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Generating monologue and dialogue to present personalised medical information to patients
Generating indicative-informative summaries with SumUM
We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies
A Flexible pragmatics-driven language generator for animated agents
This paper describes the NECA MNLG; a fully implemented Multimodal Natural Language Generation module. The MNLG is deployed as part of the NECA system which generates dialogues between animated agents. The generation module supports the seamless integration of full grammar rules, templates and canned text. The generator takes input which allows for the specification of syntactic, semantic and pragmatic constraints on the output
Automated analysis of Learner\u27s Research Article writing and feedback generation through Machine Learning and Natural Language Processing
Teaching academic writing in English to native and non-native speakers is a challenging task. Quite a variety of computer-aided instruction tools have arisen in the form of Automated Writing Evaluation (AWE) systems to help students in this regard. This thesis describes my contribution towards the implementation of the Research Writing Tutor (RWT), an AWE tool that aids students with academic research writing by analyzing a learner\u27s text at the discourse level. It offers tailored feedback after analysis based on discipline-aware corpora.
At the core of RWT lie two different computational models built using machine learning algorithms to identify the rhetorical structure of a text. RWT extends previous research on a similar AWE tool, the Intelligent Academic Discourse Evaluator (IADE) (Cotos, 2010), designed to analyze articles at the move level of discourse. As a result of the present research, RWT analyzes further at the level of discourse steps, which are the granular communicative functions that constitute a particular move. Based on features extracted from a corpus of expert-annotated research article introductions, the learning algorithm classifies each sentence of a document with a particular rhetorical move and a step. Currently, RWT analyzes the introduction section of a research article, but this work generalizes to handle the other sections of an article, including Methods, Results and Discussion/Conclusion.
This research describes RWT\u27s unique software architecture for analyzing academic writing. This architecture consists of a database schema, a specific choice of classification features, our computational model training procedure, our approach to testing for performance evaluation, and finally the method of applying the models to a learner\u27s writing sample. Experiments were done on the annotated corpus data to study the relation among the features and the rhetorical structure within the documents. Finally, I report the performance measures of our 23 computational models and their capability to identify rhetorical structure on user submitted writing. The final move classifier was trained using a total of 5828 unigrams and 11630 trigrams and performed at a maximum accuracy of 72.65%. Similarly, the step classifier was trained using a total of 27689 unigrams and 27160 trigrams and performed at a maximum accuracy of 72.01%. The revised architecture presented also led to increased speed of both training (a 9x speedup) and real-time performance (a 2x speedup). These performance rates are sufficient for satisfactory usage of RWT in the classroom. The overall goal of RWT is to empower students to write better by helping them consider writing as a series of rhetorical strategies to convey a functional meaning. This research will enable RWT to be deployed broadly into a wider spectrum of classrooms
Empowering cultural heritage professionals with tools for authoring and deploying personalised visitor experiences
This paper presents an authoring environment, which supports cultural heritage professionals in the process of creating and deploying a wide range of different personalised interactive experiences that combine the physical (objects, collection and spaces) and the digital (multimedia content). It is based on a novel flexible formalism that represents the content and the context as independent from one another and allows recombining them in multiple ways thus generating many different interactions from the same elements. The authoring environment was developed in a co-design process with heritage stakeholders and addresses the composition of the content, the definition of the personalisation, and the deployment on a physical configuration of bespoke devices. To simplify the editing while maintaining a powerful representation, the complex creation process is deconstructed into a limited number of elements and phases, including aspects to control personalisation both in content and in interaction. The user interface also includes examples of installations for inspiration and as a means for learning what is possible and how to do it. Throughout the paper, installations in public exhibitions are used to illustrate our points and what our authoring environment can produce. The expressiveness of the formalism and the variety of interactive experiences that could be created was assessed via a range of laboratory tests, while a user-centred evaluation with over 40 cultural heritage professionals assessed whether they feel confident in directly controlling personalisation
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Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : 곡과λν μ κΈ°Β·μ 보곡νλΆ, 2022. 8. κΉλ¨μ.μΈμ΄μ μ€μμ±μ νμ°μ μ΄λ€. κ·Έκ²μ μΈμ΄κ° μμ¬ μν΅μ μλ¨μ΄μ§λ§, λͺ¨λ μ¬λμ΄ μκ°νλ μ΄λ€ κ°λ
μ΄ μλ²½ν λμΌνκ² μ λ¬λ μ μλ κ²μ κΈ°μΈνλ€. μ΄λ νμ°μ μΈ μμμ΄κΈ°λ νμ§λ§, μΈμ΄ μ΄ν΄μμ μ€μμ±μ μ’
μ’
μμ¬ μν΅μ λ¨μ μ΄λ μ€ν¨λ₯Ό κ°μ Έμ€κΈ°λ νλ€.
