Align-smatch: A Novel Evaluation Method for Chinese Abstract Meaning Representation Parsing based on Alignment of Concept and Relation (2022.lrec-1)
Copied to clipboard
| Challenge: | Abstract Meaning Representation abstracts the meaning of sentences into a single-rooted, acyclic and directed graph. |
| Approach: | They propose to use a metric to evaluate concept alignment and relation alignment to improve Chinese AMR parsing evaluation methods. |
| Outcome: | The proposed method is more robust and compatible with concept alignment and relation alignment and more robust in evaluating arcs. |
Similar Papers
DocAMR: Multi-Sentence AMR Representation and Evaluation (2022.naacl-main)
Copied to clipboard
Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) graphs are compared to gold graphs by the Smatch metric, but lack a well-defined representation and evaluation. |
| Approach: | They propose an algorithm for deriving a unified graph representation using a super-sentential annotation method. |
| Outcome: | The proposed algorithm avoids the pitfalls of over-merging and lacks coherence from under merging. |
AMR Similarity Metrics from Principles (2020.tacl-1)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) graphs are rooted, acyclic, directed, and edge-labeled. |
| Approach: | They propose a canonical Smatch metric that aligns variables of two graphs and assesses triple matches. |
| Outcome: | The proposed metric is more benevolent to only very slight meaning deviations and targets the fulfilment of all established criteria. |
CALAMR: Component ALignment for Abstract Meaning Representation (2024.lrec-main)
Copied to clipboard
| Challenge: | Abstract meaning representation (AMR) graphs represent semantic structure in a syntactic independent way. |
| Approach: | They propose a method for graph alignment that can support summarization and evaluation. |
| Outcome: | The proposed method produces graphs that explain what is summarized through their alignments, which can be used to train graph based summarization learners. |
Anchor and Broadcast: An Efficient Concept Alignment Approach for Evaluation of Semantic Graphs (2024.lrec-main)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) is a sentencelevel formalism designed for English. |
| Approach: | They present an intuitive tool for evaluating graph-based meaning representations . they use an anchor broadcast alignment algorithm that is not subject to local maxima . |
| Outcome: | The proposed tool is highly correlated with the widely used Smatch score, but computation takes only about 40% the time. |
AlignBench: Benchmarking Chinese Alignment of Large Language Models (2024.acl-long)
Copied to clipboard
Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Andrew Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Xiaotao Gu, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang
| Challenge: | Effective evaluation of alignment for emerging Chinese LLMs is still significantly lacking, calling for real-scenario grounded, open-ended, challenging and automatic evaluations tailored for alignment. |
| Approach: | They propose a multi-dimensional benchmark for evaluating LLMs’ alignment in Chinese with 8 main categories, 683 real-scenario rooted queries and corresponding human verified references. |
| Outcome: | The benchmark uses a human-in-the-loop data curation pipeline, 683 real-scenario rooted queries and human verified references. |
SMATCH++: Standardized and Extended Evaluation of Semantic Graphs (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing graph-alignment metrics that measure graph distances are not reliable, we show . metric is spread out and does not provide upper bounds for extended tasks. |
| Approach: | They propose a metric to measure a distance between graphs by aligning nodes and counting matching graph triples. |
| Outcome: | The proposed method reduces search space and improves scoring by reducing the number of errors. |
Semantically Inspired AMR Alignment for the Portuguese Language (2020.emnlp-main)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) parsers require alignment between nodes and words of the sentence. |
| Approach: | They propose to use a more semantically matched word-concept pair to align graphs with words in Portuguese . they performed intrinsic and extrinsic evaluations and found it outperforms the English alignment strategies. |
| Outcome: | The proposed method outperforms the existing methods for English and achieves competitive results with a parser designed for the Portuguese language. |
A Structured Syntax-Semantics Interface for English-AMR Alignment (N18-1)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) annotations do not require explicit mapping between elements of an AMR and the corresponding elements of the sentence that evoke them. |
| Approach: | They devised an expressive framework to align AMR graphs to dependency graphs . their framework explains how 97% of AMR edges are evoked by words or syntax . |
| Outcome: | The proposed framework explains how 97% of AMR edges are evoked by words or syntax. |
AnCast++: Document-Level Evaluation of Graph-based Meaning Representations (2025.findings-acl)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (UMR) is a cross-lingual document-level graph-based representation that extends it to document- level semantic annotations. |
| Approach: | They propose an evaluation metric that unifies evaluation of four distinct sub-structures of UMR. |
| Outcome: | The proposed metric is made available on Github. |
A Survey of AMR Applications (2024.emnlp-main)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation that takes the form of a rooted, directed graph. |
| Approach: | They analyze more than 100 papers which use Abstract Meaning Representation (AMR) they highlight the range of applications for which AMR has been harnessed and techniques for incorporating it . they also highlight broader AMR engineering patterns and outline areas of future work that seem ripe for AMR incorporation. |
| Outcome: | The results highlight the range of applications for which AMR has been harnessed and the techniques for incorporating it into those applications. |