57 research outputs found

    Understanding comparative questions and retrieving argumentative answers

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    Making decisions is an integral part of everyday life, yet it can be a difficult and complex process. While peoples’ wants and needs are unlimited, resources are often scarce, making it necessary to research the possible alternatives and weigh the pros and cons before making a decision. Nowadays, the Internet has become the main source of information when it comes to comparing alternatives, making search engines the primary means for collecting new information. However, relying only on term matching is not sufficient to adequately address requests for comparisons. Therefore, search systems should go beyond this approach to effectively address comparative information needs. In this dissertation, I explore from different perspectives how search systems can respond to comparative questions. First, I examine approaches to identifying comparative questions and study their underlying information needs. Second, I investigate a methodology to identify important constituents of comparative questions like the to-be-compared options and to detect the stance of answers towards these comparison options. Then, I address ambiguous comparative search queries by studying an interactive clarification search interface. And finally, addressing answering comparative questions, I investigate retrieval approaches that consider not only the topical relevance of potential answers but also account for the presence of arguments towards the comparison options mentioned in the questions. By addressing these facets, I aim to provide a comprehensive understanding of how to effectively satisfy the information needs of searchers seeking to compare different alternatives

    Social Search: retrieving information in Online Social Platforms -- A Survey

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    Social Search research deals with studying methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new taxonomy and highlighting current limitations and open research directions. We divide the Social Search area into three subcategories, where the social aspect plays a pivotal role: Social Question&Answering, Social Content Search, and Social Collaborative Search. For each subcategory, we present the key concepts and selected representative approaches in the literature in greater detail. We found that, up to now, a large body of studies model users' preferences and their relations by simply combining social features made available by social platforms. It paves the way for significant research to exploit more structured information about users' social profiles and behaviors (as they can be inferred from data available on social platforms) to optimize their information needs further

    Improving The Usability of Software Systems Using Group Discussions: A Case Study on Galaxy

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    Usability problems in software systems cause performance degradation, user dissatisfaction and loss in terms of cost. There is a growing need for the software systems to become more accessible, retrievable and usable for the users. The usability test of a software is conducted by getting the opinions directly from the users and its goal is to identify problems, uncover opportunities and learn about target users' preferences. But accessing real users is very difficult for certain software systems. However, there are many popular user forums such as Stack Overflow, Quora, Stack Exchange, Eclipse Community Forum etc. and people from different domains of knowledge use these forums to ask about their problems and post their concerns. So exploring these forums should provide significant knowledge for getting information about a system's usability issues. Previous studies show that investigating these group discussion forums discovered several usability issues that the system was unaware of such as topic categorization, automatic tag prediction, identifying reproducible codes etc. However, there are many Scientific Workflow Management Systems (SWfMSs) such as Galaxy, Taverna, Kepler, iPlant, VizSciFlow etc. and although these SWfMSs are emerging and important for data extensive research, no study has been done earlier to figure out the usability problems of these systems. Therefore, in this thesis, we take Galaxy, a well-known SWfMS, as our use case. We explore the user forum that Galaxy offers where users ask for help from experts and other Galaxy users. We search for the issues users are discussing in the forum and find out several usability problems in different categories. In our first study, we try to group the usability problems to easily identify them and galaxy community can be informed of the existing usability problems of the system. While exploring the posts, we find a significant percentage (up to 28\%) of them lack tags. If tags are found, they do not reflect the context of the posts properly. This leads to one of the major usability problems for the discussion forums as users will be unable to identify suitable posts without proper tags. Moreover, users will face difficulties to explore the answers in those untagged questions. So in our second study, we try to suggest tags based on the context and proposed a method for automatically suggesting tags. Again in our extensive investigation, we find lots of usability issues but among them, the problem of finding and searching for the appropriate workflows emerges as a great usability problem of the system. Users, especially novice users, ask for workflow design recommendations from the experts but because of the domain-specific nature of SWfMSs, it gets difficult for them to design or implement a workflow according to their new requirements. Any software system's usability is called into question if users face trouble specifying or carrying out certain tasks and are not given the necessary resources. Therefore, to increase the usability of Galaxy, in our third study, we introduce a NLP-based workflow recommendation system where anyone can write their queries using natural language. Our system can recommend the users with the most relevant workflows in return. We develop a tool on the Galaxy platform based on the idea of the proposed method. Lastly, we believe our study findings can guide the Galaxy community to improve and extend the services according to the users' requirements. We are confident that our proposed methods can be applied to any software system to improve the usability of the system by exploring the user forums

