Papers with Chinese corpus

8 papers
HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation (C18-1)

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Challenge: Existing models for conversation systems operate sentences at word-level . word-based models suffer from Unknown Words Issue and Preference Issue .
Approach: They propose a hybrid-level Encoder-Decoder model which utilizes word-level features and character-level ones.
Outcome: The proposed model outperforms non-word-level models in automatic metrics and human annotations on a Chinese corpus.
TED-CDB: A Large-Scale Chinese Discourse Relation Dataset on TED Talks (2020.emnlp-main)

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Challenge: TED-CDB dataset is a unique corpus of spoken discourse in Chinese . TED is based on the concept that discourse relations are grounded in an identifiable set of discourse connectives or Altlex expressions.
Approach: They have created a dataset that annotates TED talks in Chinese . they propose to adapt the dataset to Chinese news text to improve its performance .
Outcome: The TED-CDB dataset can improve the performance of systems for languages other than Chinese . it is adapted to features that are not present in English and can extract discourse semantic features .
CLEEK: A Chinese Long-text Corpus for Entity Linking (2020.lrec-1)

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Challenge: Entity linking is a fundamental task in natural language processing, says nigel kilgstrom . existing corpora for entity linking in china are lacking and deficient, he says . kilsmstrom: a new method for entity disambiguation can be developed for Chinese .
Approach: They build a Chinese corpus of multi-domain long text for entity linking . they evaluate the difficulty of documents with respect to entity linking using a measure .
Outcome: The proposed corpus is based on 100 documents from diverse domains and is publicly accessible.
ConFiguRe: Exploring Discourse-level Chinese Figures of Speech (2022.coling-1)

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Challenge: Figures of speech often deviate from their literal meanings to express deeper semantic implications.
Approach: They propose a concept of figurative unit, which is the carrier of a figure, and build a Chinese corpus for Contextualized Figure Recognition.
Outcome: The proposed model is based on 12 types of figures commonly used in Chinese . it shows that the proposed tasks are challenging for existing models .
CSL: A Large-scale Chinese Scientific Literature Dataset (2022.coling-1)

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Challenge: Existing datasets centered around the English language restrict development of Chinese scientific NLP.
Approach: They present a large-scale Chinese scientific literature dataset based on Chinese papers . they use semi-structured data as a natural annotation for many supervised NLP tasks .
Outcome: The proposed dataset can serve as a Chinese corpus and perform many supervised tasks.
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification (D18-1)

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Challenge: Bank Question corpus is a corpus for sentence semantic equivalence identification (SSEI) because of rich expressions in natural languages, SSEI is really a challenging task.
Approach: They propose to cluster 120,000 question pairs from 1-year online bank custom service logs into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation algorithm to achieve questions with the same intent.
Outcome: The proposed method achieves questions with the same intent by clustering deduplicated questions into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation (AP) algorithm.
A Corpus of Adpositional Supersenses for Mandarin Chinese (2020.lrec-1)

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Challenge: Adpositions are frequent markers of semantic relations, but they are highly ambiguous and vary significantly from language to language.
Approach: They propose to annotate Chinese adpositions in a corpus with all aforementioned supersenses . they adapt a framework that defined a set of supersens according to ostensibly language-independent criteria .
Outcome: The proposed corpus is the first to be broadly annotated with adposition semantics in Chinese . it shows that the supersense categories are well-suited to Chinese adepositions despite syntactic differences from English .
A Simple Yet Effective Corpus Construction Method for Chinese Sentence Compression (2022.lrec-1)

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Challenge: Deletion-based sentence compression has made significant progress in the english language . however, there is a lack of large-scale and high-quality parallel corpus for the Chinese language to train an efficient system.
Approach: They propose to construct a Chinese corpus with 151k pairs of sentences and train extractive and generative neural compression models on the constructed corpus.
Outcome: The proposed method generates high-quality compressed sentences on automatic and human evaluation metrics compared with baselines.

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