Keywords:
Computers.
;
Optical data processing.
;
Application software.
;
Electronic books.
Type of Medium:
Online Resource
Pages:
1 online resource (861 pages)
Edition:
1st ed.
ISBN:
9783030884802
Series Statement:
Lecture Notes in Computer Science Series ; v.13028
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=6749177
DDC:
004
Language:
English
Note:
Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Oral - Fundamentals of NLP -- Coreference Resolution: Are the Eliminated Spans Totally Worthless? -- 1 Introduction -- 2 Background -- 3 Coreference Resolution with Enhanced Mention Representation -- 3.1 Mention Detection -- 3.2 Coreference Resolving with Global Spans Perceived -- 4 Model Training -- 5 Experimentation -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 5.3 Analysis on Context-Aware Word Representations -- 5.4 Case Study -- 6 Related Work -- 7 Conclusion -- References -- Chinese Macro Discourse Parsing on Dependency Graph Convolutional Network -- 1 Introduction -- 2 Related Work -- 3 Basic Model: MDParser-TS -- 4 Chinese Macro Discourse Parsing on Dependency Graph Convolutional Network -- 4.1 Internal Topic Graph Construction -- 4.2 Interactive Topic Graph Construction -- 4.3 Dependency Graph Convolutional Network -- 4.4 Classifier -- 5 Experimentation -- 5.1 Dataset and Experimental Settings -- 5.2 Baselines -- 5.3 Experimental Results -- 6 Analysis -- 6.1 Analysis on Internal Topic Graph -- 6.2 Analysis on Interactive Topic Graph -- 6.3 Experimentation on English RST-DT -- 7 Conclusion -- References -- Predicting Categorial Sememe for English-Chinese Word Pairs via Representations in Explainable Sememe Space -- 1 Introduction -- 2 Task Formalization -- 3 Methodology -- 3.1 Word Vector Space O and Sememe Space Os -- 3.2 HowNet in Sememe Space Os -- 3.3 Target Data in Sememe Space Os -- 3.4 Training and Prediction -- 4 Experiment -- 4.1 Datasets -- 4.2 Experiment Settings -- 4.3 Overall Results -- 4.4 Results on Different POS Tags -- 4.5 Results on Different Ambiguity Degrees -- 4.6 Effect of Descending Factor c -- 4.7 Effect of Training Set Ratio -- 4.8 Categorial Sememe Knowledge Base -- 5 Related Work -- 6 Conclusion and Future Work.
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References -- Multi-level Cohesion Information Modeling for Better Written and Dialogue Discourse Parsing -- 1 Introduction -- 2 Related Work -- 3 Baseline Model -- 3.1 Attention-Based EDU Encoder -- 3.2 Top-Down Baseline Model -- 3.3 Bottom-Up Baseline Model -- 3.4 Deep Sequential Baseline Model -- 4 Cohesion Modeling -- 4.1 Auto Cohesion Information Extraction -- 4.2 Graph Construction -- 4.3 Cohesion Modelling -- 4.4 Fusion Layer -- 5 Experiments -- 5.1 Datasets -- 5.2 Metric -- 5.3 Experimental Result -- 6 Conclusion -- References -- ProPC: A Dataset for In-Domain and Cross-Domain Proposition Classification Tasks -- 1 Introduction -- 2 Dataset Construction -- 2.1 Proposition Definition -- 2.2 Data Acquisition -- 2.3 Data Annotation -- 2.4 Dataset Analysis -- 3 Experiments -- 3.1 Baseline Methods -- 3.2 Experimental Setup -- 3.3 Results and Analysis -- 4 Related Work -- 5 Conclusion -- References -- CTRD: A Chinese Theme-Rheme Discourse Dataset -- 1 Introduction -- 2 Related Work -- 3 Theory Basis -- 3.1 The Theme-Rheme Theory -- 3.2 The Thematic Progression Patterns -- 4 Annotation Scheme -- 4.1 Theme-Rheme Annotation Criteria -- 4.2 Thematic Progression Annotation Criteria -- 5 Statistics -- 6 Experiments and Analysis -- 6.1 Theme-Rheme Automatic Recognition -- 6.2 Function Types Automatic Recognition -- 7 Conclusion -- References -- Machine Translation and Multilinguality -- Learning to Select Relevant Knowledge for Neural Machine Translation -- 1 Introduction -- 2 Our Approach -- 2.1 Problem Definition -- 2.2 Retrieval Stage -- 2.3 Machine Translation via Selective Context -- 2.4 Multi-task Learning Framework -- 3 Evaluation and Datasets -- 3.1 Evaluation -- 3.2 Datasets -- 3.3 Training Details -- 3.4 Baselines -- 3.5 Results -- 4 Analysis -- 5 Related Work -- 6 Conclusion -- References.
