“本文针对基于音节嵌入方式的藏文命名实体识别(TNER)中词汇信息和音节部件信息忽略的问题,提出了基于交叉Transformer架构的MECT-TL模型,融合了藏文音节信息、词汇信息和音节部件信息的多元数据特征。MECT-TL通过平面网络结构将藏文音节与词汇信息结合,并整合音节部件信息,有效提升了藏文实体识别的准确性。实验结果显示,相较于主流的TNER基准模型BiLSTM-CRF,本文模型在F1值上提高了5.14个百分点,与基于Transformer架构的TENER模型相比提高了4.18个百分点。这表明,融合藏文词汇和音节部件信息的方法可以显著提高TNER任务的性能。”

“近年来,立场检测任务受到越来越多的关注,但相关标注数据在范围和规模上都有限,不能有效支撑基于神经网络的立场检测。为此,本文探索在零样本阯少样本场景下生成式语言模型在立场检测任务上的能力。首先,构建了一个全新的面向立场检测的数据集,包含5个主题,共2500个人工标注样例;然后,在此数据集上进行了一系列探索实验,实验结果表明:生成式语言模型在零样本设定下,采用结构化的提示学习表现良好;增加额外信息能够显著提升模型性能;在少样本设定下,提供相同目标的示例能够明显提升模型性能,而不同目标示例产生了负面作用;使用思维链可以显著提升模型性能;受提示学习的启发,微调预训练语言模型进一步论证提供额外信息对立场检测的增益显著。”

“Semantic parsing aims to convert natural language utterances to logical forms. A critical challenge for constructing semantic parsers is the lack of labeled data. In this paper, we propose a data synthesis and iterative refinement framework for neural semantic parsing, which can build semantic parsers without annotated logical forms. We first generate a naive corpus by sampling logic forms from knowledge bases and synthesizing their canonical utterances. Then, we further propose a bootstrapping algorithm to iteratively refine data and model, via a denoising language model and knowledge-constrained decoding. Experimental results show that our approach achieves competitive performance on GEO, ATIS and OVERNIGHT datasets in both unsupervised and semi-supervised data settings.”

Semantic parsing aims to map natural language utterances into structured meaning representations. We present a modular platform, EUSP (Easy-to-Use Semantic Parsing PlatForm), that facilitates developers to build semantic parser from scratch. Instead of requiring a large amount of training data or complex grammar knowledge, in our platform developers can build grammar-based semantic parser or neural-based semantic parser through configure files which specify the modules and components that compose semantic parsing system. A high quality grammar-based semantic parsing system only requires domain lexicons rather than costly training data for a semantic parser. Furthermore, we provide a browser-based method to generate the semantic parsing system to minimize the difficulty of development. Experimental results show that the neural-based semantic parser system achieves competitive performance on semantic parsing task, and grammar-based semantic parsers significantly improve the performance of a business search engine.