show that the workers are indeed reliable when compared to an expert-provided gold standard. Cognitive biases are human processes that often help minimize the cost of making mistakes but keep assessors away from an objective judgment of information. A review of the cognitive biases which might manifest during the fact-checking process is presented together with a list of countermeasures that can be adopted. An exploratory study on the previously collected data set is thus performed. The findings are used to formulate hypotheses concerning which individual characteristics of statements or judges and what cognitive biases may affect crowd workers' truthfulness judgments. The findings suggest that crowd workers' degree of belief in science has an impact, that they generally overestimate truthfulness, and that their judgments are indeed affected by various cognitive biases. Automated fact-checking systems to combat misinformation spreading exist, however, their complexity usually makes them opaque to the end user, making it difficult to foster trust in the system. The E-BART model is introduced with the hope of making progress on this front. E-BART can provide a truthfulness prediction for a statement, and jointly generate a human-readable explanation. An extensive human evaluation of the impact of explanations generated by the model is conducted, showing that the explanations increase the human ability to spot misinformation. The whole set of data collected and analyzed in this thesis is publicly released to the research community at: https://doi.org/10.17605/OSF.IO/JR6VC.The spread of online misinformation has important effects on the stability of democracy. The information that is consumed every day influences human decision-making processes. The sheer size of digital content on the web and social media and the ability to immediately access and share it has made it difficult to perform timely fact-checking at scale. Indeed, fact-checking is a complex process that involves several activities. A long-term goal can be building a so-called human-in-the-loop system to cope with (mis)information by measuring truthfulness in real-time (e.g., as they appear on some social media, news outlets, and so on) using a combination of crowd-powered data, human intelligence, and machine learning techniques. In recent years, crowdsourcing has become a popular method for collecting to collect reliable truthfulness judgments in order to scale up and help study the manual fact-checking effort. Initially, this thesis investigates whether human judges can detect and objectively categorize online (mis)information and which is the environment that allows obtaining the best results. Then, the impact of cognitive biases on human assessors while judging information truthfulness is addressed. A categorization of cognitive biases is proposed together with countermeasures to combat their effects and a bias-aware judgment pipeline for fact-checking. Lastly, an approach able to predict information truthfulness and, at the same time, generate a natural language explanation supporting the prediction itself is proposed. The machine-generated explanations are evaluated to understand whether they are useful for the human assessors to better judge the truthfulness of information items. A collaborative process between systems, crowd workers, and expert fact checkers would provide a scalable and decentralized hybrid mechanism to cope with the increasing volume of online misinformation

A Critical Look at the Evaluation of Knowledge Graph Question Answering

PhD thesis in Information technologyThe field of information retrieval (IR) is concerned with systems that “make a given stored collection of information items available to a user population” [111]. The way in which information is made available to the user depends on the formulation of this broad concern of IR into specific tasks by which a system should address a user’s information need [85]. The specific IR task also dictates how the user may express their information need. The classic IR task is ad hoc retrieval, where the user issues a query to the system and gets in return a list of documents ranked by estimated relevance of each document to the query [85]. However, it has long been acknowledged that users are often looking for answers to questions, rather than an entire document or ranked list of documents [17, 141]. Question answering (QA) is thus another IR task; it comes in many flavors, but overall consists of taking in a user’s natural language (NL) question and returning an answer. This thesis describes work done within the scope of the QA task. The flavor of QA called knowledge graph question answering (KGQA) is taken as the primary focus, which enables QA with factual questions against structured data in the form of a knowledge graph (KG). This means the KGQA system addresses a structured representation of knowledge rather than—as in other QA flavors—an unstructured prose context. KGs have the benefit that given some identified entities or predicates, all associated properties are available and relationships can be utilized. KGQA then enables users to access structured data using only NL questions and without requiring formal query language expertise. Even so, the construction of satisfactory KGQA systems remains a challenge. Machine learning with deep neural networks (DNNs) is a far more promising approach than manually engineering retrieval models [29, 56, 130]. The current era dominated by DNNs began with seminal work on computer vision, where the deep learning paradigm demonstrated its first cases of “superhuman” performance [32, 71]. Subsequent work in other applications has also demonstrated “superhuman” performance with DNNs [58, 87]. As a result of its early position and hence longer history as a leading application of deep learning, computer vision with DNNs has been bolstered with much work on different approaches towards augmenting [120] or synthesizing [94] additional training data. The difficulty with machine learning approaches to KGQA appears to rest in large part with the limited volume, quality, and variety of available datasets for this task. Compared to labeled image data for computer vision, the problems of data collection, augmentation, and synthesis are only to a limited extent solved for QA, and especially for KGQA. There are few datasets for KGQA overall, and little previous work that has found unsupervised or semi-supervised learning approaches to address the sparsity of data. Instead, neural network approaches to KGQA rely on either fully or weakly supervised learning [29]. We are thus concerned with neural models trained in a supervised setting to perform QA tasks, especially of the KGQA flavor. Given a clear task to delegate to a computational system, it seems clear that we want the task performed as well as possible. However, what methodological elements are important to ensure good system performance within the chosen scope? How should the quality of system performance be assessed? This thesis describes work done to address these overarching questions through a number of more specific research questions. Altogether, we designate the topic of this thesis as KGQA evaluation, which we address in a broad sense, encompassing four subtopics from (1) the impact on performance due to volume of training data provided and (2) the information leakage between training and test splits due to unhygienic data partitioning, through (3) the naturalness of NL questions resulting from a common approach for generating KGQA datasets, to (4) the axiomatic analysis and development of evaluation measures for a specific flavor of the KGQA task. Each of the four subtopics is informed by previous work, but we aim in this thesis to critically examine the assumptions of previous work to uncover, verify, or address weaknesses in current practices surrounding KGQA evaluation

