Dialogue state tracking accuracy improvement by distinguishing slot-value pairs and dialogue behaviour

Dialog state tracking (DST) plays a critical role in cycle life of a task-oriented dialogue system. DST represents the goals of the consumer at each step by dialogue and describes such objectives as a conceptual structure comprising slot-value pairs and dialogue actions that specifically improve the performance and effectiveness of dialogue systems. DST faces several challenge

The Dialog State Tracking Challenge Series: A Review

In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the state of the conversation -- such as the user's goal -- given all of the dialog history up to that turn.  Dialog state tracking is crucial to the success of a dialog system, yet until recently there were no common resources, hampering progress.  The Dialog State Tracking Challenge series of 3 tasks introduced the first shared testbed and evaluation metrics for dialog state tracking, and has underpinned three key advances in dialog state tracking: the move from generative to discriminative models; the adoption of discriminative sequential techniques; and the incorporation of the speech recognition results directly into the dialog state tracker.  This paper reviews this research area, covering both the challenge tasks themselves and summarizing the work they have enabled

Visual world studies of conversational perspective taking: similar findings, diverging interpretations

Visual-world eyetracking greatly expanded the potential for insight into how listeners access and use common ground during situated language comprehension. Past reviews of visual world studies on perspective taking have largely taken the diverging findings of the various studies at face value, and attributed these apparently different findings to differences in the extent to which the paradigms used by different labs afford collaborative interaction. Researchers are asking questions about perspective taking of an increasingly nuanced and sophisticated nature, a clear indicator of progress. But this research has the potential not only to improve our understanding of conversational perspective taking. Grappling with problems of data interpretation in such a complex domain has the unique potential to drive visual world researchers to a deeper understanding of how to best map visual world data onto psycholinguistic theory. I will argue against this interactional affordances explanation, on two counts. First, it implies that interactivity affects the overall ability to form common ground, and thus provides no straightforward explanation of why, within a single noninteractive study, common ground can have very large effects on some aspects of processing (referential anticipation) while having negligible effects on others (lexical processing). Second, and more importantly, the explanation accepts the divergence in published findings at face value. However, a closer look at several key studies shows that the divergences are more likely to reflect inconsistent practices of analysis and interpretation that have been applied to an underlying body of data that is, in fact, surprisingly consistent. The diverging interpretations, I will argue, are the result of differences in the handling of anticipatory baseline effects (ABEs) in the analysis of visual world data. ABEs arise in perspective-taking studies because listeners have earlier access to constraining information about who knows what than they have to referential speech, and thus can already show biases in visual attention even before the processing of any referential speech has begun. To be sure, these ABEs clearly indicate early access to common ground; however, access does not imply integration, since it is possible that this information is not used later to modulate the processing of incoming speech. Failing to account for these biases using statistical or experimental controls leads to over-optimistic assessments of listeners’ ability to integrate this information with incoming speech. I will show that several key studies with varying degrees of interactional affordances all show similar temporal profiles of common ground use during the interpretive process: early anticipatory effects, followed by bottom-up effects of lexical processing that are not modulated by common ground, followed (optionally) by further late effects that are likely to be post-lexical. Furthermore, this temporal profile for common ground radically differs from the profile of contextual effects related to verb semantics. Together, these findings are consistent with the proposal that lexical processes are encapsulated from common ground, but cannot be straightforwardly accounted for by probabilistic constraint-based approaches

