469 research outputs found

    Quote Recommendation for Dialogs and Writings

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    ABSTRACT Citing proverbs and (famous) statements of other people can provide support, shed new perspective, and/or add humor to one's arguments in writings or dialogs. Recommending quote for dialog or writing can be done by considering the various features of the current text called context. We present five new approaches to quote recommendation: 1) methods to adjust the matching granularity for better context matching, 2) random forest based approach that utilizes word discrimination, 3) convolutional neural network based approach that captures important local semantic features, 4) recurrent neural network based approach that reflects the ordering of sentences and words in the context, and 5) rank aggregation of these algorithms for maximum performance. We adopt as baseline state-of-the-arts in citation recommendation and quote recommendation. Experiments show that our rank aggregation method outperforms the best baseline by up to 46.7%. As candidate quotes, we use famous proverbs and famous statement of other person in dialogs and writings. The quotes and their contexts were extracted from Twitter, Project Gutenberg, and Web blog corpus

    Research Methods for Non-Representational Approaches of Organizational Complexity. The Dialogical and Mediated Inquiry

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    This paper explores the methodological implications of non-representational approaches of organizational complexity. Representational theories focus on the syntactic complexity of systems, whereas organizing processes are predominantly characterized by semantic and pragmatic forms of complexity. After underlining the contribution of non-representational approaches to the study of organizations, the paper warns against the risk of confining the critique of representational frameworks to paradoxical dichotomies like intuition versus reflexive thought or theorizing versus experimenting. To sort out this difficulty, it is suggested to use a triadic theory of interpretation, and more particularly the concepts of semiotic mediation, inquiry and dialogism. Semiotic mediation dynamically links situated experience and generic classes of meanings. Inquiry articulates logical thinking, narrative thinking and experimenting. Dialogism conceptualizes the production of meaning through the situated interactions of actors. A methodological approach based on those concepts, โ€œthe dialogical and mediated inquiryโ€ (DMI), is proposed and experimented in a case study about work safety in the construction industry. This interpretive view requires complicating the inquiring process rather than the mirroring models of reality. In DMI, the inquiring process is complicated by establishing pluralist communities of inquiry in which different perspectives challenge each other. Finally the paper discusses the specific contribution of this approach compared with other qualitative methods and its present limits.Activity; Dialogism; Inquiry; Interpretation; Pragmatism; Research Methods; Semiotic Mediation; Work Safety

