809 research outputs found
Intention Detection Based on Siamese Neural Network With Triplet Loss
Understanding the user's intention is an essential task for the spoken language understanding (SLU) module in the dialogue system, which further illustrates vital information for managing and generating future action and response. In this paper, we propose a triplet training framework based on the multiclass classification approach to conduct the training for the intention detection task. Precisely, we utilize a Siamese neural network architecture with metric learning to construct a robust and discriminative utterance feature embedding model. We modified the RMCNN model and fine-tuned BERT model as Siamese encoders to train utterance triplets from different semantic aspects. The triplet loss can effectively distinguish the details of two input data by learning a mapping from sequence utterances to a compact Euclidean space. After generating the mapping, the intention detection task can be easily implemented using standard techniques with pre-trained embeddings as feature vectors. Besides, we use the fusion strategy to enhance utterance feature representation in the downstream of intention detection task. We conduct experiments on several benchmark datasets of intention detection task: Snips dataset, ATIS dataset, Facebook multilingual task-oriented datasets, Daily Dialogue dataset, and MRDA dataset. The results illustrate that the proposed method can effectively improve the recognition performance of these datasets and achieves new state-of-the-art results on single-turn task-oriented datasets (Snips dataset, Facebook dataset), and a multi-turn dataset (Daily Dialogue dataset)
Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino
On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-Ββit 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall βCavallerizza Realeβ. The CLiC-Ββit conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges
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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¦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
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