“语料库检索工具在语言学研究领域具有举足轻重的地位,对于高效获取信息至关重要。然而,当前国内语料库检索工具在语料库检索语言上缺乏统一标准,尤其支持语料库查询语言(CQL)的中文语料库检索工具相对稀缺。在使用不同分词粒度的语料库工具进行中文语料库检索时,会遇到噪声或数据召回难问题。为应对这些挑战,我们研发了支持多粒度分词的CQL 解析器系统CAMELS:一款支持CQL 语句检索,且兼容多粒度分词,支持非词典词检索的语料库检索引擎。经过多种分词器的测试,该引擎展现出了优异的召回率,并在性能上超越了BlackLab的检索速度,为语言学工作者提供了更加易用、精准的检索工具。”

“尽管大语言模型在自然语言处理任务中取得显著进展,但其在复杂问题推理等领域还面临着认知负荷问题,即大语言模型在推理过程需要记忆并处理大量信息。因此,如何有效地减少语言模型推理过程中的认知负荷,缓解推理过程中可能出现的认知过载是一个亟待解决的问题。对此本文提出了Self-Guide方法,用于增强语言模型的推理能力。该方法通过指引大语言模型生成常识知识和推理指导,让语言模型基于自我规划来增强其推理能力,并通过与推理链结合的方式对模型的推理过程进行校准。与现有方法不同的是,本文在不对大语言模型进行微调或使用外部工具的情况下,显著提升了语言模型的推理性能。实验结果表明,Self-Guide方法在四种常见推理任务上性能显著优于基线方法,同时相比传统的推理链模型,Self-Guide方法在推理能力较弱的模型上也具有良好的泛化性能。通过结合大语言模型的自我规划和推理能力,Self-Guide方法为提升语言模型的推理能力提供了一种新的有效途径。”

“Analyzing long and complicated sentences has always been a priority and challenge in Englishlearning. In order to conduct the parse of these sentences for Chinese English as Second Lan-guage (ESL) learners, we design the English Sentence Pattern Structure (ESPS) based on theSentence Diagramming theory. Then, we automatically construct the English Sentence PatternStructure Treebank (ESPST) through the method of rule conversion based on constituency struc-ture and evaluate the conversion results. In addition, we set up two comparative experiments,using trained parser and large language models (LLMs). The results prove that the rule-basedconversion approach is effective.”

“This paper introduces a novel crowdsourcing worker selection algorithm, enhancing annotationquality and reducing costs. Unlike previous studies targeting simpler tasks, this study con-tends with the complexities of label interdependencies in sequence labeling. The proposedalgorithm utilizes a Combinatorial Multi-Armed Bandit (CMAB) approach for worker selec-tion, and a cost-effective human feedback mechanism. The challenge of dealing with imbal-anced and small-scale datasets, which hinders offline simulation of worker selection, is tack-led using an innovative data augmentation method termed shifting, expanding, and shrink-ing (SES). Rigorous testing on CoNLL 2003 NER and Chinese OEI datasets showcased thealgorithm’s efficiency, with an increase in F1 score up to 100.04% of the expert-only base-line, alongside cost savings up to 65.97%. The paper also encompasses a dataset-independenttest emulating annotation evaluation through a Bernoulli distribution, which still led to animpressive 97.56% F1 score of the expert baseline and 59.88% cost savings. Furthermore,our approach can be seamlessly integrated into Reinforcement Learning from Human Feed-back (RLHF) systems, offering a cost-effective solution for obtaining human feedback. All re-sources, including source code and datasets, are available to the broader research community athttps://github.com/blcuicall/nlp-crowdsourcing.”

“基于自然语言生成技术的聊天机器人ChatGPT能够快速生成回答,但目前尚未对机器作答所使用的语言与人类真实语言在哪些方面存在差异进行充分研究。本研究提取并计算159个语言特征在人类和ChatGPT对中文开放域问题作答文本中的分布,使用随机森林、逻辑回归和支持向量机(SVM)三种机器学习算法训练人工智能探测器,并评估模型性能。实验结果表明,随机森林和SVM均能达到较高的分类准确率。通过对比分析,研究揭示了两种文本在描述性特征、字词常用度、字词多样性、句法复杂性、语篇凝聚力五个维度上语言表现的优势和不足。结果显示,两种文本之间的差异主要集中在描述性特征、字词常用度、字词多样性三个维度。”

“Language teachers spend a lot of time developing good examples for language learners. For this reason, we define a new task for language learning, lexical complexity controlledsentence generation, which requires precise control over the lexical complexity in thekeywords to examples generation and better fluency and semantic consistency. The chal-lenge of this task is to generate fluent sentences only using words of given complexitylevels. We propose a simple but effective approach for this task based on complexityembedding while controlling sentence length and syntactic complexity at the decodingstage. Compared with potential solutions, our approach fuses the representations of theword complexity levels into the model to get better control of lexical complexity. Andwe demonstrate the feasibility of the approach for both training models from scratch andfine-tuning the pre-trained models. To facilitate the research, we develop two datasetsin English and Chinese respectively, on which extensive experiments are conducted. Ex-perimental results show that our approach provides more precise control over lexicalcomplexity, as well as better fluency and diversity.”