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Semantic Graphs for Syntactic Simplification: A Revisit from the Age of LLM

Peiran Yao, Kostyantyn Guzhva, Denilson Barbosa

TL;DR

This paper reexamines the role of semantic graphs, notably AMR, for syntactic simplification in the era of instruction-following LLMs. It introduces AMRS^3, a lightweight, rule-based AMR-to-text pipeline that decomposes AMR graphs into semantic units and realizes them as simpler sentences, achieving competitive performance with interpretable rules. It further studies AMR as an auxiliary input through Direct AMR Prompting and AMRCoC prompting, showing direct prompting can help and AMRCoC enables explicit symbolic reasoning over AMR graphs with LLMs. The work provides practical insights into combining symbolic meaning representations with LLMs and identifies limitations and future directions such as inflection handling and broader task evaluation.

Abstract

Symbolic sentence meaning representations, such as AMR (Abstract Meaning Representation) provide expressive and structured semantic graphs that act as intermediates that simplify downstream NLP tasks. However, the instruction-following capability of large language models (LLMs) offers a shortcut to effectively solve NLP tasks, questioning the utility of semantic graphs. Meanwhile, recent work has also shown the difficulty of using meaning representations merely as a helpful auxiliary for LLMs. We revisit the position of semantic graphs in syntactic simplification, the task of simplifying sentence structures while preserving their meaning, which requires semantic understanding, and evaluate it on a new complex and natural dataset. The AMR-based method that we propose, AMRS$^3$, demonstrates that state-of-the-art meaning representations can lead to easy-to-implement simplification methods with competitive performance and unique advantages in cost, interpretability, and generalization. With AMRS$^3$ as an anchor, we discover that syntactic simplification is a task where semantic graphs are helpful in LLM prompting. We propose AMRCoC prompting that guides LLMs to emulate graph algorithms for explicit symbolic reasoning on AMR graphs, and show its potential for improving LLM on semantic-centered tasks like syntactic simplification.

Semantic Graphs for Syntactic Simplification: A Revisit from the Age of LLM

TL;DR

This paper reexamines the role of semantic graphs, notably AMR, for syntactic simplification in the era of instruction-following LLMs. It introduces AMRS^3, a lightweight, rule-based AMR-to-text pipeline that decomposes AMR graphs into semantic units and realizes them as simpler sentences, achieving competitive performance with interpretable rules. It further studies AMR as an auxiliary input through Direct AMR Prompting and AMRCoC prompting, showing direct prompting can help and AMRCoC enables explicit symbolic reasoning over AMR graphs with LLMs. The work provides practical insights into combining symbolic meaning representations with LLMs and identifies limitations and future directions such as inflection handling and broader task evaluation.

Abstract

Symbolic sentence meaning representations, such as AMR (Abstract Meaning Representation) provide expressive and structured semantic graphs that act as intermediates that simplify downstream NLP tasks. However, the instruction-following capability of large language models (LLMs) offers a shortcut to effectively solve NLP tasks, questioning the utility of semantic graphs. Meanwhile, recent work has also shown the difficulty of using meaning representations merely as a helpful auxiliary for LLMs. We revisit the position of semantic graphs in syntactic simplification, the task of simplifying sentence structures while preserving their meaning, which requires semantic understanding, and evaluate it on a new complex and natural dataset. The AMR-based method that we propose, AMRS, demonstrates that state-of-the-art meaning representations can lead to easy-to-implement simplification methods with competitive performance and unique advantages in cost, interpretability, and generalization. With AMRS as an anchor, we discover that syntactic simplification is a task where semantic graphs are helpful in LLM prompting. We propose AMRCoC prompting that guides LLMs to emulate graph algorithms for explicit symbolic reasoning on AMR graphs, and show its potential for improving LLM on semantic-centered tasks like syntactic simplification.
Paper Structure (12 sections, 1 figure, 4 tables, 1 algorithm)

This paper contains 12 sections, 1 figure, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Task Setting
  4. AMR for Rule-based Simplification
  5. Rule Set
  6. Implementation
  7. Baselines
  8. Discussion
  9. AMR for LLM-based Simplification
  10. Direct AMR Prompting
  11. AMRCoC Prompting
  12. Conclusion

Figures (1)

  • Figure 1: Three stages of AMRS$^\texttt{\scriptsize{3}}$ : (1) Complex input sentence (top) is parsed into an AMR graph. In the AMR graph, core concepts are highlighted. (2) Subgraphs (three encircled graphs) that correspond to simpler sentences are identified using the subgraph extraction algorithm. (3) The subgraphs are realized into text (three boxes at the bottom) using an AMR-to-text model.