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Contrastive Learning for Machine Translation Quality Estimation -- 1 Introduction -- 2 Related Work -- 2.1 Machine Translation Quality Estimation -- 2.2 Contrastive Learning -- 3 Our Method -- 3.1 Denoising Reconstructed Samples -- 3.2 Contrastive Training -- 4 Experiments -- 4.1 Setup -- 4.2 Results and Analysis -- 4.3 Different Methods to Create Negative Samples -- 4.4 Compare with Metric-Based Method -- 5 Conclusion -- References -- Sentence-State LSTMs For Sequence-to-Sequence Learning -- 1 Introduction -- 2 Approach -- 2.1 Sentence-State LSTM Encoder -- 2.2 Comparison with RNNs, CNNs and Transformer -- 2.3 LSTM Decoder -- 2.4 Training -- 3 Experiments -- 3.1 Main Results -- 4 Analysis -- 4.1 Ablation Study -- 4.2 Effect of Recurrent Steps -- 5 Related Work -- 5.1 Seq2seq Modeling -- 5.2 Efficient Sequence Encoding -- 6 Conclusion -- References -- Guwen-UNILM: Machine Translation Between Ancient and Modern Chinese Based on Pre-Trained Models -- 1 Introduction -- 2 Related Work -- 3 The Guwen-UNILM Framework -- 3.1 Pre-training Step -- 3.2 Fine-Tuning Step -- 4 Experiment -- 4.1 Datasets -- 4.2 Experimental Setup -- 4.3 Comparative Models -- 4.4 Evaluation Metrics -- 4.5 Results and Discussion -- 5 Conclusion -- References -- Adaptive Transformer for Multilingual Neural Machine Translation -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Proposed Method -- 4.1 Adaptive Transformer -- 4.2 Adaptive Attention Layer -- 4.3 Adaptive Feed-Forward Layer -- 5 Experiments -- 5.1 Dataset -- 5.2 Model Configurations -- 5.3 Main Results -- 5.4 Ablation Study -- 5.5 Analysis on Shared Rate -- 5.6 Analysis on Low-Resource Language -- 6 Conclusion and Future Work -- References -- Improving Non-autoregressive Machine Translation with Soft-Masking -- 1 Introduction -- 2 Background -- 2.1 Autoregressive Machine Translation.
