Quote Recommendation for Dialogs and Writings

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

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, “the dialogical and mediated inquiry” (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

지식 기반 대화에서의 대화 특성을 활용한 지식 선택 및 랭킹 방법

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·컴퓨터공학부, 2022. 8. 이상구.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.지식 기반 대화 모델은 대화 기록과 외부 지식 이 두 가지 모두에 관련된 응답을 생성하는 것을 목표로 한다. 지식 기반 대화 모델의 가장 중요한 부분 중 하나는 응답의 기반을 제공하는 지식을 찾는 것이다. 지식 기반 모델이 주어진 문맥에 부적합한 지식을 찾는 경우 관련성이 떨어지거나 지식이 부족한 응답이 생성될 수 있다. 이 문제를 해결하기 위해 이 논문에서는 지식 기반 대화를 위해 대화 여러 특성을 활용하여 지식을 선정하는 지식 선택 모델과 지식 순위 모델을 제시한다. 구체적으로 본 논문에서는 다중 턴 대화에서의 순차적 구조 또는 응답 이전 문맥과 대화의 주제를 활용하는 새로운 두 가지 방법을 제시한다. 첫 번째 방법으로써 본 논문은 두 가지 지식 선택 전략을 제안한다. 제안하는 전략 중 하나는 지식과 대화 턴 간의 순차적 매칭 특징을 보존하는 방법이고 다른 전략은 대화의 순차적 특성을 인코딩하여 지식을 선택하는 방법이다. 두 번째로 본 논문은 대화의 주제 키워드와 응답 바로 이전의 문맥을 모두 활용하여 적절한 범위의 관련 문서들로 검색 결과를 구성하는 새로운 지식 순위 모델을 제안한다. 마지막으로 지식 순위 모델의 적응성 검증을 위해 정답 인용구와 의미적으로 유사하지만 정답은 아닌 인용구의 집합을 인용구 순위 모델에 제공하는 인용구 추천 프레임워크를 제안한다. 제안된 지식 선택 및 순위 모델을 기반으로 하는 지식 기반 대화 모델이 경쟁 모델보다 외부 지식 및 대화 문맥과의 관련성 측면에서 우수하다는 것을 사람 간의 대화 데이터를 이용한 다수의 실험을 통해 검증하였다.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 초록 162박

SFL in L2 Writing Teacher Education: A Case Study of an EFL Pre-service Teacher in Conceptualizing Grammar

Abstract SFL in L2 Writing Teacher Education: A Case STUDY of an EFL PRE-SERVICE Teacher IN CONCEPTUALIZING GRAMMAR English education globally has been challenged by an increasing need for academic English practices to support access to content area knowledge and scholarly exchanges. However, EFL (English as a Foreign Language) teachers often lack the linguistic and pedagogical knowledge of how academic texts work to construct meanings in specific disciplines and how to design effective academic literacy instruction accessible to all students. This study, therefore, is aimed at responding to the intensifying demand for academic literacy instruction in international contexts by investigating an EFL teacher’s participation in MATESOL program in North America informed in part by Halliday’s SFL (Systemic Functional Linguistics) and Martin’s genre theory. The study focuses on exploring how this teacher’s conceptions of grammar shifted, if at all, over the courses in the teacher education program and how the teacher’s classroom practice during the first year in her career reflect, if at all, the perspective of language learning. This study is informed by two main conceptions as the theoretical frameworks. First, Halliday and Martin’s social conception of language and language learning serves as the theoretical basis informing the pedagogical knowledge that the teacher develops. Second, a sociocultural approach to teachers’ knowledge development serves as a framework to understand how the teacher conceptualizes a more functional conception of language and language learning for academic literacy instruction in a sustained process of teacher learning with respect to the teacher’s whole lived experiences. This study uses an ethnographic method of data collection and analysis. The data were collected from multiple sources including field notes, instructional materials, audio tapes, email exchanges, interviews, textbooks, and course assignments. Data collection focused on documenting this teacher in conceptualizing of grammar over her participation in the teacher education program and over one year of teaching experience upon completion of her MATESOL program. In analyzing these data, this study involved coding and categorizing processes to generate patterns of themes with reference to the research questions. This study is expected to contribute to an effort of preparing teachers with the expertise of teaching academic literacy and development in international contexts by considering how local contexts shape their pedagogical knowledge development

L2 Learners’ Perceptions and Preferences of Automated Corrective Feedback

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 & Abualsha’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 learners’ preferences and perceptions of automated CF and four different CF strategies: identification, direct correction, metalinguistic CF, and graduated CF. Second, it examines learners’ preferences between different CF timings and frequency choices. Third, it explores learners’ past experience with AWE tools and investigates if past Automated Writing Evaluation (AWE) experience affects learners’ 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 learners’ 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 learners’ concerns that undermine the value of CF qualities and strategies that were empirically proven to be effective for L2 development

Reformed traditions

Postprin