Towards structured neural spoken dialogue modelling.

195 p.In this thesis, we try to alleviate some of the weaknesses of the current approaches to dialogue modelling,one of the most challenging areas of Artificial Intelligence. We target three different types of dialogues(open-domain, task-oriented and coaching sessions), and use mainly machine learning algorithms to traindialogue models. One challenge of open-domain chatbots is their lack of response variety, which can betackled using Generative Adversarial Networks (GANs). We present two methodological contributions inthis regard. On the one hand, we develop a method to circumvent the non-differentiability of textprocessingGANs. On the other hand, we extend the conventional task of discriminators, which oftenoperate at a single response level, to the batch level. Meanwhile, two crucial aspects of task-orientedsystems are their understanding capabilities because they need to correctly interpret what the user islooking for and their constraints), and the dialogue strategy. We propose a simple yet powerful way toimprove spoken understanding and adapt the dialogue strategy by explicitly processing the user's speechsignal through audio-processing transformer neural networks. Finally, coaching dialogues shareproperties of open-domain and task-oriented dialogues. They are somehow task-oriented but, there is norush to complete the task, and it is more important to calmly converse to make the users aware of theirown problems. In this context, we describe our collaboration in the EMPATHIC project, where a VirtualCoach capable of carrying out coaching dialogues about nutrition was built, using a modular SpokenDialogue System. Second, we model such dialogues with an end-to-end system based on TransferLearning

‘Did the speaker change?’: Temporal tracking for overlapping speaker segmentation in multi-speaker scenarios

Diarization systems are an essential part of many speech processing applications, such as speaker indexing, improving automatic speech recognition (ASR) performance and making single speaker-based algorithms available for use in multi-speaker domains. This thesis will focus on the first task of the diarization process, that being the task of speaker segmentation which can be thought of as trying to answer the question ‘Did the speaker change?’ in an audio recording. This thesis starts by showing that time-varying pitch properties can be used advantageously within the segmentation step of a multi-talker diarization system. It is then highlighted that an individual’s pitch is smoothly varying and, therefore, can be predicted by means of a Kalman filter. Subsequently, it is shown that if the pitch is not predictable, then this is most likely due to a change in the speaker. Finally, a novel system is proposed that uses this approach of pitch prediction for speaker change detection. This thesis then goes on to demonstrate how voiced harmonics can be useful in detecting when more than one speaker is talking, such as during overlapping speaker activity. A novel system is proposed to track multiple harmonics simultaneously, allowing for the determination of onsets and end-points of a speaker’s utterance in the presence of an additional active speaker. This thesis then extends this work to explore the use of a new multimodal approach for overlapping speaker segmentation that tracks both the fundamental frequency (F0) and direction of arrival (DoA) of each speaker simultaneously. The proposed multiple hypothesis tracking system, which simultaneously tracks both features, shows an improvement in segmentation performance when compared to tracking these features separately. Lastly, this thesis focuses on the DoA estimation part of the newly proposed multimodal approach. It does this by exploring a polynomial extension to the multiple signal classification (MUSIC) algorithm, spatio-spectral polynomial (SSP)-MUSIC, and evaluating its performance when using speech sound sources.Open Acces

