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Natural Language Processing and Chinese Computing : 10th CCF International Conference, NLPCC 2021, Qingdao, China, October 13-17, 2021, Proceedings, Part II - Lu Wang

Natural Language Processing and Chinese Computing

10th CCF International Conference, NLPCC 2021, Qingdao, China, October 13-17, 2021, Proceedings, Part II

By: Lu Wang (Editor), Yansong Feng (Editor), Yu Hong (Editor)

Paperback | 10 October 2021

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Posters - Fundamentals of NLP.- Syntax and Coherence - The Effect on Automatic Argument Quality Assessment.- ExperienceGen 1.0: A Text Generation Challenge Which Requires Deduction and Induction Ability.- Machine Translation and Multilinguality.- SynXLM-R: Syntax-enhanced XLM-R in Translation Quality Estimation.- Machine Learning for NLP.- Memetic Federated Learning for Biomedical Natural Language Processing.- Information Extraction and Knowledge Graph.- Event Argument Extraction via a Distance-Sensitive Graph Convolutional Network.- Exploit Vague Relation: An Augmented Temporal Relation Corpus and Evaluation.- Searching Effective Transformer for Seq2Seq Keyphrase Generation.- Prerequisite Learning with Pre-trained Language and Graph Embedding Models.- Summarization and Generation.- Variational Autoencoder with Interactive Attention for Affective Text Generation.- CUSTOM: Aspect-Oriented Product Summarization for E-Commerce.- Question Answering.- FABERT: A Feature Aggregation BERT-Based Model for Document Reranking.- Generating Relevant, Correct and Fluent Answers in Natural Answer Generation.- GeoCQA: A Large-scale Geography-Domain Chinese Question Answering Dataset from Examination.- Dialogue Systems.- Generating Informative Dialogue Responses with Keywords-Guided Networks.- Zero-Shot Deployment for Cross-Lingual Dialogue System.- MultiWOZ 2.3: A multi-domain task-oriented dialogue dataset enhanced with annotation corrections and co-reference annotation.- EmoDialoGPT: Enhancing DialoGPT with Emotion.- Social Media and Sentiment Analysis.- BERT-based Meta-learning Approach with Looking Back for Sentiment Analysis of Literary Book Reviews.- ISWR: an Implicit Sentiment Words Recognition Model Based on Sentiment Propagation.- An Aspect-Centralized Graph Convolutional Network for Aspect-based Sentiment Classification.- NLP Applications and Text Mining.- Capturing Global Informativeness in Open Domain Keyphrase Extraction.- Background Semantic Information Improves VerbalMetaphor Identification.- Multimodality and Explainability.- Towards unifying the explainability evaluation methods for NLP.- Explainable AI Workshop.- Detecting Covariate Drift with Explanations.- A Data-Centric Approach Towards Deducing Bias in Artificial Intelligence Systems for Textual Contexts.- Student Workshop.- Enhancing Model Robustness via Lexical Distilling.- Multi-stage Multi-modal Pre-training for Video Representation.- Nested Causality Extraction on Traffic Accident Texts as Question Answering.- Evaluation Workshop.- MSDF: A General Open-Domain Multi-Skill Dialog Framework.- RoKGDS: A Robust Knowledge Grounded Dialog System.- Enhanced Few-shot Learning with Multiple-Pattern-Exploiting Training.- BIT-Event at NLPCC-2021 Task 3: Subevent Identification via Adversarial Training.- Few-shot Learning for Chinese NLP tasks.- When Few-shot Learning Meets Large-scale Knowledge-enhanced Pre-training: Alibaba at FewCLUE.- TKB²ert: Two-stage Knowledge Infused Behavioral Fine-tuned BERT.- A Unified Information Extraction System Based on Role Recognition and Combination.- A Simple but Effective System for Multi-format Information Extraction.- A Hierarchical Sequence Labeling Model for Argument Pair Extraction.- Distant finetuning with discourse relations for stance classification.- The Solution of Xiaomi AI Lab to the 2021 Language and Intelligence Challenge: Multi-Format Information Extraction Task.- A Unified Platform for Information Extraction with Two-stage Process.- Overview of the NLPCC 2021 Shared Task: AutoIE2.- Task 1 - Argumentative Text Understanding for AI Debater (AIDebater).- Two Stage Learning for Argument Pairs Extraction.- Overview of Argumentative Text Understanding for AI Debater Challenge.- ACE: A Context-Enhanced model for Interactive Argument Pair Identification.- Context-Aware and Data-Augmented Transformer for Interactive Argument Pair Identification.

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