Universal and language-specific processing : the case of prosody

A key question in the science of language is how speech processing can be influenced by both language-universal and language-specific mechanisms (Cutler, Klein, & Levinson, 2005). My graduate research aimed to address this question by adopting a crosslanguage approach to compare languages with different phonological systems. Of all components of linguistic structure, prosody is often considered to be one of the most language-specific dimensions of speech. This can have significant implications for our understanding of language use, because much of speech processing is specifically tailored to the structure and requirements of the native language. However, it is still unclear whether prosody may also play a universal role across languages, and very little comparative attempts have been made to explore this possibility. In this thesis, I examined both the production and perception of prosodic cues to prominence and phrasing in native speakers of English and Mandarin Chinese. In focus production, our research revealed that English and Mandarin speakers were alike in how they used prosody to encode prominence, but there were also systematic language-specific differences in the exact degree to which they enhanced the different prosodic cues (Chapter 2). This, however, was not the case in focus perception, where English and Mandarin listeners were alike in the degree to which they used prosody to predict upcoming prominence, even though the precise cues in the preceding prosody could differ (Chapter 3). Further experiments examining prosodic focus prediction in the speech of different talkers have demonstrated functional cue equivalence in prosodic focus detection (Chapter 4). Likewise, our experiments have also revealed both crosslanguage similarities and differences in the production and perception of juncture cues (Chapter 5). Overall, prosodic processing is the result of a complex but subtle interplay of universal and language-specific structure

Subtitling Humour from the Perspective of Relevance Theory: The Office in Traditional Chinese

Subtitling the scenes containing humorous utterances in cinematic-televisual productions encounters a myriad of challenges, because the subtitler has to face the technical constraints that characterise the professional subtitling environment and the cultural barriers when reproducing humorous utterances for viewers inhabiting another culture. Past studies tend to explore more limited humour-related areas, which means that a more comprehensive picture of this specialised field is missing. The current research investigates the subtitling of humour, drawing on the framework of relevance theory and the British sitcom The Office, translated from English dialogue into Traditional Chinese subtitles. This research enquires into whether or not relevance theory can explain the subtitling strategies activated to deal with various humorous utterances in the sitcom, and, if so, to what extent. The English-Chinese Corpus of The Office (ECCO), which contains sample texts, media files and annotations, has been constructed to perform an empirical study. To enrich the corpus with valuable annotations, a typology of humour has been developed based on the concept of frame, and a taxonomy of subtitling strategies has also been proposed. The quantitative analysis demonstrates that the principle of relevance is the main benchmark for the choice of a subtitling micro-strategy within any given macro-strategy. With the chi-square test, it further proves the existence of a statistically significant association between humour types/frames and subtitling strategies at the global level. The qualitative analysis shows that the principle of relevance can operate in a subtle way, in which the subtitler invests more cognitive efforts to enhance the acceptability of subtitles. It also develops three levels of mutual dependency between the two variables, from strong, weak to null, to classify different examples. Overall, this study improves our understanding of humour translation and can facilitate a change in the curricula of translator training

