Challenge: Chinese couplet generation aims to generate a pair of clauses with certain rules adhered . previous studies have focused on learning the correspondences between antecedent and subsequent clauses .
Approach: They propose to leverage syntactic information to generate Chinese couplets by POS tags and word dependencies.
Outcome: The proposed approach outperforms baselines on a Chinese couplet generation dataset.

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Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics (2022.aacl-short)

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Challenge: Current neural models for Chinese story generation struggle to generate high-quality long text narratives due to ambiguity in syntactically parsing the Chinese language.
Approach: They propose a framework that enhances the feature capturing mechanism by informing the generation model of dependencies between words and additionally augmenting the semantic representation learning through synonym denoising training.
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Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark (2024.lrec-main)

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Challenge: Compared with sentence-level topic structure, paragraph-level topics can grasp and understand the context of a document from a higher level.
Approach: They propose a hierarchical paragraph-level topic structure representation with three layers to guide corpus construction.
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Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge (2020.acl-main)

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Challenge: Chinese word segmentation and part-of-speech tagging are important fundamental tasks in natural language processing.
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Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation (2021.naacl-main)

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Challenge: Existing definition generation methods take the source word as an indecomposable semantic unit, but in parataxis languages like Chinese, word meanings can be composed using the word formation process.
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MCTS: A Multi-Reference Chinese Text Simplification Dataset (2024.lrec-main)

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Challenge: Existing studies on text simplification systems have focused on unsupervised methods due to the limited evaluation data in language and domain.
Approach: They propose a Chinese text simplification dataset that provides a detailed analysis and an annotation process.
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Elaboration-Generating Commonsense Question Answering at Scale (2023.acl-long)

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Challenge: elaborations are generated using language models that generate background knowledge that helps improve performance . human evaluations show that the quality of the generated ellaborations is high .
Approach: They propose to finetune smaller language models to generate useful intermediate context . they compare a language model with an answer predictor and generate elaborations . human evaluations show that the quality of the generated ellaborations is high .
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CSSWiki: A Chinese Sentence Simplification Dataset with Linguistic and Content Operations (2024.lrec-main)

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Challenge: Existing datasets for sentence simplification focus on English, but limited in Chinese . SS tasks are aimed at improving readability and making sentences more accessible for readers .
Approach: They propose an open-source dataset for Chinese sentence simplification based on Wikipedia . they analyze differences in annotation scheme and data statistics between datasets .
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Automatic Comment Generation for Chinese Student Narrative Essays (2022.emnlp-demos)

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Challenge: Existing studies focus on giving discrete scores for holistic quality or distinct traits, but real-world teachers usually provide detailed comments in natural language, which are more informative than single scores.
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Neural Chinese Address Parsing (N19-1)

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Challenge: Recent research shows that systems that perform address parsing can be useful for building e-commerce or product recommendation systems.
Approach: They propose a task of parsing Chinese addresses into semantically meaningful chunks using a linear-chain structure.
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Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation (2020.acl-main)

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Challenge: Question Generation is a simple syntactic transformation but many aspects of semantics influence what questions are good to form.
Approach: They propose a set of syntactic rules which transform declarative sentences into question-answer pairs.
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