Application of Machine Learning within Visual Content Production

We are living in an era where digital content is being produced at a dazzling pace. The heterogeneity of contents and contexts is so varied that a numerous amount of applications have been created to respond to people and market demands. The visual content production pipeline is the generalisation of the process that allows a content editor to create and evaluate their product, such as a video, an image, a 3D model, etc. Such data is then displayed on one or more devices such as TVs, PC monitors, virtual reality head-mounted displays, tablets, mobiles, or even smartwatches. Content creation can be simple as clicking a button to film a video and then share it into a social network, or complex as managing a dense user interface full of parameters by using keyboard and mouse to generate a realistic 3D model for a VR game. In this second example, such sophistication results in a steep learning curve for beginner-level users. In contrast, expert users regularly need to refine their skills via expensive lessons, time-consuming tutorials, or experience. Thus, user interaction plays an essential role in the diffusion of content creation software, primarily when it is targeted to untrained people. In particular, with the fast spread of virtual reality devices into the consumer market, new opportunities for designing reliable and intuitive interfaces have been created. Such new interactions need to take a step beyond the point and click interaction typical of the 2D desktop environment. The interactions need to be smart, intuitive and reliable, to interpret 3D gestures and therefore, more accurate algorithms are needed to recognise patterns. In recent years, machine learning and in particular deep learning have achieved outstanding results in many branches of computer science, such as computer graphics and human-computer interface, outperforming algorithms that were considered state of the art, however, there are only fleeting efforts to translate this into virtual reality. In this thesis, we seek to apply and take advantage of deep learning models to two different content production pipeline areas embracing the following subjects of interest: advanced methods for user interaction and visual quality assessment. First, we focus on 3D sketching to retrieve models from an extensive database of complex geometries and textures, while the user is immersed in a virtual environment. We explore both 2D and 3D strokes as tools for model retrieval in VR. Therefore, we implement a novel system for improving accuracy in searching for a 3D model. We contribute an efficient method to describe models through 3D sketch via an iterative descriptor generation, focusing both on accuracy and user experience. To evaluate it, we design a user study to compare different interactions for sketch generation. Second, we explore the combination of sketch input and vocal description to correct and fine-tune the search for 3D models in a database containing fine-grained variation. We analyse sketch and speech queries, identifying a way to incorporate both of them into our system's interaction loop. Third, in the context of the visual content production pipeline, we present a detailed study of visual metrics. We propose a novel method for detecting rendering-based artefacts in images. It exploits analogous deep learning algorithms used when extracting features from sketches

Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network development. The changes were to include evaluation tools that can be applied to neural networks at each phase of the software engineering life cycle. The result was a formal evaluation approach to increase the product quality of systems that use neural networks for their implementation

Speech technologies for the audiovisual and multimedia interaction environments

361 p

Data-efficient methods for dialogue systems

Conversational User Interface (CUI) has become ubiquitous in everyday life, in consumer-focused products like Siri and Alexa or more business-oriented customer support automation solutions. Deep learning underlies many recent breakthroughs in dialogue systems but requires very large amounts of training data, often annotated by experts — and this dramatically increases the cost of deploying such systems in production setups and reduces their flexibility as software products. Trained with smaller data, these methods end up severely lacking robustness to various phenomena of spoken language (e.g. disfluencies), out-of-domain input, and often just have too little generalisation power to other tasks and domains. In this thesis, we address the above issues by introducing a series of methods for bootstrapping robust dialogue systems from minimal data. Firstly, we study two orthogonal approaches to dialogue: a linguistically informed model (DyLan) and a machine learning-based one (MemN2N) — from the data efficiency perspective, i.e. their potential to generalise from minimal data and robustness to natural spontaneous input. We outline the steps to obtain data-efficient solutions with either approach and proceed with the neural models for the rest of the thesis. We then introduce the core contributions of this thesis, two data-efficient models for dialogue response generation: the Dialogue Knowledge Transfer Network (DiKTNet) based on transferable latent dialogue representations, and the Generative-Retrieval Transformer (GRTr) combining response generation logic with a retrieval mechanism as the fallback. GRTr ranked first at the Dialog System Technology Challenge 8 Fast Domain Adaptation task. Next, we the problem of training robust neural models from minimal data. As such, we look at robustness to disfluencies and propose a multitask LSTM-based model for domain-general disfluency detection. We then go on to explore robustness to anomalous, or out-of-domain (OOD) input. We address this problem by (1) presenting Turn Dropout, a data-augmentation technique facilitating training for anomalous input only using in-domain data, and (2) introducing VHCN and AE-HCN, autoencoder-augmented models for efficient training with turn dropout based on the Hybrid Code Networks (HCN) model family. With all the above work addressing goal-oriented dialogue, our final contribution in this thesis focuses on social dialogue where the main objective is maintaining natural, coherent, and engaging conversation for as long as possible. We introduce a neural model for response ranking in social conversation used in Alana, the 3rd place winner in the Amazon Alexa Prize 2017 and 2018. For our model, we employ a novel technique of predicting the dialogue length as the main objective for ranking. We show that this approach matches the performance of its counterpart based on the conventional, human rating-based objective — and surpasses it given more raw dialogue transcripts, thus reducing the dependence on costly and cumbersome dialogue annotations.EPSRC project BABBLE (grant EP/M01553X/1)

