# Incorporating Sememes into Chinese Definition Modeling

arXiv: https://arxiv.org/abs/1905.06512 · HTML: https://mlsanity.com/paper/1905.06512/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

## Built on by (most cited first, 13 of 13)

- [Multitasking Framework for Unsupervised Simple Definition Generation](https://mlsanity.com/paper/2203.12926/) (2022)
- [Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings](https://mlsanity.com/paper/2205.13858/) (2022)
- [Understanding Jargon: Combining Extraction and Generation for Definition Modeling](https://mlsanity.com/paper/2111.07267/) (2021)
- [Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning](https://mlsanity.com/paper/2306.06058/) (2023)
- [Fine-grained Contrastive Learning for Definition Generation](https://mlsanity.com/paper/2210.00543/) (2022)
- [BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling](https://mlsanity.com/paper/2204.07701/) (2022)
- [DORE: A Dataset For Portuguese Definition Generation](https://mlsanity.com/paper/2403.18018/) (2024)
- [Can Large Language Models Understand Internet Buzzwords Through User-Generated Content](https://mlsanity.com/paper/2505.15071/) (2025)
- [IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations](https://mlsanity.com/paper/2205.06840/) (2022)
- [COMPILING: A Benchmark Dataset for Chinese Complexity Controllable Definition Generation](https://mlsanity.com/paper/2209.14614/) (2022)
- [Vec2Gloss: definition modeling leveraging contextualized vectors with Wordnet gloss](https://mlsanity.com/paper/2305.17855/) (2023)
- [LitMind Dictionary: An Open-Source Online Dictionary](https://mlsanity.com/paper/2204.11087/) (2022)
- [Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncoder](https://mlsanity.com/paper/2506.20083/) (2025)

## Abstract (arXiv)

Chinese definition modeling is a challenging task that generates a dictionary definition in Chinese for a given Chinese word. To accomplish this task, we construct the Chinese Definition Modeling Corpus (CDM), which contains triples of word, sememes and the corresponding definition. We present two novel models to improve Chinese definition modeling: the Adaptive-Attention model (AAM) and the Self- and Adaptive-Attention Model (SAAM). AAM successfully incorporates sememes for generating the definition with an adaptive attention mechanism. It has the capability to decide which sememes to focus on and when to pay attention to sememes. SAAM further replaces recurrent connections in AAM with self-attention and relies entirely on the attention mechanism, reducing the path length between word, sememes and definition. Experiments on CDM demonstrate that by incorporating sememes, our best proposed model can outperform the state-of-the-art method by +6.0 BLEU.