967 research outputs found
Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds
We propose a computational model of situated language comprehension based on
the Indexical Hypothesis that generates meaning representations by translating
amodal linguistic symbols to modal representations of beliefs, knowledge, and
experience external to the linguistic system. This Indexical Model incorporates
multiple information sources, including perceptions, domain knowledge, and
short-term and long-term experiences during comprehension. We show that
exploiting diverse information sources can alleviate ambiguities that arise
from contextual use of underspecific referring expressions and unexpressed
argument alternations of verbs. The model is being used to support linguistic
interactions in Rosie, an agent implemented in Soar that learns from
instruction.Comment: Advances in Cognitive Systems 3 (2014
A Survey of Knowledge-based Sequential Decision Making under Uncertainty
Reasoning with declarative knowledge (RDK) and sequential decision-making
(SDM) are two key research areas in artificial intelligence. RDK methods reason
with declarative domain knowledge, including commonsense knowledge, that is
either provided a priori or acquired over time, while SDM methods
(probabilistic planning and reinforcement learning) seek to compute action
policies that maximize the expected cumulative utility over a time horizon;
both classes of methods reason in the presence of uncertainty. Despite the rich
literature in these two areas, researchers have not fully explored their
complementary strengths. In this paper, we survey algorithms that leverage RDK
methods while making sequential decisions under uncertainty. We discuss
significant developments, open problems, and directions for future work
DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations
In open-domain dialogue generation tasks, contexts and responses in most
datasets are one-to-one mapped, violating an important many-to-many
characteristic: a context leads to various responses, and a response answers
multiple contexts. Without such patterns, models poorly generalize and prefer
responding safely. Many attempts have been made in either multi-turn settings
from a one-to-many perspective or in a many-to-many perspective but limited to
single-turn settings. The major challenge to many-to-many augment multi-turn
dialogues is that discretely replacing each turn with semantic similarity
breaks fragile context coherence. In this paper, we propose DialoGue Path
Sampling (DialoGPS) method in continuous semantic space, the first many-to-many
augmentation method for multi-turn dialogues. Specifically, we map a dialogue
to our extended Brownian Bridge, a special Gaussian process. We sample latent
variables to form coherent dialogue paths in the continuous space. A dialogue
path corresponds to a new multi-turn dialogue and is used as augmented training
data. We show the effect of DialoGPS with both automatic and human evaluation.Comment: ACL 2023 mai
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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
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