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2.2 Non-autoregressive Machine Translation -- 3 Method -- 3.1 Encoder -- 3.2 Decoder -- 3.3 Discriminator -- 3.4 Glancing Training -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Main Results -- 4.3 Decoding Speed -- 5 More Analysis -- 6 Related Works -- 7 Conclusion -- References -- Machine Learning for NLP -- AutoNLU: Architecture Search for Sentence and Cross-sentence Attention Modeling with Re-designed Search Space -- 1 Introduction -- 2 Search Space Design -- 2.1 Meta-architectures -- 2.2 Encoder Operations -- 2.3 Aggregator Search Space -- 2.4 Design Choices -- 3 Architecture Search -- 3.1 Search Algorithm -- 3.2 Child Model Training -- 3.3 Improving Weight Sharing -- 3.4 Search Warm-Up -- 4 Experiments and Discussion -- 4.1 Datasets -- 4.2 Architecture Search Protocols -- 4.3 Results -- 4.4 Ablation on Our Strategies -- 4.5 Ablation on Our Search Space -- 5 Conclusion and Future Work -- References -- AutoTrans: Automating Transformer Design via Reinforced Architecture Search -- 1 Introduction -- 2 Related Work -- 3 Search Space Design -- 4 Architecture Search -- 4.1 Search Algorithm -- 4.2 Deriving Architectures -- 4.3 Cross-operation Parameter Sharing -- 4.4 Cross-layer Parameter Sharing -- 5 Experiments and Results -- 5.1 Datasets -- 5.2 Architecture Search Protocols -- 5.3 Main Results -- 5.4 Effects of Proportions of Training Data -- 5.5 Effects of Different Learning Rates on the Learned Architecture -- 5.6 Effects of Learning Rate on Search -- 6 Conclusions and Discussions -- References -- A Word-Level Method for Generating Adversarial Examples Using Whole-Sentence Information -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Selecting Candidate Substitutes -- 3.2 Searching for Adversarial Examples -- 4 Experiments -- 4.1 Setup -- 4.2 Results -- 5 Analysis and Discussions -- 5.1 Ablation Analyses -- 5.2 Effect of Beam Size.
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5.3 Adversarial Training -- 6 Conclusion -- References -- RAST: A Reward Augmented Model for Fine-Grained Sentiment Transfer -- 1 Introduction -- 2 Methodology -- 2.1 Overview -- 2.2 Encoder-Decoder Based Sentiment Transfer Model -- 2.3 Comparative Discriminator -- 2.4 Reward Augmented Training of Sentiment Transfer Model -- 3 Experiments -- 3.1 Experiment Settings -- 3.2 Evaluation Metrics -- 3.3 Results and Analysis -- 3.4 Ablation Study -- 3.5 Case Study -- 4 Related Work -- 5 Conclusion -- References -- Pre-trained Language Models for Tagalog with Multi-source Data -- 1 Introduction -- 2 Related Previous Research -- 2.1 Natural Language Processing for Tagalog -- 2.2 Pre-trained Language Model for Tagalog -- 3 Model -- 3.1 BERT -- 3.2 RoBERTa -- 3.3 ELECTRA -- 4 Pre-training Corpus -- 4.1 Oscar -- 4.2 Wiki -- 4.3 News -- 5 Experiment -- 5.1 Downstream Tasks -- 5.2 Pre-training -- 5.3 Fine-Tuning -- 5.4 Experiment Results and Analysis -- 6 Conclusion -- References -- Accelerating Pretrained Language Model Inference Using Weighted Ensemble Self-distillation -- 1 Introduction -- 2 Weighted Ensemble Self-distillation -- 2.1 Early Exiting -- 2.2 Weighted Ensemble Self-distillation -- 2.3 Adaptive Inference -- 3 Experiments -- 3.1 Datasets and Evaluation Metrics -- 3.2 Baselines -- 3.3 Implementation Details -- 3.4 Comparative Results -- 3.5 Ablation Experiments -- 3.6 The Effect of Weighted Ensemble Self-distillation -- 4 Conclusions -- References -- Information Extraction and Knowledge Graph -- Employing Sentence Compression to Improve Event Coreference Resolution -- 1 Introduction -- 2 Related Work -- 3 Event Coreference Resolution on Sentence Compression -- 3.1 Event Extraction -- 3.2 Event Sentence Compression -- 3.3 Event Coreference Resolution -- 4 Experimentation -- 4.1 Experimental Settings -- 4.2 Results on Event Extraction.
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4.3 Results on Event Coreference Resolution.
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