806 research outputs found
Multi-level head-wise match and aggregation in transformer for textual sequence matching
Transformer has been successfully applied to many natural language processing
tasks. However, for textual sequence matching, simple matching between the
representation of a pair of sequences might bring in unnecessary noise. In this
paper, we propose a new approach to sequence pair matching with Transformer, by
learning head-wise matching representations on multiple levels. Experiments
show that our proposed approach can achieve new state-of-the-art performance on
multiple tasks that rely only on pre-computed sequence-vector-representation,
such as SNLI, MNLI-match, MNLI-mismatch, QQP, and SQuAD-binary.Comment: AAAI 2020, 8 page
μ§μ κΈ°λ° λνμμμ λν νΉμ±μ νμ©ν μ§μ μ ν λ° λνΉ λ°©λ²
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Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : 곡과λν μ κΈ°Β·μ»΄ν¨ν°κ³΅νλΆ, 2022. 8. μ΄μꡬ.Knowledge grounded conversation (KGC) model aims to generate informative responses relevant to both conversation history and external knowledge. One of the most important parts of KGC models is to find the knowledge which provides the basis on which the responses are grounded. If the model selects inappropriate knowledge, it may produce responses that are irrelevant or lack knowledge. In this dissertation, we study the methods of leveraging conversational characteristics to select or rank the knowledge for knowledge grounded conversation.
In particular, this dissertation provides novel two methods, where one of which focuses on the sequential structure of multi-turn conversation, and the other focuses on utilizing local context and topic of a long conversation. We first propose two knowledge selection strategies of which one preserves the sequential matching features and the other encodes the sequential nature of the conversation. Second, we propose a novel knowledge ranking model that composes an appropriate range of relevant documents by exploiting both the topic keywords and local context of a conversation. In addition, we apply the knowledge ranking model in quote recommendation with our new quote recommendation framework that provides hard negative samples to the model. Our experimental results show that the KGC models based on our proposed knowledge selection and ranking methods outperform the competitive models in terms of groundness and relevance.μ§μ κΈ°λ° λν λͺ¨λΈμ λν κΈ°λ‘κ³Ό μΈλΆ μ§μ μ΄ λ κ°μ§ λͺ¨λμ κ΄λ ¨λ μλ΅μ μμ±νλ κ²μ λͺ©νλ‘ νλ€. μ§μ κΈ°λ° λν λͺ¨λΈμ κ°μ₯ μ€μν λΆλΆ μ€ νλλ μλ΅μ κΈ°λ°μ μ 곡νλ μ§μμ μ°Ύλ κ²μ΄λ€. μ§μ κΈ°λ° λͺ¨λΈμ΄ μ£Όμ΄μ§ λ¬Έλ§₯μ λΆμ ν©ν μ§μμ μ°Ύλ κ²½μ° κ΄λ ¨μ±μ΄ λ¨μ΄μ§κ±°λ μ§μμ΄ λΆμ‘±ν μλ΅μ΄ μμ±λ μ μλ€. μ΄ λ¬Έμ λ₯Ό ν΄κ²°νκΈ° μν΄ μ΄ λ
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Όλ¬Έμ λνμ μ£Όμ ν€μλμ μλ΅ λ°λ‘ μ΄μ μ λ¬Έλ§₯μ λͺ¨λ νμ©νμ¬ μ μ ν λ²μμ κ΄λ ¨ λ¬Έμλ€λ‘ κ²μ κ²°κ³Όλ₯Ό ꡬμ±νλ μλ‘μ΄ μ§μ μμ λͺ¨λΈμ μ μνλ€. λ§μ§λ§μΌλ‘ μ§μ μμ λͺ¨λΈμ μ μμ± κ²μ¦μ μν΄ μ λ΅ μΈμ©κ΅¬μ μλ―Έμ μΌλ‘ μ μ¬νμ§λ§ μ λ΅μ μλ μΈμ©κ΅¬μ μ§ν©μ μΈμ©κ΅¬ μμ λͺ¨λΈμ μ 곡νλ μΈμ©κ΅¬ μΆμ² νλ μμν¬λ₯Ό μ μνλ€. μ μλ μ§μ μ ν λ° μμ λͺ¨λΈμ κΈ°λ°μΌλ‘ νλ μ§μ κΈ°λ° λν λͺ¨λΈμ΄ κ²½μ λͺ¨λΈλ³΄λ€ μΈλΆ μ§μ λ° λν λ¬Έλ§₯κ³Όμ κ΄λ ¨μ± μΈ‘λ©΄μμ μ°μνλ€λ κ²μ μ¬λ κ°μ λν λ°μ΄ν°λ₯Ό μ΄μ©ν λ€μμ μ€νμ ν΅ν΄ κ²μ¦νμλ€.Abstract 1
1. Introduction 17
2. Background and Related Works 25
2.1 Terminology 25
2.2 Overview of Technologies for Conversational Systems 27
2.2.1 Open-domain Dialogue System 27
2.2.2 Task-oriented Dialogue System 29
2.2.3 Question Answering System 29
2.3 Components of Knowledge Grounded Conversation Model 31
2.4 Related Works 36
2.4.1 KGC datasets 36
2.4.2 Soft Selection-based KGC Model 36
2.4.3 Hard Selection-based KGC Model 37
2.4.4 Retrieval-based KGC Models 39
2.4.5 Response Generation with Knowledge Integration 39
2.4.6 Quote Recommendation 42
2.5 Evaluation Methods 44
2.6 Problem Statements 47
3. Knowledge Selection with Sequential Structure of Conversation 48
3.1 Motivation 48
3.2 Reduce-Match Strategy & Match-Reduce Strategy 49
3.2.1 Backbone architecture 51
3.2.2 Reduce-Match Strategy-based Models 52
3.2.3 Match-Reduce Strategy-based Models 56
3.3 Experiments 62
3.3.1 Experimental Setup 62
3.3.2 Experimental Results 70
3.4 Analysis 72
3.4.1 Case Study 72
3.4.2 Impact of Matching Difficulty 75
3.4.3 Impact of Length of Context 77