    Leveraging Formulae and Text for Improved Math Retrieval

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    Large collections containing millions of math formulas are available online. Retrieving math expressions from these collections is challenging. Users can use formula, formula+text, or math questions to express their math information needs. The structural complexity of formulas requires specialized processing. Despite the existence of math search systems and online community question-answering websites for math, little is known about mathematical information needs. This research first explores the characteristics of math searches using a general search engine. The findings show how math searches are different from general searches. Then, test collections for math-aware search are introduced. The ARQMath test collections have two main tasks: 1) finding answers for math questions and 2) contextual formula search. In each test collection (ARQMath-1 to -3) the same collection is used, Math Stack Exchange posts from 2010 to 2018, introducing different topics for each task. Compared to the previous test collections, ARQMath has a much larger number of diverse topics, and improved evaluation protocol. Another key role of this research is to leverage text and math information for improved math information retrieval. Three formula search models that only use the formula, with no context are introduced. The first model is an n-gram embedding model using both symbol layout tree and operator tree representations. The second model uses tree-edit distance to re-rank the results from the first model. Finally, a learning-to-rank model that leverages full-tree, sub-tree, and vector similarity scores is introduced. To use context, Math Abstract Meaning Representation (MathAMR) is introduced, which generalizes AMR trees to include math formula operations and arguments. This MathAMR is then used for contextualized formula search using a fine-tuned Sentence-BERT model. The experiments show tree-edit distance ranking achieves the current state-of-the-art results on contextual formula search task, and the MathAMR model can be beneficial for re-ranking. This research also addresses the answer retrieval task, introducing a two-step retrieval model in which similar questions are first found and then answers previously given to those similar questions are ranked. The proposed model, fine-tunes two Sentence-BERT models, one for finding similar questions and another one for ranking the answers. For Sentence-BERT model, raw text as well as MathAMR are used

    Actas del XXIV Workshop de Investigadores en Ciencias de la Computación: WICC 2022

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    Compilación de las ponencias presentadas en el XXIV Workshop de Investigadores en Ciencias de la Computación (WICC), llevado a cabo en Mendoza en abril de 2022.Red de Universidades con Carreras en Informátic

    Conversational question answering: a survey

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    Published online: 6 September 2022Question answering (QA) systems provide a way of querying the information available in various formats including, but not limited to, unstructured and structured data in natural languages. It constitutes a considerable part of conversational artificial intelligence (AI) which has led to the introduction of a special research topic on conversational question answering (CQA), wherein a system is required to understand the given context and then engages in multi-turn QA to satisfy a user’s information needs. While the focus of most of the existing research work is subjected to single-turn QA, the field of multi-turn QA has recently grasped attention and prominence owing to the availability of large-scale, multi-turn QA datasets and the development of pre-trained language models. With a good amount of models and research papers adding to the literature every year recently, there is a dire need of arranging and presenting the related work in a unified manner to streamline future research. This survey is an effort to present a comprehensive review of the state-of-the-art research trends of CQA primarily based on reviewed papers over the recent years. Our findings show that there has been a trend shift from single-turn to multi-turn QA which empowers the field of Conversational AI from different perspectives. This survey is intended to provide an epitome for the research community with the hope of laying a strong foundation for the field of CQA.Munazza Zaib, Wei Emma Zhang, Quan Z. Sheng, Adnan Mahmood, Yang Zhan

    Interpreting Neural Networks for and with Natural Language

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    In the past decade, natural language processing (NLP) systems have come to be built almost exclusively on a backbone of large neural models. As the landscape of feasible tasks has widened due to the capabilities of these models, the space of applications has also widened to include subfields with real-world consequences, such as fact-checking, fake news detection, and medical decision support. The increasing size and nonlinearity of these models results in an opacity that hinders efforts by machine learning practitioners and lay-users alike to understand their internals and derive meaning or trust from their predictions. The fields of explainable artificial intelligence (XAI) and more specifically explainable NLP (ExNLP) have emerged as an active area for remedying this opacity and for ensuring models' reliability and trustworthiness in high-stakes scenarios, by providing textual explanations meaningful to human users. Models that produce justifications for their individual predictions can be inspected for the purposes of debugging, quantifying bias and fairness, understanding model behavior, and ascertaining robustness and privacy. Textual explanation is a predominant form of explanation in machine learning datasets regardless of task modality. As such, this dissertation covers both explaining tasks with natural language and explaining natural language tasks. In this dissertation, I propose test suites for evaluating the quality of model explanations under two definitions of meaning: faithfulness and human acceptability. I use these evaluation methods to investigate the utility of two explanation forms and three model architectures. I finally propose two methods to improve explanation quality– one which increases the likelihood of faithful highlight explanations and one which improves the human acceptability of free-text explanations. This work strives to increase the likelihood of positive use and outcomes when AI systems are deployed in practice.Ph.D

    Entity-Oriented Search

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    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
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