论文标题
线性编程单词问题使用Ensemblecrf NER标签和带有数据增强的T5文本生成器
Linear programming word problems formulation using EnsembleCRF NER labeler and T5 text generator with data augmentations
论文作者
论文摘要
我们提出了一种合奏方法，以预测线性编程单词问题中的标签。实体识别和含义表示形式是在NL4OPT竞争中解决的两种任务类型。我们提出了ENSEMBLECRF方法，以确定第一个任务的命名实体。我们发现在分析中给定任务的单个模型并没有改善。一组预测模型预测了实体。将生成的结果组合在一起以在Ensemblecrf方法中形成共识结果。我们提出了一个集合文本生成器，以生成第二个任务的表示句子。由于输出的溢出，我们想到将问题分为多个小任务。单个模型根据提示生成不同的表示。将所有生成的文本组合在一起以形成合奏并产生线性编程问题的数学含义。
We propose an ensemble approach to predict the labels in linear programming word problems. The entity identification and the meaning representation are two types of tasks to be solved in the NL4Opt competition. We propose the ensembleCRF method to identify the named entities for the first task. We found that single models didn't improve for the given task in our analysis. A set of prediction models predict the entities. The generated results are combined to form a consensus result in the ensembleCRF method. We present an ensemble text generator to produce the representation sentences for the second task. We thought of dividing the problem into multiple small tasks due to the overflow in the output. A single model generates different representations based on the prompt. All the generated text is combined to form an ensemble and produce a mathematical meaning of a linear programming problem.