음성언어 이해에서의 중의성 해소

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022. 8. 김남수.언어의 중의성은 필연적이다. 그것은 언어가 의사 소통의 수단이지만, 모든 사람이 생각하는 어떤 개념이 완벽히 동일하게 전달될 수 없는 것에 기인한다. 이는 필연적인 요소이기도 하지만, 언어 이해에서 중의성은 종종 의사 소통의 단절이나 실패를 가져오기도 한다. 언어의 중의성에는 다양한 층위가 존재한다. 하지만, 모든 상황에서 중의성이 해소될 필요는 없다. 태스크마다, 도메인마다 다른 양상의 중의성이 존재하며, 이를 잘 정의하고 해소될 수 있는 중의성임을 파악한 후 중의적인 부분 간의 경계를 잘 정하는 것이 중요하다. 본고에서는 음성 언어 처리, 특히 의도 이해에 있어 어떤 양상의 중의성이 발생할 수 있는지 알아보고, 이를 해소하기 위한 연구를 진행한다. 이러한 현상은 다양한 언어에서 발생하지만, 그 정도 및 양상은 언어에 따라서 다르게 나타나는 경우가 많다. 우리의 연구에서 주목하는 부분은, 음성 언어에 담긴 정보량과 문자 언어의 정보량 차이로 인해 중의성이 발생하는 경우들이다. 본 연구는 운율(prosody)에 따라 문장 형식 및 의도가 다르게 표현되는 경우가 많은 한국어를 대상으로 진행된다. 한국어에서는 다양한 기능이 있는(multi-functional한) 종결어미(sentence ender), 빈번한 탈락 현상(pro-drop), 의문사 간섭(wh-intervention) 등으로 인해, 같은 텍스트가 여러 의도로 읽히는 현상이 발생하곤 한다. 이것이 의도 이해에 혼선을 가져올 수 있다는 데에 착안하여, 본 연구에서는 이러한 중의성을 먼저 정의하고, 중의적인 문장들을 감지할 수 있도록 말뭉치를 구축한다. 의도 이해를 위한 말뭉치를 구축하는 과정에서 문장의 지향성(directivity)과 수사성(rhetoricalness)이 고려된다. 이것은 음성 언어의 의도를 서술, 질문, 명령, 수사의문문, 그리고 수사명령문으로 구분하게 하는 기준이 된다. 본 연구에서는 기록된 음성 언어(spoken language)를 충분히 높은 일치도(kappa = 0.85)로 주석한 말뭉치를 이용해, 음성이 주어지지 않은 상황에서 중의적인 텍스트를 감지하는 데에 어떤 전략 혹은 언어 모델이 효과적인가를 보이고, 해당 태스크의 특징을 정성적으로 분석한다. 또한, 우리는 텍스트 층위에서만 중의성에 접근하지 않고, 실제로 음성이 주어진 상황에서 중의성 해소(disambiguation)가 가능한지를 알아보기 위해, 텍스트가 중의적인 발화들만으로 구성된 인공적인 음성 말뭉치를 설계하고 다양한 집중(attention) 기반 신경망(neural network) 모델들을 이용해 중의성을 해소한다. 이 과정에서 모델 기반 통사적/의미적 중의성 해소가 어떠한 경우에 가장 효과적인지 관찰하고, 인간의 언어 처리와 어떤 연관이 있는지에 대한 관점을 제시한다. 본 연구에서는 마지막으로, 위와 같은 절차로 의도 이해 과정에서의 중의성이 해소되었을 경우, 이를 어떻게 산업계 혹은 연구 단에서 활용할 수 있는가에 대한 간략한 로드맵을 제시한다. 텍스트에 기반한 중의성 파악과 음성 기반의 의도 이해 모듈을 통합한다면, 오류의 전파를 줄이면서도 효율적으로 중의성을 다룰 수 있는 시스템을 만들 수 있을 것이다. 이러한 시스템은 대화 매니저(dialogue manager)와 통합되어 간단한 대화(chit-chat)가 가능한 목적 지향 대화 시스템(task-oriented dialogue system)을 구축할 수도 있고, 단일 언어 조건(monolingual condition)을 넘어 음성 번역에서의 에러를 줄이는 데에 활용될 수도 있다. 우리는 본고를 통해, 운율에 민감한(prosody-sensitive) 언어에서 의도 이해를 위한 중의성 해소가 가능하며, 이를 산업 및 연구 단에서 활용할 수 있음을 보이고자 한다. 본 연구가 다른 언어 및 도메인에서도 고질적인 중의성 문제를 해소하는 데에 도움이 되길 바라며, 이를 위해 연구를 진행하는 데에 활용된 리소스, 결과물 및 코드들을 공유함으로써 학계의 발전에 이바지하고자 한다.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 ​​or 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 126박