Erli Meng


Pre-training with Meta Learning for Chinese Word Segmentation
Zhen Ke | Liang Shi | Songtao Sun | Erli Meng | Bin Wang | Xipeng Qiu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Recent researches show that pre-trained models (PTMs) are beneficial to Chinese Word Segmentation (CWS). However, PTMs used in previous works usually adopt language modeling as pre-training tasks, lacking task-specific prior segmentation knowledge and ignoring the discrepancy between pre-training tasks and downstream CWS tasks. In this paper, we propose a CWS-specific pre-trained model MetaSeg, which employs a unified architecture and incorporates meta learning algorithm into a multi-criteria pre-training task. Empirical results show that MetaSeg could utilize common prior segmentation knowledge from different existing criteria and alleviate the discrepancy between pre-trained models and downstream CWS tasks. Besides, MetaSeg can achieve new state-of-the-art performance on twelve widely-used CWS datasets and significantly improve model performance in low-resource settings.


Porous Lattice Transformer Encoder for Chinese NER
Xue Mengge | Bowen Yu | Tingwen Liu | Yue Zhang | Erli Meng | Bin Wang
Proceedings of the 28th International Conference on Computational Linguistics

Incorporating lexicons into character-level Chinese NER by lattices is proven effective to exploitrich word boundary information. Previous work has extended RNNs to consume lattice inputsand achieved great success. However, due to the DAG structure and the inherently unidirectionalsequential nature, this method precludes batched computation and sufficient semantic interaction.In this paper, we propose PLTE, an extension of transformer encoder that is tailored for ChineseNER, which models all the characters and matched lexical words in parallel with batch process-ing. PLTE augments self-attention with positional relation representations to incorporate latticestructure. It also introduces a porous mechanism to augment localness modeling and maintainthe strength of capturing the rich long-term dependencies. Experimental results show that PLTEperforms up to 11.4 times faster than state-of-the-art methods while realizing better performance.We also demonstrate that using BERT representations further substantially boosts the performanceand brings out the best in PLTE.