    ์ง€์‹ ๊ธฐ๋ฐ˜ ๋Œ€ํ™”์—์„œ์˜ ๋Œ€ํ™” ํŠน์„ฑ์„ ํ™œ์šฉํ•œ ์ง€์‹ ์„ ํƒ ๋ฐ ๋žญํ‚น ๋ฐฉ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2022. 8. ์ด์ƒ๊ตฌ.Knowledge grounded conversation (KGC) model aims to generate informative responses relevant to both conversation history and external knowledge. One of the most important parts of KGC models is to find the knowledge which provides the basis on which the responses are grounded. If the model selects inappropriate knowledge, it may produce responses that are irrelevant or lack knowledge. In this dissertation, we study the methods of leveraging conversational characteristics to select or rank the knowledge for knowledge grounded conversation. In particular, this dissertation provides novel two methods, where one of which focuses on the sequential structure of multi-turn conversation, and the other focuses on utilizing local context and topic of a long conversation. We first propose two knowledge selection strategies of which one preserves the sequential matching features and the other encodes the sequential nature of the conversation. Second, we propose a novel knowledge ranking model that composes an appropriate range of relevant documents by exploiting both the topic keywords and local context of a conversation. In addition, we apply the knowledge ranking model in quote recommendation with our new quote recommendation framework that provides hard negative samples to the model. Our experimental results show that the KGC models based on our proposed knowledge selection and ranking methods outperform the competitive models in terms of groundness and relevance.์ง€์‹ ๊ธฐ๋ฐ˜ ๋Œ€ํ™” ๋ชจ๋ธ์€ ๋Œ€ํ™” ๊ธฐ๋ก๊ณผ ์™ธ๋ถ€ ์ง€์‹ ์ด ๋‘ ๊ฐ€์ง€ ๋ชจ๋‘์— ๊ด€๋ จ๋œ ์‘๋‹ต์„ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ์ง€์‹ ๊ธฐ๋ฐ˜ ๋Œ€ํ™” ๋ชจ๋ธ์˜ ๊ฐ€์žฅ ์ค‘์š”ํ•œ ๋ถ€๋ถ„ ์ค‘ ํ•˜๋‚˜๋Š” ์‘๋‹ต์˜ ๊ธฐ๋ฐ˜์„ ์ œ๊ณตํ•˜๋Š” ์ง€์‹์„ ์ฐพ๋Š” ๊ฒƒ์ด๋‹ค. ์ง€์‹ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์ด ์ฃผ์–ด์ง„ ๋ฌธ๋งฅ์— ๋ถ€์ ํ•ฉํ•œ ์ง€์‹์„ ์ฐพ๋Š” ๊ฒฝ์šฐ ๊ด€๋ จ์„ฑ์ด ๋–จ์–ด์ง€๊ฑฐ๋‚˜ ์ง€์‹์ด ๋ถ€์กฑํ•œ ์‘๋‹ต์ด ์ƒ์„ฑ๋  ์ˆ˜ ์žˆ๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ง€์‹ ๊ธฐ๋ฐ˜ ๋Œ€ํ™”๋ฅผ ์œ„ํ•ด ๋Œ€ํ™” ์—ฌ๋Ÿฌ ํŠน์„ฑ์„ ํ™œ์šฉํ•˜์—ฌ ์ง€์‹์„ ์„ ์ •ํ•˜๋Š” ์ง€์‹ ์„ ํƒ ๋ชจ๋ธ๊ณผ ์ง€์‹ ์ˆœ์œ„ ๋ชจ๋ธ์„ ์ œ์‹œํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‹ค์ค‘ ํ„ด ๋Œ€ํ™”์—์„œ์˜ ์ˆœ์ฐจ์  ๊ตฌ์กฐ ๋˜๋Š” ์‘๋‹ต ์ด์ „ ๋ฌธ๋งฅ๊ณผ ๋Œ€ํ™”์˜ ์ฃผ์ œ๋ฅผ ํ™œ์šฉํ•˜๋Š” ์ƒˆ๋กœ์šด ๋‘ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋ฐฉ๋ฒ•์œผ๋กœ์จ ๋ณธ ๋…ผ๋ฌธ์€ ๋‘ ๊ฐ€์ง€ ์ง€์‹ ์„ ํƒ ์ „๋žต์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ์ „๋žต ์ค‘ ํ•˜๋‚˜๋Š” ์ง€์‹๊ณผ ๋Œ€ํ™” ํ„ด ๊ฐ„์˜ ์ˆœ์ฐจ์  ๋งค์นญ ํŠน์ง•์„ ๋ณด์กดํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๊ณ  ๋‹ค๋ฅธ ์ „๋žต์€ ๋Œ€ํ™”์˜ ์ˆœ์ฐจ์  ํŠน์„ฑ์„ ์ธ์ฝ”๋”ฉํ•˜์—ฌ ์ง€์‹์„ ์„ ํƒํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ ๋ณธ ๋…ผ๋ฌธ์€ ๋Œ€ํ™”์˜ ์ฃผ์ œ ํ‚ค์›Œ๋“œ์™€ ์‘๋‹ต ๋ฐ”๋กœ ์ด์ „์˜ ๋ฌธ๋งฅ์„ ๋ชจ๋‘ ํ™œ์šฉํ•˜์—ฌ ์ ์ ˆํ•œ ๋ฒ”์œ„์˜ ๊ด€๋ จ ๋ฌธ์„œ๋“ค๋กœ ๊ฒ€์ƒ‰ ๊ฒฐ๊ณผ๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ์ƒˆ๋กœ์šด ์ง€์‹ ์ˆœ์œ„ ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ง€์‹ ์ˆœ์œ„ ๋ชจ๋ธ์˜ ์ ์‘์„ฑ ๊ฒ€์ฆ์„ ์œ„ํ•ด ์ •๋‹ต ์ธ์šฉ๊ตฌ์™€ ์˜๋ฏธ์ ์œผ๋กœ ์œ ์‚ฌํ•˜์ง€๋งŒ ์ •๋‹ต์€ ์•„๋‹Œ ์ธ์šฉ๊ตฌ์˜ ์ง‘ํ•ฉ์„ ์ธ์šฉ๊ตฌ ์ˆœ์œ„ ๋ชจ๋ธ์— ์ œ๊ณตํ•˜๋Š” ์ธ์šฉ๊ตฌ ์ถ”์ฒœ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ์ง€์‹ ์„ ํƒ ๋ฐ ์ˆœ์œ„ ๋ชจ๋ธ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ์ง€์‹ ๊ธฐ๋ฐ˜ ๋Œ€ํ™” ๋ชจ๋ธ์ด ๊ฒฝ์Ÿ ๋ชจ๋ธ๋ณด๋‹ค ์™ธ๋ถ€ ์ง€์‹ ๋ฐ ๋Œ€ํ™” ๋ฌธ๋งฅ๊ณผ์˜ ๊ด€๋ จ์„ฑ ์ธก๋ฉด์—์„œ ์šฐ์ˆ˜ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ์‚ฌ๋žŒ ๊ฐ„์˜ ๋Œ€ํ™” ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•œ ๋‹ค์ˆ˜์˜ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒ€์ฆํ•˜์˜€๋‹ค.Abstract 1 1. Introduction 17 2. Background and Related Works 25 2.1 Terminology 25 2.2 Overview of Technologies for Conversational Systems 27 2.2.1 Open-domain Dialogue System 27 2.2.2 Task-oriented Dialogue System 29 2.2.3 Question Answering System 29 2.3 Components of Knowledge Grounded Conversation Model 31 2.4 Related Works 36 2.4.1 KGC datasets 36 2.4.2 Soft Selection-based KGC Model 36 2.4.3 Hard Selection-based KGC Model 37 2.4.4 Retrieval-based KGC Models 39 2.4.5 Response Generation with Knowledge Integration 39 2.4.6 Quote Recommendation 42 2.5 Evaluation Methods 44 2.6 Problem Statements 47 3. Knowledge Selection with Sequential Structure of Conversation 48 3.1 Motivation 48 3.2 Reduce-Match Strategy & Match-Reduce Strategy 49 3.2.1 Backbone architecture 51 3.2.2 Reduce-Match Strategy-based Models 52 3.2.3 Match-Reduce Strategy-based Models 56 3.3 Experiments 62 3.3.1 Experimental Setup 62 3.3.2 Experimental Results 70 3.4 Analysis 72 3.4.1 Case Study 72 3.4.2 Impact of Matching Difficulty 75 3.4.3 Impact of Length of Context 77 3.4.4 Impact of Dialogue Act of Message 78 4. Knowledge Ranking with Local Context and Topic Keywords 81 4.1 Motivation 81 4.2 Retrieval-Augmented Knowledge Grounded Conversation Model 85 4.2.1 Base Model 86 4.2.2 Topic-aware Dual Matching for Knowledge Re-ranking 86 4.2.3 Data Weighting Scheme for Retrieval Augmented Generation Models 89 4.3 Experiments 90 4.3.1 Experimental Setup 90 4.3.2 Experimental Results 94 4.4 Analysis 98 4.4.1 Case Study 98 4.4.2 Ablation Study 99 4.4.3 Model Variations 104 4.4.4 Error Analysis 105 5. Application: Quote Recommendation with Knowledge Ranking 110 5.1 Motivation 110 5.2 CAGAR: A Framework for Quote Recommendation 112 5.2.1 Conversation Encoder 114 5.2.2 Quote Encoder 114 5.2.3 Candidate Generator 115 5.2.4 Re-ranker 116 5.2.5 Training and Inference 116 5.3 Experiments 117 5.3.1 Experimental Setup 117 5.3.2 Experimental Results 119 5.4 Analysis 120 5.4.1 Ablation Study 120 5.4.2 Case Study 121 5.4.3 Impact of Length of Context 121 5.4.4 Impact of Training Set Size per Quote 123 6. Conclusion 125 6.1 Contributions and Limitations 126 6.2 Future Works 128 Appendix A. Preliminary Experiments for Quote Recommendations 131 A.1 Methods 131 A.1.1 Matching Granularity Adjustment 131 A.1.2 Random Forest 133 A.1.3 Convolutional Neural Network 133 A.1.4 Recurrent Neural Network 134 A.2 Experiments 135 A.2.1 Baselines and Implementation Details 135 A.2.2 Datasets 136 A.2.3 Results and Discussions 137 ์ดˆ๋ก 162๋ฐ•