μΈμ΄μ μ€μμ±μλ λ€μν μΈ΅μκ° μ‘΄μ¬νλ€. νμ§λ§, λͺ¨λ μν©μμ μ€μμ±μ΄ ν΄μλ νμλ μλ€. νμ€ν¬λ§λ€, λλ©μΈλ§λ€ λ€λ₯Έ μμμ μ€μμ±μ΄ μ‘΄μ¬νλ©°, μ΄λ₯Ό μ μ μνκ³ ν΄μλ μ μλ μ€μμ±μμ νμ
ν ν μ€μμ μΈ λΆλΆ κ°μ κ²½κ³λ₯Ό μ μ νλ κ²μ΄ μ€μνλ€.
λ³Έκ³ μμλ μμ± μΈμ΄ μ²λ¦¬, νΉν μλ μ΄ν΄μ μμ΄ μ΄λ€ μμμ μ€μμ±μ΄ λ°μν μ μλμ§ μμλ³΄κ³ , μ΄λ₯Ό ν΄μνκΈ° μν μ°κ΅¬λ₯Ό μ§ννλ€. μ΄λ¬ν νμμ λ€μν μΈμ΄μμ λ°μνμ§λ§, κ·Έ μ λ λ° μμμ μΈμ΄μ λ°λΌμ λ€λ₯΄κ² λνλλ κ²½μ°κ° λ§λ€. μ°λ¦¬μ μ°κ΅¬μμ μ£Όλͺ©νλ λΆλΆμ, μμ± μΈμ΄μ λ΄κΈ΄ μ 보λκ³Ό λ¬Έμ μΈμ΄μ μ 보λ μ°¨μ΄λ‘ μΈν΄ μ€μμ±μ΄ λ°μνλ κ²½μ°λ€μ΄λ€.
λ³Έ μ°κ΅¬λ μ΄μ¨(prosody)μ λ°λΌ λ¬Έμ₯ νμ λ° μλκ° λ€λ₯΄κ² ννλλ κ²½μ°κ° λ§μ νκ΅μ΄λ₯Ό λμμΌλ‘ μ§νλλ€. νκ΅μ΄μμλ λ€μν κΈ°λ₯μ΄ μλ(multi-functionalν) μ’
κ²°μ΄λ―Έ(sentence ender), λΉλ²ν νλ½ νμ(pro-drop), μλ¬Έμ¬ κ°μ(wh-intervention) λ±μΌλ‘ μΈν΄, κ°μ ν
μ€νΈκ° μ¬λ¬ μλλ‘ μ½νλ νμμ΄ λ°μνκ³€ νλ€. μ΄κ²μ΄ μλ μ΄ν΄μ νΌμ μ κ°μ Έμ¬ μ μλ€λ λ°μ μ°©μνμ¬, λ³Έ μ°κ΅¬μμλ μ΄λ¬ν μ€μμ±μ λ¨Όμ μ μνκ³ , μ€μμ μΈ λ¬Έμ₯λ€μ κ°μ§ν μ μλλ‘ λ§λμΉλ₯Ό ꡬμΆνλ€.
μλ μ΄ν΄λ₯Ό μν λ§λμΉλ₯Ό ꡬμΆνλ κ³Όμ μμ λ¬Έμ₯μ μ§ν₯μ±(directivity)κ³Ό μμ¬μ±(rhetoricalness)μ΄ κ³ λ €λλ€. μ΄κ²μ μμ± μΈμ΄μ μλλ₯Ό μμ , μ§λ¬Έ, λͺ
λ Ή, μμ¬μλ¬Έλ¬Έ, κ·Έλ¦¬κ³ μμ¬λͺ
λ Ήλ¬ΈμΌλ‘ ꡬλΆνκ² νλ κΈ°μ€μ΄ λλ€. λ³Έ μ°κ΅¬μμλ κΈ°λ‘λ μμ± μΈμ΄(spoken language)λ₯Ό μΆ©λΆν λμ μΌμΉλ(kappa = 0.85)λ‘ μ£Όμν λ§λμΉλ₯Ό μ΄μ©ν΄, μμ±μ΄ μ£Όμ΄μ§μ§ μμ μν©μμ μ€μμ μΈ ν
μ€νΈλ₯Ό κ°μ§νλ λ°μ μ΄λ€ μ λ΅ νΉμ μΈμ΄ λͺ¨λΈμ΄ ν¨κ³Όμ μΈκ°λ₯Ό 보μ΄κ³ , ν΄λΉ νμ€ν¬μ νΉμ§μ μ μ±μ μΌλ‘ λΆμνλ€.
