Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE (2210.16407v1)

Abstract: 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 LLMs can improve their performance beyond standard fine-tuning (3.3% improvement in Acc@60).

• The paper presents a novel DREAM-FLUTE system that integrates scene elaborations to enhance the understanding of textual entailment in figurative language.
• It employs a T5-based architecture with single, two-step, and ensemble configurations to generate contextual explanations and predict logical entailment.
• The approach achieves an Acc@60 of 63.3% and outperforms baseline models, emphasizing its potential for advanced AI language comprehension.

An Overview of "Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE"

The paper "Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE" introduces a novel system that addresses the challenges of understanding figurative language, such as metaphors and similes, by forming a "mental model" of the situations described in the text. This system, named DREAM-FLUTE, employs the existing scene elaboration model, DREAM, to enhance the comprehension and explanation of textual entailment when figurative language is present.

System Architecture and Approach

DREAM-FLUTE aims to tackle the task of recognizing and explaining textual entailment (RTE) in scenarios involving figurative language. The system builds upon the DREAM model, utilizing a T5-based architecture to generate additional contextual elaborations. These elaborations enrich the original text by providing insights into the likely consequences, emotions, motivations, and social norms associated with the scenes described in the premise and hypothesis. This additional context aids in determining whether the hypothesis logically follows from the premise, considering the implicit meanings often embedded in figurative language.

The research introduces several system configurations, with each leveraging different dimensions from DREAM:

1. Single Model Systems:
   • Basic model trained on <Premise, Hypothesis> to predict <Label, Explanation>.
   • Joint prediction model incorporating the type of figurative language.
   • Models using scene elaborations from DREAM as context (emotion, motivation, consequence, social norm, or a combination of all).
2. Two-step System:
   • Separates the tasks of label prediction and explanation generation to assess impact on outcomes.
3. Ensemble System:
   • Implements a cognitive continuum, combining various individual systems to enhance performance by integrating different levels of intuition and analysis.

Evaluation and Results

The evaluation of DREAM-FLUTE was conducted using the FigLang2022 Shared Task data. The system exhibits significant improvements in understanding and explaining figurative language over baseline models by incorporating scene elaborations. Notably, the model configuration utilizing likely consequences demonstrated a leading performance with an impressive Acc@60 of 63.3%, reflecting both high accuracy in entailment labeling and superior explanation quality. Further improvements were observed using an ensemble approach, achieving even better results across various metrics.

Implications and Future Directions

DREAM-FLUTE demonstrates how integrating a reflective component, such as mental modeling with scene elaborations, can significantly enhance the performance of pretrained LLMs in tasks involving complex language constructs. This advancement not only improves practical applications like natural language inference and question answering but also contributes theoretically to the understanding of how AI can mimic some aspects of human cognitive processes in language comprehension.

Future developments in AI could explore further customization of the cognitive continuum approach for diverse domains and tasks, adapting the order and weighting of different contextual insights. Additionally, expanding the framework to handle longer text passages and more nuanced figurative expressions could broaden its applicability and robustness in real-world applications. There is also potential for adapting this approach to other sophisticated NLP tasks that require deep semantic understanding and contextualization, further bridging the gap between human-like understanding and machine processing of language.