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We propose an ensemble approach to predict the labels in linear programmingword problems. The entity identification and the meaning representation are twotypes of tasks to be solved in the NL4Opt competition. We propose theensembleCRF method to identify the named entities for the first task. We foundthat single models didn't improve for the given task in our analysis. A set ofprediction models predict the entities. The generated results are combined toform a consensus result in the ensembleCRF method. We present an ensemble textgenerator to produce the representation sentences for the second task. Wethought of dividing the problem into multiple small tasks due to the overflowin the output. A single model generates different representations based on theprompt. All the generated text is combined to form an ensemble and produce amathematical meaning of a linear programming problem.

Linear programming word problems formulation using EnsembleCRF NER labeler and T5 text generator with data augmentations