λν, μ°λ¦¬λ ν
μ€νΈ μΈ΅μμμλ§ μ€μμ±μ μ κ·Όνμ§ μκ³ , μ€μ λ‘ μμ±μ΄ μ£Όμ΄μ§ μν©μμ μ€μμ± ν΄μ(disambiguation)κ° κ°λ₯νμ§λ₯Ό μμ보기 μν΄, ν
μ€νΈκ° μ€μμ μΈ λ°νλ€λ§μΌλ‘ ꡬμ±λ μΈκ³΅μ μΈ μμ± λ§λμΉλ₯Ό μ€κ³νκ³ λ€μν μ§μ€(attention) κΈ°λ° μ κ²½λ§(neural network) λͺ¨λΈλ€μ μ΄μ©ν΄ μ€μμ±μ ν΄μνλ€. μ΄ κ³Όμ μμ λͺ¨λΈ κΈ°λ° ν΅μ¬μ /μλ―Έμ μ€μμ± ν΄μκ° μ΄λ ν κ²½μ°μ κ°μ₯ ν¨κ³Όμ μΈμ§ κ΄μ°°νκ³ , μΈκ°μ μΈμ΄ μ²λ¦¬μ μ΄λ€ μ°κ΄μ΄ μλμ§μ λν κ΄μ μ μ μνλ€.
λ³Έ μ°κ΅¬μμλ λ§μ§λ§μΌλ‘, μμ κ°μ μ μ°¨λ‘ μλ μ΄ν΄ κ³Όμ μμμ μ€μμ±μ΄ ν΄μλμμ κ²½μ°, μ΄λ₯Ό μ΄λ»κ² μ°μ
κ³ νΉμ μ°κ΅¬ λ¨μμ νμ©ν μ μλκ°μ λν κ°λ΅ν λ‘λ맡μ μ μνλ€. ν
μ€νΈμ κΈ°λ°ν μ€μμ± νμ
κ³Ό μμ± κΈ°λ°μ μλ μ΄ν΄ λͺ¨λμ ν΅ν©νλ€λ©΄, μ€λ₯μ μ νλ₯Ό μ€μ΄λ©΄μλ ν¨μ¨μ μΌλ‘ μ€μμ±μ λ€λ£° μ μλ μμ€ν
μ λ§λ€ μ μμ κ²μ΄λ€. μ΄λ¬ν μμ€ν
μ λν 맀λμ (dialogue manager)μ ν΅ν©λμ΄ κ°λ¨ν λν(chit-chat)κ° κ°λ₯ν λͺ©μ μ§ν₯ λν μμ€ν
(task-oriented dialogue system)μ ꡬμΆν μλ μκ³ , λ¨μΌ μΈμ΄ 쑰건(monolingual condition)μ λμ΄ μμ± λ²μμμμ μλ¬λ₯Ό μ€μ΄λ λ°μ νμ©λ μλ μλ€.
μ°λ¦¬λ λ³Έκ³ λ₯Ό ν΅ν΄, μ΄μ¨μ λ―Όκ°ν(prosody-sensitive) μΈμ΄μμ μλ μ΄ν΄λ₯Ό μν μ€μμ± ν΄μκ° κ°λ₯νλ©°, μ΄λ₯Ό μ°μ
λ° μ°κ΅¬ λ¨μμ νμ©ν μ μμμ 보μ΄κ³ μ νλ€. λ³Έ μ°κ΅¬κ° λ€λ₯Έ μΈμ΄ λ° λλ©μΈμμλ κ³ μ§μ μΈ μ€μμ± λ¬Έμ λ₯Ό ν΄μνλ λ°μ λμμ΄ λκΈΈ λ°λΌλ©°, μ΄λ₯Ό μν΄ μ°κ΅¬λ₯Ό μ§ννλ λ°μ νμ©λ 리μμ€, κ²°κ³Όλ¬Ό λ° μ½λλ€μ 곡μ ν¨μΌλ‘μ¨ νκ³μ λ°μ μ μ΄λ°μ§νκ³ μ νλ€.Ambiguity in the language is inevitable. It is because, albeit language is a means of communication, a particular concept that everyone thinks of cannot be conveyed in a perfectly identical manner. As this is an inevitable factor, ambiguity in language understanding often leads to breakdown or failure of communication.
