# Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation

arXiv: https://arxiv.org/abs/2210.03018 · HTML: https://mlsanity.com/paper/2210.03018/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://mlsanity.com/paper/1907.05791/) (2019)
- [Cross-lingual Abstract Meaning Representation Parsing](https://mlsanity.com/paper/1704.04539/) (2017)
- [Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank](https://mlsanity.com/paper/2010.03662/) (2020)
- [Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity](https://mlsanity.com/paper/2108.11949/) (2021)
- [Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing](https://mlsanity.com/paper/2106.04565/) (2021)
- [Evaluating the Efficacy of Summarization Evaluation across Languages](https://mlsanity.com/paper/2106.01478/) (2021)
- [Toward More Effective Human Evaluation for Machine Translation](https://mlsanity.com/paper/2204.05307/) (2022)
- [Identifying Semantic Divergences in Parallel Text without Annotations](https://mlsanity.com/paper/1803.11112/) (2018)
- [Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation](https://mlsanity.com/paper/2105.15087/) (2021)
- [A Dynamic, Interpreted CheckList for Meaning-oriented NLG Metric Evaluation -- through the Lens of Semantic Similarity Rating](https://mlsanity.com/paper/2205.12176/) (2022)

## Built on by (most cited first, 2 of 2)

- [Can Uniform Meaning Representation Help GPT-4 Translate from Indigenous Languages?](https://mlsanity.com/paper/2502.08900/) (2025)
- [Foundations of Stochastic Lexical Calculus: Semantic Descent and Random Dynamics on Probability Simplices](https://mlsanity.com/paper/2609.20207/) (2026)

## Abstract (arXiv)

Identifying semantically equivalent sentences is important for many cross-lingual and mono-lingual NLP tasks. Current approaches to semantic equivalence take a loose, sentence-level approach to "equivalence," despite previous evidence that fine-grained differences and implicit content have an effect on human understanding (Roth and Anthonio, 2021) and system performance (Briakou and Carpuat, 2021). In this work, we introduce a novel, more sensitive method of characterizing semantic equivalence that leverages Abstract Meaning Representation graph structures. We develop an approach, which can be used with either gold or automatic AMR annotations, and demonstrate that our solution is in fact finer-grained than existing corpus filtering methods and more accurate at predicting strictly equivalent sentences than existing semantic similarity metrics. We suggest that our finer-grained measure of semantic equivalence could limit the workload in the task of human post-edited machine translation and in human evaluation of sentence similarity.