67 research outputs found
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Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :곡과λν μ»΄ν¨ν°κ³΅νλΆ,2020. 2. μ΄μꡬ.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.μ΅κ·Ό λ₯λ¬λ κΈ°λ° μμ± λͺ¨λΈμ κΈκ²©ν λ°μ μΌλ‘ μ΄λ₯Ό μ΄μ©ν μμ± κΈ°λ° λ°μ΄ν° μ¦κ° κΈ°λ²(generative data augmentation, GDA)μ μ€ν κ°λ₯μ±μ λν κΈ°λκ° μ»€μ§κ³ μλ€. μμ± κΈ°λ° λ°μ΄ν° μ¦κ° κΈ°λ²μ λ₯λ¬λ κΈ°λ° μ μ¬λ³μ λͺ¨λΈμμ μμ± λ μνμ μλ³Έ λ°μ΄ν°μ
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μ€νΈ λΆλ₯(text classification), μμ°¨μ λ μ΄λΈλ§κ³Ό λ©ν°νμ€νΉ κΈ°μ μ΄ νμν λ°ν μ΄ν΄(spoken language understanding, SLU), κ³μΈ΅μ μ΄λ©° μ¬κ·μ μΈ λ°μ΄ν° ꡬ쑰μ λν κ³ λ €κ° νμν λν μν μΆμ (dialogue state tracking, DST) λ± μΈ κ°μ§ λ¬Έμ μμ λ₯λ¬λ κΈ°λ° μμ± λͺ¨λΈμ νμ©ν λ°μ΄ν° μ¦κ° κΈ°λ²μ νλΉμ±μ λν΄ λ€λ£¬λ€. λ³Έ μ°κ΅¬μμλ 쑰건λΆ, κ³μΈ΅μ λ° μμ°¨μ variational autoencoder (VAE)μ κΈ°λ°νμ¬ κ° μμ°μ΄μ²λ¦¬ λ¬Έμ μ νΉνλ ν
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μ¦νμλ€. λΆμμ μ°κ΅¬μμλ μκΈ°νκ·μ (autoregressive) VAEμμ λΉλ²ν λ°μνλ posterior collapse λ¬Έμ μ λν΄ νꡬνκ³ , ν΄λΉ λ¬Έμ λ₯Ό μνν μ μλ μ κ· λ°©μλ μ μνλ€. ν΄λΉ λ°©λ²μ μμ±μ λ°μ΄ν° μ¦κ°μ νμν 볡μ‘ν VAE λͺ¨λΈμ μ μ©νμμ λ, μμ± λͺ¨λΈμ μμ± μ§μ΄ ν₯μλμ΄ λ°μ΄ν° μ¦κ° ν¨κ³Όμλ κΈμ μ μΈ μν₯μ λ―ΈμΉ μ μμμ κ²μ¦νμλ€. λ³Έ λ
Όλ¬Έμ ν΅ν΄ μμ°μ΄μ²λ¦¬ λΆμΌμμ κΈ°μ‘΄ μ κ·ν κΈ°λ²κ³Ό λ³ν μ μ© κ°λ₯ν λΉμ§λ ννμ λ°μ΄ν° μ¦κ° κΈ°λ²μ νμ€νλ₯Ό κΈ°λν΄ λ³Ό μ μλ€.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
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 β 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) β
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 β 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)
Data Augmentation for Conversational AI
Advancements in conversational systems have revolutionized information
access, surpassing the limitations of single queries. However, developing
dialogue systems requires a large amount of training data, which is a challenge
in low-resource domains and languages. Traditional data collection methods like
crowd-sourcing are labor-intensive and time-consuming, making them ineffective
in this context. Data augmentation (DA) is an affective approach to alleviate
the data scarcity problem in conversational systems. This tutorial provides a
comprehensive and up-to-date overview of DA approaches in the context of
conversational systems. It highlights recent advances in conversation
augmentation, open domain and task-oriented conversation generation, and
different paradigms of evaluating these models. We also discuss current
challenges and future directions in order to help researchers and practitioners
to further advance the field in this area
Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation
Hierarchical neural networks are often used to model inherent structures
within dialogues. For goal-oriented dialogues, these models miss a mechanism
adhering to the goals and neglect the distinct conversational patterns between
two interlocutors. In this work, we propose Goal-Embedded Dual Hierarchical
Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture
interlocutor-level disparity while modeling goal-oriented dialogues.
Experiments on dialogue generation, response generation, and human evaluations
demonstrate that the proposed model successfully generates higher-quality, more
diverse and goal-centric dialogues. Moreover, we apply data augmentation via
goal-oriented dialogue generation for task-oriented dialog systems with better
performance achieved.Comment: Accepted by CoNLL-201
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