This paper establishes a reproducible baseline for identifying metaphor-related words in Chinese text at the token level, following the MIPVU linguistic framework. The author compares three approaches on the PSU Chinese Metaphor Corpus: fine-tuned Chinese RoBERTa, MelBERT (adapted to Chinese with a new basic-meaning resource from a Chinese dictionary covering 71.51% of corpus vocabulary), and instruction-tuned Qwen3.5-9B. MelBERT achieves the best F1 (0.7281), marginally beating plain RoBERTa (0.7142), while Qwen lags by ~11 F1 points (0.6157) due to recall problems from discrete output formatting.
Main takeaways:
- MelBERT's basic-meaning channel (which checks if a word's usage matches its dictionary definition) works for Chinese metaphor detection, unlike the "SPV" channel (checking if novel meanings emerge)
- The generative Qwen model struggles with recall because it must commit to discrete labels rather than producing continuous scores
- Several Qwen task formulations failed due to output format design issues, not model capacity limits
- The author releases all splits, outputs, the Chinese basic-meaning resource (from Modern Chinese Dictionary 7th edition), and training scripts
- Conventional metaphor dominates in Chinese, consistent with why the SPV (novel-meaning) channel doesn't help much