A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective

딥러닝 기반 생성 모델을 이용한 자연어처리 데이터 증강 기법

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 이상구.Recent advances in generation capability of deep learning models have spurred interest in utilizing deep generative models for unsupervised generative data augmentation (GDA). Generative data augmentation aims to improve the performance of a downstream machine learning model by augmenting the original dataset with samples generated from a deep latent variable model. This data augmentation approach is attractive to the natural language processing community, because (1) there is a shortage of text augmentation techniques that require little supervision and (2) resource scarcity being prevalent. In this dissertation, we explore the feasibility of exploiting deep latent variable models for data augmentation on three NLP tasks: sentence classification, spoken language understanding (SLU) and dialogue state tracking (DST), represent NLP tasks of various complexities and properties -- SLU requires multi-task learning of text classification and sequence tagging, while DST requires the understanding of hierarchical and recurrent data structures. For each of the three tasks, we propose a task-specific latent variable model based on conditional, hierarchical and sequential variational autoencoders (VAE) for multi-modal joint modeling of linguistic features and the relevant annotations. We conduct extensive experiments to statistically justify our hypothesis that deep generative data augmentation is beneficial for all subject tasks. Our experiments show that deep generative data augmentation is effective for the select tasks, supporting the idea that the technique can potentially be utilized for other range of NLP tasks. Ablation and qualitative studies reveal deeper insight into the underlying mechanisms of generative data augmentation. As a secondary contribution, we also shed light onto the recurring posterior collapse phenomenon in autoregressive VAEs and, subsequently, propose novel techniques to reduce the model risk, which is crucial for proper training of complex VAE models, enabling them to synthesize better samples for data augmentation. In summary, this work intends to demonstrate and analyze the effectiveness of unsupervised generative data augmentation in NLP. Ultimately, our approach enables standardized adoption of generative data augmentation, which can be applied orthogonally to existing regularization techniques.최근 딥러닝 기반 생성 모델의 급격한 발전으로 이를 이용한 생성 기반 데이터 증강 기법(generative data augmentation, GDA)의 실현 가능성에 대한 기대가 커지고 있다. 생성 기반 데이터 증강 기법은 딥러닝 기반 잠재변수 모델에서 생성 된 샘플을 원본 데이터셋에 추가하여 연관된 태스크의 성능을 향상시키는 기술을 의미한다. 따라서 생성 기반 데이터 증강 기법은 데이터 공간에서 이뤄지는 정규화 기술의 한 형태로 간주될 수 있다. 이러한 딥러닝 기반 생성 모델의 새로운 활용 가능성은 자연어처리 분야에서 더욱 중요하게 부각되는 이유는 (1) 범용 가능한 텍스트 데이터 증강 기술의 부재와 (2) 텍스트 데이터의 희소성을 극복할 수 있는 대안이 필요하기 때문이다. 문제의 복잡도와 특징을 골고루 채집하기 위해 본 논문에서는 텍스트 분류(text classification), 순차적 레이블링과 멀티태스킹 기술이 필요한 발화 이해(spoken language understanding, SLU), 계층적이며 재귀적인 데이터 구조에 대한 고려가 필요한 대화 상태 추적(dialogue state tracking, DST) 등 세 가지 문제에서 딥러닝 기반 생성 모델을 활용한 데이터 증강 기법의 타당성에 대해 다룬다. 본 연구에서는 조건부, 계층적 및 순차적 variational autoencoder (VAE)에 기반하여 각 자연어처리 문제에 특화된 텍스트 및 연관 부착 정보를 동시에 생성하는 특수 딥러닝 생성 모델들을 제시하고, 다양한 하류 모델과 데이터셋을 다루는 등 폭 넓은 실험을 통해 딥 생성 모델 기반 데이터 증강 기법의 효과를 통계적으로 입증하였다. 부수적 연구에서는 자기회귀적(autoregressive) VAE에서 빈번히 발생하는 posterior collapse 문제에 대해 탐구하고, 해당 문제를 완화할 수 있는 신규 방안도 제안한다. 해당 방법을 생성적 데이터 증강에 필요한 복잡한 VAE 모델에 적용하였을 때, 생성 모델의 생성 질이 향상되어 데이터 증강 효과에도 긍정적인 영향을 미칠 수 있음을 검증하였다. 본 논문을 통해 자연어처리 분야에서 기존 정규화 기법과 병행 적용 가능한 비지도 형태의 데이터 증강 기법의 표준화를 기대해 볼 수 있다.1 Introduction 1 1.1 Motivation 1 1.2 Dissertation Overview 6 2 Background and Related Work 8 2.1 Deep Latent Variable Models 8 2.1.1 Variational Autoencoder (VAE) 10 2.1.2 Deep Generative Models and Text Generation 12 2.2 Data Augmentation 12 2.2.1 General Description 13 2.2.2 Categorization of Data Augmentation 14 2.2.3 Theoretical Explanations 21 2.3 Summary 24 3 Basic Task: Text Classi cation 25 3.1 Introduction 25 3.2 Our Approach 28 3.2.1 Proposed Models 28 3.2.2 Training with I-VAE 29 3.3 Experiments 31 3.3.1 Datasets 32 3.3.2 Experimental Settings 33 3.3.3 Implementation Details 34 3.3.4 Data Augmentation Results 36 3.3.5 Ablation Studies 39 3.3.6 Qualitative Analysis 40 3.4 Summary 45 4 Multi-task Learning: Spoken Language Understanding 46 4.1 Introduction 46 4.2 Related Work 48 4.3 Model Description 48 4.3.1 Framework Formulation 48 4.3.2 Joint Generative Model 49 4.4 Experiments 56 4.4.1 Datasets 56 4.4.2 Experimental Settings 57 4.4.3 Generative Data Augmentation Results 61 4.4.4 Comparison to Other State-of-the-art Results 63 4.4.5 Ablation Studies 63 4.5 Summary 67 5 Complex Data: Dialogue State Tracking 68 5.1 Introduction 68 5.2 Background and Related Work 70 5.2.1 Task-oriented Dialogue 70 5.2.2 Dialogue State Tracking 72 5.2.3 Conversation Modeling 72 5.3 Variational Hierarchical Dialogue Autoencoder (VHDA) 73 5.3.1 Notations 73 5.3.2 Variational Hierarchical Conversational RNN 74 5.3.3 Proposed Model 75 5.3.4 Posterior Collapse 82 5.4 Experimental Results 84 5.4.1 Experimental Settings 84 5.4.2 Data Augmentation Results 90 5.4.3 Intrinsic Evaluation - Language Evaluation 94 5.4.4 Qualitative Results 95 5.5 Summary 101 6 Conclusion 103 6.1 Summary 103 6.2 Limitations 104 6.3 Future Work 105Docto

