mlsanityData to 14 Oct 2026

CC-Riddle: A Question Answering Dataset of Chinese Character Riddles

arXiv 2206.13778 ↗First posted 28 Jun 2022cs.CLcited by 6

Not on the map. Why?

Abstract (arXiv)

The Chinese character riddle is a unique form of cultural entertainment specific to the Chinese language. It typically comprises two parts: the riddle description and the solution. The solution to the riddle is a single character, while the riddle description primarily describes the glyph of the solution, occasionally supplemented with its explanation and pronunciation. Solving Chinese character riddles is a challenging task that demands understanding of character glyph, general knowledge, and a grasp of figurative language. In this paper, we construct a Chinese Character riddle dataset named CC-Riddle, which covers the majority of common simplified Chinese characters. The construction process is a combination of web crawling, language model generation and manual filtering. In generation stage, we input the Chinese phonetic alphabet, glyph and meaning of the solution character into the generation model, which then produces multiple riddle descriptions. The generated riddles are then manually filtered and the final CC-Riddle dataset is composed of both human-written riddles and these filtered, generated riddles. In order to assess the performance of language models on the task of solving character riddles, we use retrieval-based, generative and multiple-choice QA strategies to test three language models: BERT, ChatGPT and ChatGLM. The test results reveal that current language models still struggle to solve Chinese character riddles. CC-Riddle is publicly available at urlhttps://github.com/pku0xff/CC-Riddle.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 28 Jun 2022.

Builds on

15 of 28 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,031
  5. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  6. 2021CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generationcited by 81
  7. 2021ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Informationcited by 65
  8. 2021RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledgecited by 61
  9. 2016Generating Chinese Classical Poems with RNN Encoder-Decodercited by 16
  10. 2021Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Languagecited by 15
  11. 2021Chinese Character Decomposition for Neural MT with Multi-Word Expressionscited by 11
  12. 2021BiRdQA: A Bilingual Dataset for Question Answering on Tricky Riddlescited by 10
3 more
  1. 2015Component-Enhanced Chinese Character Embeddingscited by 8
  2. 2021GlyphCRM: Bidirectional Encoder Representation for Chinese Character with its Glyphcited by 4
  3. 2021TransCouplet:Transformer based Chinese Couplet Generationcited by 2

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 5.

6 papers.