3.4.4 Impact of Dialogue Act of Message 78
4. Knowledge Ranking with Local Context and Topic Keywords 81
4.1 Motivation 81
4.2 Retrieval-Augmented Knowledge Grounded Conversation Model 85
4.2.1 Base Model 86
4.2.2 Topic-aware Dual Matching for Knowledge Re-ranking 86
4.2.3 Data Weighting Scheme for Retrieval Augmented Generation Models 89
4.3 Experiments 90
4.3.1 Experimental Setup 90
4.3.2 Experimental Results 94
4.4 Analysis 98
4.4.1 Case Study 98
4.4.2 Ablation Study 99
4.4.3 Model Variations 104
4.4.4 Error Analysis 105
5. Application: Quote Recommendation with Knowledge Ranking 110
5.1 Motivation 110
5.2 CAGAR: A Framework for Quote Recommendation 112
5.2.1 Conversation Encoder 114
5.2.2 Quote Encoder 114
5.2.3 Candidate Generator 115
5.2.4 Re-ranker 116
5.2.5 Training and Inference 116
5.3 Experiments 117
5.3.1 Experimental Setup 117
5.3.2 Experimental Results 119
5.4 Analysis 120
5.4.1 Ablation Study 120
5.4.2 Case Study 121
5.4.3 Impact of Length of Context 121
5.4.4 Impact of Training Set Size per Quote 123
6. Conclusion 125
6.1 Contributions and Limitations 126
6.2 Future Works 128
Appendix A. Preliminary Experiments for Quote Recommendations 131
A.1 Methods 131
A.1.1 Matching Granularity Adjustment 131
A.1.2 Random Forest 133
A.1.3 Convolutional Neural Network 133
A.1.4 Recurrent Neural Network 134
A.2 Experiments 135
A.2.1 Baselines and Implementation Details 135
A.2.2 Datasets 136
A.2.3 Results and Discussions 137
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Computational Aesthetics for Fashion
The online fashion industry is growing fast and with it, the need for advanced systems able to automatically solve different tasks in an accurate way. With the rapid advance of digital technologies, Deep Learning has played an important role in Computational Aesthetics, an interdisciplinary area that tries to bridge fine art, design, and computer science. Specifically, Computational Aesthetics aims to automatize human aesthetic judgments with computational methods. In this thesis, we focus on three applications of computer vision in fashion, and we discuss how Computational Aesthetics helps solve them accurately
Shatter and Gather: Learning Referring Image Segmentation with Text Supervision
Referring image segmentation, the task of segmenting any arbitrary entities
described in free-form texts, opens up a variety of vision applications.
However, manual labeling of training data for this task is prohibitively
costly, leading to lack of labeled data for training. We address this issue by
a weakly supervised learning approach using text descriptions of training
images as the only source of supervision. To this end, we first present a new
model that discovers semantic entities in input image and then combines such
entities relevant to text query to predict the mask of the referent. We also
present a new loss function that allows the model to be trained without any
further supervision. Our method was evaluated on four public benchmarks for
referring image segmentation, where it clearly outperformed the existing method
for the same task and recent open-vocabulary segmentation models on all the
benchmarks.Comment: Accepted to ICCV 2023, Project page:
https://southflame.github.io/sag
Dual Path Modeling for Semantic Matching by Perceiving Subtle Conflicts
Transformer-based pre-trained models have achieved great improvements in
semantic matching. However, existing models still suffer from insufficient
ability to capture subtle differences. The modification, addition and deletion
of words in sentence pairs may make it difficult for the model to predict their
relationship. To alleviate this problem, we propose a novel Dual Path Modeling
Framework to enhance the model's ability to perceive subtle differences in
sentence pairs by separately modeling affinity and difference semantics. Based
on dual-path modeling framework we design the Dual Path Modeling Network
(DPM-Net) to recognize semantic relations. And we conduct extensive experiments
on 10 well-studied semantic matching and robustness test datasets, and the
experimental results show that our proposed method achieves consistent
improvements over baselines.Comment: ICASSP 2023. arXiv admin note: text overlap with arXiv:2210.0345
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