Construct a Sentence with Multiple Specified Words

TL;DR

This paper presents a method to fine-tune a BART model for constructing high-quality sentences from arbitrary sets of words, enabling versatile sentence generation and evaluation in NLP.

Contribution

It introduces a novel fine-tuning approach for BART to generate sentences from varying numbers of words, expanding NLP capabilities.

Findings

Model generates high-quality sentences from arbitrary word sets.

The approach works with different numbers of input words.

Potential applications include real-world sentence construction and model evaluation.

Abstract

This paper demonstrates a task to finetune a BART model so it can construct a sentence from an arbitrary set of words, which used to be a difficult NLP task. The training task is making sentences with four words, but the trained model can generate sentences when fewer or more words are provided. The output sentences have high quality in general. The model can have some real-world applications, and this task can be used as an evaluation mechanism for any language model as well.