1,669 research outputs found
Deep Open Intent Classification with Adaptive Decision Boundary
Open intent classification is a challenging task in dialogue systems. On the
one hand, it should ensure the quality of known intent identification. On the
other hand, it needs to detect the open (unknown) intent without prior
knowledge. Current models are limited in finding the appropriate decision
boundary to balance the performances of both known intents and the open intent.
In this paper, we propose a post-processing method to learn the adaptive
decision boundary (ADB) for open intent classification. We first utilize the
labeled known intent samples to pre-train the model. Then, we automatically
learn the adaptive spherical decision boundary for each known class with the
aid of well-trained features. Specifically, we propose a new loss function to
balance both the empirical risk and the open space risk. Our method does not
need open intent samples and is free from modifying the model architecture.
Moreover, our approach is surprisingly insensitive with less labeled data and
fewer known intents. Extensive experiments on three benchmark datasets show
that our method yields significant improvements compared with the
state-of-the-art methods. The codes are released at
https://github.com/thuiar/Adaptive-Decision-Boundary.Comment: Accepted by AAAI 2021 (Main Track, Long Paper
A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery
Intent Detection is one of the tasks of the Natural Language Understanding
(NLU) unit in task-oriented dialogue systems. Out of Scope (OOS) and Out of
Domain (OOD) inputs may run these systems into a problem. On the other side, a
labeled dataset is needed to train a model for Intent Detection in
task-oriented dialogue systems. The creation of a labeled dataset is
time-consuming and needs human resources. The purpose of this article is to
address mentioned problems. The task of identifying OOD/OOS inputs is named
OOD/OOS Intent Detection. Also, discovering new intents and pseudo-labeling of
OOD inputs is well known by Intent Discovery. In OOD intent detection part, we
make use of a Variational Autoencoder to distinguish between known and unknown
intents independent of input data distribution. After that, an unsupervised
clustering method is used to discover different unknown intents underlying
OOD/OOS inputs. We also apply a non-linear dimensionality reduction on OOD/OOS
representations to make distances between representations more meaning full for
clustering. Our results show that the proposed model for both OOD/OOS Intent
Detection and Intent Discovery achieves great results and passes baselines in
English and Persian languages
Open World Classification with Adaptive Negative Samples
Open world classification is a task in natural language processing with key
practical relevance and impact. Since the open or {\em unknown} category data
only manifests in the inference phase, finding a model with a suitable decision
boundary accommodating for the identification of known classes and
discrimination of the open category is challenging. The performance of existing
models is limited by the lack of effective open category data during the
training stage or the lack of a good mechanism to learn appropriate decision
boundaries. We propose an approach based on \underline{a}daptive
\underline{n}egative \underline{s}amples (ANS) designed to generate effective
synthetic open category samples in the training stage and without requiring any
prior knowledge or external datasets. Empirically, we find a significant
advantage in using auxiliary one-versus-rest binary classifiers, which
effectively utilize the generated negative samples and avoid the complex
threshold-seeking stage in previous works. Extensive experiments on three
benchmark datasets show that ANS achieves significant improvements over
state-of-the-art methods.Comment: Accepted by EMNLP 2021 (Main Track, Long Paper
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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
Signal Processing and Machine Learning Techniques Towards Various Real-World Applications
abstract: Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques require data to train the algorithms and model a complex system and make predictions based on that model. Due to development of sophisticated sensors it has become easier to collect large volumes of data which is used to make necessary hypotheses using ML. The promising results obtained using ML have opened up new opportunities of research across various departments and this dissertation is a manifestation of it. Here, some unique studies have been presented, from which valuable inference have been drawn for a real-world complex system. Each study has its own unique sets of motivation and relevance to the real world. An ensemble of signal processing (SP) and ML techniques have been explored in each study. This dissertation provides the detailed systematic approach and discusses the results achieved in each study. Valuable inferences drawn from each study play a vital role in areas of science and technology, and it is worth further investigation. This dissertation also provides a set of useful SP and ML tools for researchers in various fields of interest.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
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