Chinese Computational Linguistics 23rd China National Conference, CCL 2024, Taiyuan, China, July 25–28, 2024, Proceedings

This book constitutes the refereed proceedings of the 23rd China National Conference on Chinese Computational Linguistics, CCL 2024, held in Taiyuan, China, during July 25–28, 2024.

The 34 full papers included in this book were carefully reviewed and selected from 320 submissions. They were organized in topical sections as follows: Information Retrieval, Text Classification and QA; Text Generation, Dialogue and Summarization; Machine Translation and Multilingual Information Processing; Knowledge Graph and Information Extraction; Social Computing and Sentiment Analysis; NLP Applications; Fundamental Theory and Method of Language Computing and Cognition; Language Resource and Evaluation; and Large Language Models.

.- Information Retrieval, Text Classification and QA.
.- Enhancing Free-Form Table Question Answering Models by Distilling Relevant-Cell-Based Rationales.
.- Enhangcing Sequence Representation for Personalized Search.
.- Joint Similarity Guidance Hash Coding Based on Adaptive Weight Mixing Strategy For Cross-Modal Retrieval.
.- Text Generation, Dialogue and Summarization.
.- Generate-then-Revise: An Effective Synthetic Training Data Generation framework for Event Detection.
.- Machine Translation and Multilingual Information Processing.
.- E³: Optimizing Language Model Training for Translation via Enhancing Efficiency and Effectiveness.
.- Multi-features Enhanced Multi-task Learning for Vietnamese Treebank Conversion.
.- SimCLNMT: A Simple Contrastive Learning Method for Enhancing Neural Machine Translation Quality.
.- Translate-and-Revise: Boosting Large Language Models for Constrained Translation.
.- Knowledge Graph and Information Extraction.
.- A Multi-Task Biomedical Named Entity Recognition Method Based on Data Augmentation.
.- Biomedical Event Causal Relation Extraction by Reasoning Optimal Entity Relation Path.
.- Joint Entity and Relation Extraction Based on Bidirectional Update and Long-Term Memory Gate Mechanism.
.- MFE-NER:Multi-feature Fusion Embedding for Chinese Named Entity Recognition.
.- UDAA: An Unsupervised Domain Adaptation Adversarial Learning Framework for Zero-Resource Cross-Domain Named Entity Recognition.
.- Social Computing and Sentiment Analysis.
.- Triple-view Event Hierarchy Model for Biomedical Event Representation.
.- NLP Applications.
.- DialectMoE: An End-to-End Multi-Dialect Speech Recognition Model with Mixture-of-Experts.
.- Distinguishing Neural Speech Synthesis Models Through Fingerprints in Speech Waveforms.
.- Knowledge Graph-Enhanced Recommendation with Box Embeddings.
.- Readability-guided Idiom-aware Sentence Simplification(RISS) for Chinese.
.- Fundamental Theory and Method of Language Computing and Cognition.
.- A Tone-based Hierarchical Structure of Chinese Prosody.
.- Linguistic Guidance for Sequence-to-Sequence AMR Parsing.
.- Language Resource and Evaluation.
.- Automatic Construction of the English Sentence Pattern Structure Treebank for Chinese ESL learners.
.- Cost-efficient Crowdsourcing for Span-based Sequence Labeling:Worker Selection and Data Augmentation.
.- DLUE: Benchmarking Document Language Understanding.
.- Do Large Language Models Understand Conversational Implicature- A case study with a Chinese sitcom.
.- EmoFake: An Initial Dataset for Emotion Fake Audio Detection.
.- Going Beyond Passages:Readability Assessment for Book-level Long Texts.
.- Mitigating the Bias of Large Language Model Evaluation.
.- PPDAC: A Plug-and -Play Data Augmentation Component for Few-shot Extractive Question Answering.
.- Sentence-Space Metrics (SSM) for the Evaluation of Sentence Comprehension.
.- Large Language Models.
.- AuditWen: An Open-Source Large Language Model for Audit.
.- Chinese Grammatical Error Correction via Large Language Model Guided Optimization Training.
.- Pattern Shifting or Knowledge Losing? A Forgetting Perspective for Understanding the Effect of Instruction Fine-Tuning.
.- Prior Constrains-based Reward Model Training for Aligning Large Language Models.
.- Prompt Engineering 101 Prompt Engineering Guidelines from a Linguistic Perspective.