Linear programming word problems formulation using EnsembleCRF NER labeler and T5 text generator with data augmentations
This work addresses a domain-specific challenge in natural language processing for optimization problems, presenting an incremental improvement over existing approaches.
The authors tackled the problem of automatically labeling entities and generating mathematical representations from linear programming word problems, achieving competitive performance in the NL4Opt competition through ensemble methods.
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.