    L2 Learnersโ€™ Perceptions and Preferences of Automated Corrective Feedback

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    Form-focused automated Corrective Feedback (CF) is widely used in general-purpose and specialized writing software and research indicates a positive effect for automated CF on second language (L2) learning (AbuSeileek, 2013; AbuSeileek & Abualshaโ€™r, 2014). As any educational practice, preferences and perceptions of learners are expected to influence how automated CF is used by L2 learners (Amrhein & Nassaji, 2010; Brown, 2009; Schulz, 2001). However, preferences and perceptions of automated CF are poorly understood due to paucity of relevant research. This study contributes to this line of investigation by exploring three pertinent topics. First, it explores L2 learnersโ€™ preferences and perceptions of automated CF and four different CF strategies: identification, direct correction, metalinguistic CF, and graduated CF. Second, it examines learnersโ€™ preferences between different CF timings and frequency choices. Third, it explores learnersโ€™ past experience with AWE tools and investigates if past Automated Writing Evaluation (AWE) experience affects learnersโ€™ preferences and perceptions of automated CF. To accomplish these objectives, the present study surveyed and interviewed 30 learners at an intermediate to advanced English as Second Language (ESL) proficiency level. It calculated descriptive statistics of the surveys and employed exploratory factor analysis to identify the underlying relationships between different variables measured by the survey. For interview analysis, it employed a grounded theory approach to identify major concerns and perceptions of automated CF not accounted for in the survey. Results revealed a strong preference for direct correction followed by metalinguistic CF, identification, and graduated CF respectively. Factor analysis identified a close association between clarity and usefulness perceptions and preferences for CF strategies, between comprehensive CF and direct correction and between the frequency of AWE use and identification. The interviews revealed two major concerns with potential influence on preferences for CF strategies: time and learning. Based on preference data, the time required to use CF successfully for error correction is a more important factor for most learners than learning from CF. In other words, CF strategy preferences appear to be mainly shaped by the time factor. This dissertation concludes with specific implications of these findings for developers of AWE tools and L2 educators. Specifically, developers should be mindful of the wide range of concerns that shape L2 learnersโ€™ preferences and perceptions of CF in order to design and deliver CF that is timely, desirable, and positively perceived by L2 learners. Furthermore, L2 educators should exert the effort to mitigate L2 learnersโ€™ concerns that undermine the value of CF qualities and strategies that were empirically proven to be effective for L2 development

    Reformed traditions

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