mlsanityData to 14 Oct 2026

Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE

arXiv 2210.16407 ↗First posted 28 Oct 2022cs.CLcited by 8

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Abstract (arXiv)

Figurative language (e.g., "he flew like the wind") is challenging to understand, as it is hard to tell what implicit information is being conveyed from the surface form alone. We hypothesize that to perform this task well, the reader needs to mentally elaborate the scene being described to identify a sensible meaning of the language. We present DREAM-FLUTE, a figurative language understanding system that does this, first forming a "mental model" of situations described in a premise and hypothesis before making an entailment/contradiction decision and generating an explanation. DREAM-FLUTE uses an existing scene elaboration model, DREAM, for constructing its "mental model." In the FigLang2022 Shared Task evaluation, DREAM-FLUTE achieved (joint) first place (Acc@60=63.3%), and can perform even better with ensemble techniques, demonstrating the effectiveness of this approach. More generally, this work suggests that adding a reflective component to pretrained language models can improve their performance beyond standard fine-tuning (3.3% improvement in Acc@60).

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 11 of its 22 references that are in the map. First posted on arXiv 28 Oct 2022.

Builds on

11 of 22 references.

References · 11

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  5. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  6. 2020Aligning AI With Shared Human Valuescited by 723
  7. 2020Measuring Association Between Labels and Free-Text Rationalescited by 160
  8. 2020Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?cited by 95
  9. 2022FLUTE: Figurative Language Understanding through Textual Explanationscited by 79
  10. 2021Figurative Language in Recognizing Textual Entailmentcited by 26
  11. 2021DREAM: Improving Situational QA by First Elaborating the Situationcited by 18

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Citing papers per year: 2022: 2; 2023: 1; 2024: 4; 2025: 1.

8 papers.