Glyce: Glyph-vectors for Chinese Character Representations
Authors: Yuxian Meng, Wei Wu, Fei Wang, Xiaoya Li, Ping Nie, Fan Yin, Muyu Li, Qinghong Han, Xiaofei Sun, Jiwei Li
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that glyph-based models are able to consistently outperform word/char ID-based models in a wide range of Chinese NLP tasks. We are able to set new stateof-the-art results for a variety of Chinese NLP tasks, including tagging (NER, CWS, POS), sentence pair classification, single sentence classification tasks, dependency parsing, and semantic role labeling. |
| Researcher Affiliation | Industry | Shannon.AI {yuxian meng, wei wu, fei wang, xiaoya li, ping nie, fan yin, muyu li, qinghong han, xiaofei sun, jiwei li}@shannonai.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | code is available at https://github.com/Shannon AI/glyce. |
| Open Datasets | Yes | NER For the task of Chinese NER, we used the widely-used Onto Notes, MSRA, Weibo and resume datasets. We used the widely-used PKU, MSR, CITYU and AS benchmarks from SIGHAN 2005 bake-off for evaluation. We use the CTB5, CTB9 and UD1 (Universal Dependencies) benchmarks to test our models. We employ the following four different datasets: (1) BQ (binary classification task) [Bowman et al., 2015]; (2) LCQMC (binary classification task) [Liu et al., 2018], (3) XNLI (three-class classification task) [Williams and Bowman], and (4) NLPCC-DBQA. Datasets that we use include: (1) Chn Senti Corp (binary classification); (2) the Fudan corpus (5-class classification) [Li, 2011]; and (3) Ifeng (5-class classification). For dependency parsing [Chen and Manning, 2014, Dyer et al., 2015], we used the widely-used Chinese Penn Treebank 5.1 dataset for evaluation. For the task of semantic role labeling (SRL) [Roth and Lapata, 2016, Marcheggiani and Titov, 2017, He et al., 2018], we used the Co NLL-2009 shared-task. |
| Dataset Splits | Yes | To enable apples-to-apples comparison, we perform grid parameter search for both baselines and the proposed model on the dev set. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | No | The paper mentions performing a 'grid parameter search' but does not provide concrete hyperparameter values or detailed training configurations within the main text. |