Shilin Zhou


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CCL23-Eval 任务2系统报告:基于图融合的自回归和非自回归中文AMR语义分析(System Report for CCL23-Eval Task 2: Autoregressive and Non-autoregressive Chinese AMR Semantic Parsing based on Graph Ensembling)
Yanggan Gu (辜仰淦) | Shilin Zhou (周仕林) | Zhenghua Li (李正华)
Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)

“本文介绍了我们在第二十二届中国计算语言学大会中文抽象语义表示解析评测中提交的参赛系统。抽象语义表示(Abstract Meaning Representation,AMR)以有向无环图的形式表示一个句子的语义。本次评测任务针对中文抽象语义表示(Chinese AMR,CAMR),参赛系统不仅需要对常规的AMR图解析预测,还需要预测CAMR数据特有的概念节点对齐、虚词关系对齐、概念同指。我们同时使用多个自回归模型和多个非自回归模型,然后基于图融合的方法将多个模型输出结果融合起来。最终,我们在两个赛道共六个测试集上取得了五项第一名,一项第二名。”


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Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing
Shilin Zhou | Qingrong Xia | Zhenghua Li | Yu Zhang | Yu Hong | Min Zhang
Proceedings of the 29th International Conference on Computational Linguistics

This paper proposes to cast end-to-end span-based SRL as a word-based graph parsing task. The major challenge is how to represent spans at the word level. Borrowing ideas from research on Chinese word segmentation and named entity recognition, we propose and compare four different schemata of graph representation, i.e., BES, BE, BIES, and BII, among which we find that the BES schema performs the best. We further gain interesting insights through detailed analysis. Moreover, we propose a simple constrained Viterbi procedure to ensure the legality of the output graph according to the constraints of the SRL structure. We conduct experiments on two widely used benchmark datasets, i.e., CoNLL05 and CoNLL12. Results show that our word-based graph parsing approach achieves consistently better performance than previous results, under all settings of end-to-end and predicate-given, without and with pre-trained language models (PLMs). More importantly, our model can parse 669/252 sentences per second, without and with PLMs respectively.

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Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures inside Arguments
Yu Zhang | Qingrong Xia | Shilin Zhou | Yong Jiang | Guohong Fu | Min Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community. Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. Despite ubiquity, they share some intrinsic drawbacks of not considering internal argument structures, potentially hindering the model’s expressiveness. The key challenge is arguments are flat structures, and there are no determined subtree realizations for words inside arguments. To remedy this, in this paper, we propose to regard flat argument spans as latent subtrees, accordingly reducing SRL to a tree parsing task. In particular, we equip our formulation with a novel span-constrained TreeCRF to make tree structures span-aware and further extend it to the second-order case. We conduct extensive experiments on CoNLL05 and CoNLL12 benchmarks. Results reveal that our methods perform favorably better than all previous syntax-agnostic works, achieving new state-of-the-art under both end-to-end and w/ gold predicates settings.