Supporting Source Code Search with Context-Aware and Semantics-Driven Query Reformulation

Software bugs and failures cost trillions of dollars every year, and could even lead to deadly accidents (e.g., Therac-25 accident). During maintenance, software developers fix numerous bugs and implement hundreds of new features by making necessary changes to the existing software code. Once an issue report (e.g., bug report, change request) is assigned to a developer, she chooses a few important keywords from the report as a search query, and then attempts to find out the exact locations in the software code that need to be either repaired or enhanced. As a part of this maintenance, developers also often select ad hoc queries on the fly, and attempt to locate the reusable code from the Internet that could assist them either in bug fixing or in feature implementation. Unfortunately, even the experienced developers often fail to construct the right search queries. Even if the developers come up with a few ad hoc queries, most of them require frequent modifications which cost significant development time and efforts. Thus, construction of an appropriate query for localizing the software bugs, programming concepts or even the reusable code is a major challenge. In this thesis, we overcome this query construction challenge with six studies, and develop a novel, effective code search solution (BugDoctor) that assists the developers in localizing the software code of interest (e.g., bugs, concepts and reusable code) during software maintenance. In particular, we reformulate a given search query (1) by designing novel keyword selection algorithms (e.g., CodeRank) that outperform the traditional alternatives (e.g., TF-IDF), (2) by leveraging the bug report quality paradigm and source document structures which were previously overlooked and (3) by exploiting the crowd knowledge and word semantics derived from Stack Overflow Q&A site, which were previously untapped. Our experiment using 5000+ search queries (bug reports, change requests, and ad hoc queries) suggests that our proposed approach can improve the given queries significantly through automated query reformulations. Comparison with 10+ existing studies on bug localization, concept location and Internet-scale code search suggests that our approach can outperform the state-of-the-art approaches with a significant margin

Entity-Oriented Search

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms

Towards a crowdsourced solution for the authoring bottleneck in interactive narratives

Interactive Storytelling research has produced a wealth of technologies that can be employed to create personalised narrative experiences, in which the audience takes a participating rather than observing role. But so far this technology has not led to the production of large scale playable interactive story experiences that realise the ambitions of the field. One main reason for this state of affairs is the difficulty of authoring interactive stories, a task that requires describing a huge amount of story building blocks in a machine friendly fashion. This is not only technically and conceptually more challenging than traditional narrative authoring but also a scalability problem. This thesis examines the authoring bottleneck through a case study and a literature survey and advocates a solution based on crowdsourcing. Prior work has already shown that combining a large number of example stories collected from crowd workers with a system that merges these contributions into a single interactive story can be an effective way to reduce the authorial burden. As a refinement of such an approach, this thesis introduces the novel concept of Crowd Task Adaptation. It argues that in order to maximise the usefulness of the collected stories, a system should dynamically and intelligently analyse the corpus of collected stories and based on this analysis modify the tasks handed out to crowd workers. Two authoring systems, ENIGMA and CROSCAT, which show two radically different approaches of using the Crowd Task Adaptation paradigm have been implemented and are described in this thesis. While ENIGMA adapts tasks through a realtime dialog between crowd workers and the system that is based on what has been learned from previously collected stories, CROSCAT modifies the backstory given to crowd workers in order to optimise the distribution of branching points in the tree structure that combines all collected stories. Two experimental studies of crowdsourced authoring are also presented. They lead to guidelines on how to employ crowdsourced authoring effectively, but more importantly the results of one of the studies demonstrate the effectiveness of the Crowd Task Adaptation approach

Report from Dagstuhl Seminar 23031: Frontiers of Information Access Experimentation for Research and Education

This report documents the program and the outcomes of Dagstuhl Seminar 23031 ``Frontiers of Information Access Experimentation for Research and Education'', which brought together 37 participants from 12 countries. The seminar addressed technology-enhanced information access (information retrieval, recommender systems, natural language processing) and specifically focused on developing more responsible experimental practices leading to more valid results, both for research as well as for scientific education. The seminar brought together experts from various sub-fields of information access, namely IR, RS, NLP, information science, and human-computer interaction to create a joint understanding of the problems and challenges presented by next generation information access systems, from both the research and the experimentation point of views, to discuss existing solutions and impediments, and to propose next steps to be pursued in the area in order to improve not also our research methods and findings but also the education of the new generation of researchers and developers. The seminar featured a series of long and short talks delivered by participants, who helped in setting a common ground and in letting emerge topics of interest to be explored as the main output of the seminar. This led to the definition of five groups which investigated challenges, opportunities, and next steps in the following areas: reality check, i.e. conducting real-world studies, human-machine-collaborative relevance judgment frameworks, overcoming methodological challenges in information retrieval and recommender systems through awareness and education, results-blind reviewing, and guidance for authors.Comment: Dagstuhl Seminar 23031, report