딥 뉴럴 네트워크 기반의 문장 인코더를 이용한 문장 간 관계 모델링

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 이상구.문장 매칭이란 두 문장 간 의미적으로 일치하는 정도를 예측하는 문제이다. 어떤 모델이 두 문장 사이의 관계를 효과적으로 밝혀내기 위해서는 높은 수준의 자연어 텍스트 이해 능력이 필요하기 때문에, 문장 매칭은 다양한 자연어 처리 응용의 성능에 중요한 영향을 미친다. 본 학위 논문에서는 문장 인코더, 매칭 함수, 준지도 학습이라는 세 가지 측면에서 문장 매칭의 성능 개선을 모색한다. 문장 인코더란 문장으로부터 유용한 특질들을 추출하는 역할을 하는 구성 요소로, 본 논문에서는 문장 인코더의 성능 향상을 위하여 Gumbel Tree-LSTM과 Cell-aware Stacked LSTM이라는 두 개의 새로운 아키텍처를 제안한다. Gumbel Tree-LSTM은 재귀적 뉴럴 네트워크(recursive neural network) 구조에 기반한 아키텍처이다. 구조 정보가 포함된 데이터를 입력으로 사용하던 기존의 재귀적 뉴럴 네트워크 모델과 달리, Gumbel Tree-LSTM은 구조가 없는 데이터로부터 특정 문제에 대한 성능을 최대화하는 파싱 전략을 학습한다. Cell-aware Stacked LSTM은 LSTM 구조를 개선한 아키텍처로, 여러 LSTM 레이어를 중첩하여 사용할 때 망각 게이트(forget gate)를 추가적으로 도입하여 수직 방향의 정보 흐름을 더 효율적으로 제어할 수 있도록 한다. 한편, 새로운 매칭 함수로서 우리는 요소별 쌍선형 문장 매칭(element-wise bilinear sentence matching, ElBiS) 함수를 제안한다. ElBiS 알고리즘은 특정 문제를 해결하는 데에 적합한 방식으로 두 문장 표현을 하나의 벡터로 합치는 방법을 자동으로 찾는 것을 목적으로 한다. 문장 표현을 얻을 때에 서로 같은 문장 인코더를 사용한다는 사실로부터 우리는 벡터의 각 요소 간 쌍선형(bilinear) 상호 작용만을 고려하여도 두 문장 벡터 간 비교를 충분히 잘 수행할 수 있다는 가설을 수립하고 이를 실험적으로 검증한다. 상호 작용의 범위를 제한함으로써, 자동으로 유용한 병합 방법을 찾는다는 이점을 유지하면서 모든 상호 작용을 고려하는 쌍선형 풀링 방법에 비해 필요한 파라미터의 수를 크게 줄일 수 있다. 마지막으로, 학습 시 레이블이 있는 데이터와 레이블이 없는 데이터를 함께 사용하는 준지도 학습을 위해 우리는 교차 문장 잠재 변수 모델(cross-sentence latent variable model, CS-LVM)을 제안한다. CS-LVM의 생성 모델은 출처 문장(source sentence)의 잠재 표현 및 출처 문장과 목표 문장(target sentence) 간의 관계를 나타내는 변수로부터 목표 문장이 생성된다고 가정한다. CS-LVM에서는 두 문장이 하나의 모델 안에서 모두 고려되기 때문에, 학습에 사용되는 목적 함수가 더 자연스럽게 정의된다. 또한, 우리는 생성 모델의 파라미터가 더 의미적으로 적합한 문장을 생성하도록 유도하기 위하여 일련의 의미 제약들을 정의한다. 본 학위 논문에서 제안된 개선 방안들은 문장 매칭 과정을 포함하는 다양한 자연어 처리 응용의 효용성을 높일 것으로 기대된다.Sentence matching is a task of predicting the degree of match between two sentences. Since high level of understanding natural language text is needed for a model to identify the relationship between two sentences, it is an important component for various natural language processing applications. In this dissertation, we seek for the improvement of the sentence matching module from the following three ingredients: sentence encoder, matching function, and semi-supervised learning. To enhance a sentence encoder network which takes responsibility of extracting useful features from a sentence, we propose two new sentence encoder architectures: Gumbel Tree-LSTM and Cell-aware Stacked LSTM (CAS-LSTM). Gumbel Tree-LSTM is based on a recursive neural network (RvNN) architecture, however unlike typical RvNN architectures it does not need a structured input. Instead, it learns from data a parsing strategy that is optimized for a specific task. The latter, CAS-LSTM, extends the stacked long short-term memory (LSTM) architecture by introducing an additional forget gate for better handling of vertical information flow. And then, as a new matching function, we present the element-wise bilinear sentence matching (ElBiS) function. It aims to automatically find an aggregation scheme that fuses two sentence representations into a single one suitable for a specific task. From the fact that a sentence encoder is shared across inputs, we hypothesize and empirically prove that considering