Active Collaboration of Classifiers for Visual Tracking

Recently, discriminative visual trackers obtain state-of-the-art performance, yet they suffer in the presence of different real-world challenges such as target motion and appearance changes. In a discriminative tracker, one or more classifiers are employed to obtain the target/nontarget label for the samples, which in turn determine the target’s location. To cope with variations of the target shape and appearance, the classifier(s) are updated online with different samples of the target and the background. Sample selection, labeling, and updating the classifier are prone to various sources of errors that drift the tracker. In this study, we motivate, conceptualize, realize, and formalize a novel active co-tracking framework, step by step to demonstrate the challenges and generic solutions for them. In this framework, not only classifiers cooperate in labeling the samples but also exchange their information to robustify the labeling, improve the sampling, and realize efficient yet effective updating. The proposed framework is evaluated against state-of-the-art trackers on public dataset and showed promising results

Beyond Traditional Emotion Recognition

Ph.DDOCTOR OF PHILOSOPH

A system for recognizing human emotions based on speech analysis and facial feature extraction: applications to Human-Robot Interaction

With the advance in Artificial Intelligence, humanoid robots start to interact with ordinary people based on the growing understanding of psychological processes. Accumulating evidences in Human Robot Interaction (HRI) suggest that researches are focusing on making an emotional communication between human and robot for creating a social perception, cognition, desired interaction and sensation. Furthermore, robots need to receive human emotion and optimize their behavior to help and interact with a human being in various environments. The most natural way to recognize basic emotions is extracting sets of features from human speech, facial expression and body gesture. A system for recognition of emotions based on speech analysis and facial features extraction can have interesting applications in Human-Robot Interaction. Thus, the Human-Robot Interaction ontology explains how the knowledge of these fundamental sciences is applied in physics (sound analyses), mathematics (face detection and perception), philosophy theory (behavior) and robotic science context. In this project, we carry out a study to recognize basic emotions (sadness, surprise, happiness, anger, fear and disgust). Also, we propose a methodology and a software program for classification of emotions based on speech analysis and facial features extraction. The speech analysis phase attempted to investigate the appropriateness of using acoustic (pitch value, pitch peak, pitch range, intensity and formant), phonetic (speech rate) properties of emotive speech with the freeware program PRAAT, and consists of generating and analyzing a graph of speech signals. The proposed architecture investigated the appropriateness of analyzing emotive speech with the minimal use of signal processing algorithms. 30 participants to the experiment had to repeat five sentences in English (with durations typically between 0.40 s and 2.5 s) in order to extract data relative to pitch (value, range and peak) and rising-falling intonation. Pitch alignments (peak, value and range) have been evaluated and the results have been compared with intensity and speech rate. The facial feature extraction phase uses the mathematical formulation (B\ue9zier curves) and the geometric analysis of the facial image, based on measurements of a set of Action Units (AUs) for classifying the emotion. The proposed technique consists of three steps: (i) detecting the facial region within the image, (ii) extracting and classifying the facial features, (iii) recognizing the emotion. Then, the new data have been merged with reference data in order to recognize the basic emotion. Finally, we combined the two proposed algorithms (speech analysis and facial expression), in order to design a hybrid technique for emotion recognition. Such technique have been implemented in a software program, which can be employed in Human-Robot Interaction. The efficiency of the methodology was evaluated by experimental tests on 30 individuals (15 female and 15 male, 20 to 48 years old) form different ethnic groups, namely: (i) Ten adult European, (ii) Ten Asian (Middle East) adult and (iii) Ten adult American. Eventually, the proposed technique made possible to recognize the basic emotion in most of the cases