Extracting personal information from conversations

Personal knowledge is a versatile resource that is valuable for a wide range of downstream applications. Background facts about users can allow chatbot assistants to produce more topical and empathic replies. In the context of recommendation and retrieval models, personal facts can be used to customize the ranking results for individual users. A Personal Knowledge Base, populated with personal facts, such as demographic information, interests and interpersonal relationships, is a unique endpoint for storing and querying personal knowledge. Such knowledge bases are easily interpretable and can provide users with full control over their own personal knowledge, including revising stored facts and managing access by downstream services for personalization purposes. To alleviate users from extensive manual effort to build such personal knowledge base, we can leverage automated extraction methods applied to the textual content of the users, such as dialogue transcripts or social media posts. Mainstream extraction methods specialize on well-structured data, such as biographical texts or encyclopedic articles, which are rare for most people. In turn, conversational data is abundant but challenging to process and requires specialized methods for extraction of personal facts. In this dissertation we address the acquisition of personal knowledge from conversational data. We propose several novel deep learning models for inferring speakers’ personal attributes: • Demographic attributes, age, gender, profession and family status, are inferred by HAMs - hierarchical neural classifiers with attention mechanism. Trained HAMs can be transferred between different types of conversational data and provide interpretable predictions. • Long-tailed personal attributes, hobby and profession, are predicted with CHARM - a zero-shot learning model, overcoming the lack of labeled training samples for rare attribute values. By linking conversational utterances to external sources, CHARM is able to predict attribute values which it never saw during training. • Interpersonal relationships are inferred with PRIDE - a hierarchical transformer-based model. To accurately predict fine-grained relationships, PRIDE leverages personal traits of the speakers and the style of conversational utterances. Experiments with various conversational texts, including Reddit discussions and movie scripts, demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.Personengebundene Fakten sind eine vielseitig nutzbare Quelle für die verschiedensten Anwendungen. Hintergrundfakten über Nutzer können es Chatbot-Assistenten ermöglichen, relevantere und persönlichere Antworten zu geben. Im Kontext von Empfehlungs- und Retrievalmodellen können personengebundene Fakten dazu verwendet werden, die Ranking-Ergebnisse für Nutzer individuell anzupassen. Eine Personengebundene Wissensdatenbank, gefüllt mit persönlichen Daten wie demografischen Angaben, Interessen und Beziehungen, kann eine universelle Schnittstelle für die Speicherung und Abfrage solcher Fakten sein. Wissensdatenbanken sind leicht zu interpretieren und bieten dem Nutzer die vollständige Kontrolle über seine personenbezogenen Fakten, einschließlich der Überarbeitung und der Verwaltung des Zugriffs durch nachgelagerte Dienste, etwa für Personalisierungszwecke. Um den Nutzern den aufwändigen manuellen Aufbau einer solchen persönlichen Wissensdatenbank zu ersparen, können automatisierte Extraktionsmethoden auf den textuellen Inhalten der Nutzer – wie z.B. Konversationen oder Beiträge in sozialen Medien – angewendet werden. Die üblichen Extraktionsmethoden sind auf strukturierte Daten wie biografische Texte oder enzyklopädische Artikel spezialisiert, die bei den meisten Menschen keine Rolle spielen. In dieser Dissertation beschäftigen wir uns mit der Gewinnung von persönlichem Wissen aus Dialogdaten und schlagen mehrere neuartige Deep-Learning-Modelle zur Ableitung persönlicher Attribute von Sprechern vor: • Demographische Attribute wie Alter, Geschlecht, Beruf und Familienstand werden durch HAMs - Hierarchische Neuronale Klassifikatoren mit Attention-Mechanismus - abgeleitet. Trainierte HAMs können zwischen verschiedenen Arten von Gesprächsdaten übertragen werden und liefern interpretierbare Vorhersagen • Vielseitige persönliche Attribute wie Hobbys oder Beruf werden mit CHARM ermittelt - einem Zero-Shot-Lernmodell, das den Mangel an markierten Trainingsbeispielen für seltene Attributwerte überwindet. Durch die Verknüpfung von Gesprächsäußerungen mit externen Quellen ist CHARM in der Lage, Attributwerte zu ermitteln, die es beim Training nie gesehen hat • Zwischenmenschliche Beziehungen werden mit PRIDE, einem hierarchischen transformerbasierten Modell, abgeleitet. Um präzise Beziehungen vorhersagen zu können, nutzt PRIDE persönliche Eigenschaften der Sprecher und den Stil von Konversationsäußerungen Experimente mit verschiedenen Konversationstexten, inklusive Reddit-Diskussionen und Filmskripten, demonstrieren die Praxistauglichkeit unserer Methoden und ihre hervorragende Leistung im Vergleich zum aktuellen Stand der Technik