only the element-wise bilinear interaction is sufficient for comparing two sentence vectors. By restricting the interaction, we can largely reduce the number of required parameters compared with full bilinear pooling methods without losing the advantage of automatically discovering useful aggregation schemes. Finally, to facilitate semi-supervised training, i.e. to make use of both labeled and unlabeled data in training, we propose the cross-sentence latent variable model (CS-LVM). Its generative model assumes that a target sentence is generated from the latent representation of a source sentence and the variable indicating the relationship between the source and the target sentence. As it considers the two sentences in a pair together in a single model, the training objectives are defined more naturally than prior approaches based on the variational auto-encoder (VAE). We also define semantic constraints that force the generator to generate semantically more plausible sentences. We believe that the improvements proposed in this dissertation would advance the effectiveness of various natural language processing applications containing modeling sentence pairs.Chapter 1 Introduction 1 1.1 Sentence Matching 1 1.2 Deep Neural Networks for Sentence Matching 2 1.3 Scope of the Dissertation 4 Chapter 2 Background and Related Work 9 2.1 Sentence Encoders 9 2.2 Matching Functions 11 2.3 Semi-Supervised Training 13 Chapter 3 Sentence Encoder: Gumbel Tree-LSTM 15 3.1 Motivation 15 3.2 Preliminaries 16 3.2.1 Recursive Neural Networks 16 3.2.2 Training RvNNs without Tree Information 17 3.3 Model Description 19 3.3.1 Tree-LSTM 19 3.3.2 Gumbel-Softmax 20 3.3.3 Gumbel Tree-LSTM 22 3.4 Implementation Details 25 3.5 Experiments 27 3.5.1 Natural Language Inference 27 3.5.2 Sentiment Analysis 32 3.5.3 Qualitative Analysis 33 3.6 Summary 36 Chapter 4 Sentence Encoder: Cell-aware Stacked LSTM 38 4.1 Motivation 38 4.2 Related Work 40 4.3 Model Description 43 4.3.1 Stacked LSTMs 43 4.3.2 Cell-aware Stacked LSTMs 44 4.3.3 Sentence Encoders 46 4.4 Experiments 47 4.4.1 Natural Language Inference 47 4.4.2 Paraphrase Identification 50 4.4.3 Sentiment Classification 52 4.4.4 Machine Translation 53 4.4.5 Forget Gate Analysis 55 4.4.6 Model Variations 56 4.5 Summary 59 Chapter 5 Matching Function: Element-wise Bilinear Sentence Matching 60 5.1 Motivation 60 5.2 Proposed Method: ElBiS 61 5.3 Experiments 63 5.3.1 Natural language inference 64 5.3.2 Paraphrase Identification 66 5.4 Summary and Discussion 68 Chapter 6 Semi-Supervised Training: Cross-Sentence Latent Variable Model 70 6.1 Motivation 70 6.2 Preliminaries 71 6.2.1 Variational Auto-Encoders 71 6.2.2 von Mises–Fisher Distribution 73 6.3 Proposed Framework: CS-LVM 74 6.3.1 Cross-Sentence Latent Variable Model 75 6.3.2 Architecture 78 6.3.3 Optimization 79 6.4 Experiments 84 6.4.1 Natural Language Inference 84 6.4.2 Paraphrase Identification 85 6.4.3 Ablation Study 86 6.4.4 Generated Sentences 88 6.4.5 Implementation Details 89 6.5 Summary and Discussion 90 Chapter 7 Conclusion 92 Appendix A Appendix 96 A.1 Sentences Generated from CS-LVM 96Docto