There are various hierarchies of language ambiguity. However, not all ambiguity needs to be resolved. Different aspects of ambiguity exist for each domain and task, and it is crucial to define the boundary after recognizing the ambiguity that can be well-defined and resolved.
In this dissertation, we investigate the types of ambiguity that appear in spoken language processing, especially in intention understanding, and conduct research to define and resolve it. Although this phenomenon occurs in various languages, its degree and aspect depend on the language investigated. The factor we focus on is cases where the ambiguity comes from the gap between the amount of information in the spoken language and the text.
Here, we study the Korean language, which often shows different sentence structures and intentions depending on the prosody. In the Korean language, a text is often read with multiple intentions due to multi-functional sentence enders, frequent pro-drop, wh-intervention, etc. We first define this type of ambiguity and construct a corpus that helps detect ambiguous sentences, given that such utterances can be problematic for intention understanding.
In constructing a corpus for intention understanding, we consider the directivity and rhetoricalness of a sentence. They make up a criterion for classifying the intention of spoken language into a statement, question, command, rhetorical question, and rhetorical command. Using the corpus annotated with sufficiently high agreement on a spoken language corpus, we show that colloquial corpus-based language models are effective in classifying ambiguous text given only textual data, and qualitatively analyze the characteristics of the task.
We do not handle ambiguity only at the text level. To find out whether actual disambiguation is possible given a speech input, we design an artificial spoken language corpus composed only of ambiguous sentences, and resolve ambiguity with various attention-based neural network architectures. In this process, we observe that the ambiguity resolution is most effective when both textual and acoustic input co-attends each feature, especially when the audio processing module conveys attention information to the text module in a multi-hop manner.
Finally, assuming the case that the ambiguity of intention understanding is resolved by proposed strategies, we present a brief roadmap of how the results can be utilized at the industry or research level. By integrating text-based ambiguity detection and speech-based intention understanding module, we can build a system that handles ambiguity efficiently while reducing error propagation. Such a system can be integrated with dialogue managers to make up a task-oriented dialogue system capable of chit-chat, or it can be used for error reduction in multilingual circumstances such as speech translation, beyond merely monolingual conditions.
Throughout the dissertation, we want to show that ambiguity resolution for intention understanding in prosody-sensitive language can be achieved and can be utilized at the industry or research level. We hope that this study helps tackle chronic ambiguity issues in other languages ββor other domains, linking linguistic science and engineering approaches.1 Introduction 1
1.1 Motivation 2
1.2 Research Goal 4
1.3 Outline of the Dissertation 5
2 Related Work 6
2.1 Spoken Language Understanding 6
2.2 Speech Act and Intention 8
2.2.1 Performatives and statements 8
2.2.2 Illocutionary act and speech act 9
2.2.3 Formal semantic approaches 11
2.3 Ambiguity of Intention Understanding in Korean 14
2.3.1 Ambiguities in language 14
2.3.2 Speech act and intention understanding in Korean 16
3 Ambiguity in Intention Understanding of Spoken Language 20
3.1 Intention Understanding and Ambiguity 20
3.2 Annotation Protocol 23
3.2.1 Fragments 24
3.2.2 Clear-cut cases 26
3.2.3 Intonation-dependent utterances 28
3.3 Data Construction . 32
3.3.1 Source scripts 32
3.3.2 Agreement 32
3.3.3 Augmentation 33
3.3.4 Train split 33
3.4 Experiments and Results 34
3.4.1 Models 34
3.4.2 Implementation 36
3.4.3 Results 37
3.5 Findings and Summary 44
3.5.1 Findings 44
3.5.2 Summary 45
4 Disambiguation of Speech Intention 47
4.1 Ambiguity Resolution 47
4.1.1 Prosody and syntax 48
4.1.2 Disambiguation with prosody 50
4.1.3 Approaches in SLU 50
4.2 Dataset Construction 51
4.2.1 Script generation 52
4.2.2 Label tagging 54
4.2.3 Recording 56
4.3 Experiments and Results 57
4.3.1 Models 57
4.3.2 Results 60
4.4 Summary 63
5 System Integration and Application 65
5.1 System Integration for Intention Identification 65
5.1.1 Proof of concept 65
5.1.2 Preliminary study 69
5.2 Application to Spoken Dialogue System 75
5.2.1 What is 'Free-running' 76
5.2.2 Omakase chatbot 76
5.3 Beyond Monolingual Approaches 84
5.3.1 Spoken language translation 85
5.3.2 Dataset 87
5.3.3 Analysis 94
5.3.4 Discussion 95
5.4 Summary 100
6 Conclusion and Future Work 103
Bibliography 105
Abstract (In Korean) 124
Acknowledgment 126λ°
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