SMATCH++: Standardized and Extended Evaluation of Semantic Graphs
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arXiv
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866912649995878400 |
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| author | Opitz, Juri |
| author_facet | Opitz, Juri |
| contents | The Smatch metric is a popular method for evaluating graph distances, as is necessary, for instance, to assess the performance of semantic graph parsing systems. However, we observe some issues in the metric that jeopardize meaningful evaluation. E.g., opaque pre-processing choices can affect results, and current graph-alignment solvers do not provide us with upper-bounds. Without upper-bounds, however, fair evaluation is not guaranteed. Furthermore, adaptions of Smatch for extended tasks (e.g., fine-grained semantic similarity) are spread out, and lack a unifying framework.
For better inspection, we divide the metric into three modules: pre-processing, alignment, and scoring. Examining each module, we specify its goals and diagnose potential issues, for which we discuss and test mitigation strategies. For pre-processing, we show how to fully conform to annotation guidelines that allow structurally deviating but valid graphs. For safer and enhanced alignment, we show the feasibility of optimal alignment in a standard evaluation setup, and develop a lossless graph compression method that shrinks the search space and significantly increases efficiency. For improved scoring, we propose standardized and extended metric calculation of fine-grained sub-graph meaning aspects. Our code is available at https://github.com/flipz357/smatchpp |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_06993 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SMATCH++: Standardized and Extended Evaluation of Semantic Graphs Opitz, Juri Computation and Language Artificial Intelligence The Smatch metric is a popular method for evaluating graph distances, as is necessary, for instance, to assess the performance of semantic graph parsing systems. However, we observe some issues in the metric that jeopardize meaningful evaluation. E.g., opaque pre-processing choices can affect results, and current graph-alignment solvers do not provide us with upper-bounds. Without upper-bounds, however, fair evaluation is not guaranteed. Furthermore, adaptions of Smatch for extended tasks (e.g., fine-grained semantic similarity) are spread out, and lack a unifying framework. For better inspection, we divide the metric into three modules: pre-processing, alignment, and scoring. Examining each module, we specify its goals and diagnose potential issues, for which we discuss and test mitigation strategies. For pre-processing, we show how to fully conform to annotation guidelines that allow structurally deviating but valid graphs. For safer and enhanced alignment, we show the feasibility of optimal alignment in a standard evaluation setup, and develop a lossless graph compression method that shrinks the search space and significantly increases efficiency. For improved scoring, we propose standardized and extended metric calculation of fine-grained sub-graph meaning aspects. Our code is available at https://github.com/flipz357/smatchpp |
| title | SMATCH++: Standardized and Extended Evaluation of Semantic Graphs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2305.06993 |