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Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks?

Paper · 2021 · Thang Pham, Trung Bui, Long Mai et al.

Summary

The paper asks whether BERT-style classifiers that beat humans on GLUE actually rely on word order. The authors fine-tuned base versions of BERT, RoBERTa and ALBERT on seven GLUE tasks: CoLA, SST-2, QQP, MRPC, STS-B, RTE and QNLI. They then re-tested each model on correctly classified, class-balanced dev examples in which a single sentence had its 1-, 2- or 3-grams randomly permuted. A Word-Order Sensitivity (WOS) score measures how far accuracy falls toward the 50% chance level. Apart from CoLA, the models mostly kept their original predictions on scrambled input, so they behave much like bag-of-words systems. Analysis with LIME and attention shows what they use instead. Sentiment models lean on the polarity of key words, and pair-input models have self-attention heads that link matching tokens across the two inputs regardless of position. When RoBERTa was first fine-tuned on synthetic tasks that force attention to word order, results improved on most GLUE tasks and on SQuAD 2.0. The study suggests GLUE does not really require syntax or deep reasoning.

Key points

  • Between 75% and 90% of correct predictions stayed the same after input words were shuffled. About 65% of gold labels across five GLUE tasks could still be recovered from shuffled sentences.
  • In SST-2, the sentiment of one salient word predicts the label of the whole sentence in at least 60% of cases.
  • WOS is defined as s = (100 − p)/(100 − b), with b = 50. CoLA is the clearly order-sensitive task, followed by RTE among the rest.
  • Extra fine-tuning on synthetic tasks raised WOS by 148–194%. Gains included:
    • RTE's average climbed to 73.21% (up from 72.2%), with the best model reaching 74.73%.
    • SQuAD 2.0 F1 rose from 80.62 to 81.08.
    • SST-2 was the only task that did not improve.
  • RoBERTa remained fairly insensitive to shuffling even on the harder ANLI benchmark, with a WOS of 0.63.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢Adobe SystemsSan Jose · United States14 works · 3,493 citations🎓The University of QueenslandBrisbane · Australia2 works · 4,399 citations🎓Auburn UniversityAuburn · United States2 works · 151 citations

📚 Shelves 4 Computer Science · 9,086 works Artificial Intelligence · 8,889 works Arts and Humanities · 9,296 works Language and Linguistics · 9,206 works

Cite

Cite as Thang Pham, Trung Bui, Long Mai and Anh Nguyen. 2021. Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks?. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.findings-acl.98

@inproceedings{pham2021out,
  title = {{Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks?}},
  author = {Thang Pham, Trung Bui, Long Mai and Anh Nguyen},
  booktitle = {Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021},
  year = {2021},
  doi = {10.18653/v1/2021.findings-acl.98},
  url = {https://doi.org/10.18653/v1/2021.findings-acl.98}
}

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