mlsanityData to 14 Oct 2026

Construct a Sentence with Multiple Specified Words

arXiv 2206.12565 ↗First posted 25 Jun 2022cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 11 references that are in the map. First posted on arXiv 25 Jun 2022.

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References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  5. 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,488
  6. 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 834
  7. 2017Regularizing Neural Networks by Penalizing Confident Output Distributionscited by 464

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