Structured Tree Alignment for Evaluation of (Speech) Constituency Parsing

Fuente: arXiv
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Autores principales: Shi, Freda, Gimpel, Kevin, Livescu, Karen
Formato: Preprint
Publicado: 2024
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author Shi, Freda
Gimpel, Kevin
Livescu, Karen
author_facet Shi, Freda
Gimpel, Kevin
Livescu, Karen
contents We present the structured average intersection-over-union ratio (STRUCT-IOU), a similarity metric between constituency parse trees motivated by the problem of evaluating speech parsers. STRUCT-IOU enables comparison between a constituency parse tree (over automatically recognized spoken word boundaries) with the ground-truth parse (over written words). To compute the metric, we project the ground-truth parse tree to the speech domain by forced alignment, align the projected ground-truth constituents with the predicted ones under certain structured constraints, and calculate the average IOU score across all aligned constituent pairs. STRUCT-IOU takes word boundaries into account and overcomes the challenge that the predicted words and ground truth may not have perfect one-to-one correspondence. Extending to the evaluation of text constituency parsing, we demonstrate that STRUCT-IOU can address token-mismatch issues, and shows higher tolerance to syntactically plausible parses than PARSEVAL (Black et al., 1991).
format Preprint
id arxiv_https___arxiv_org_abs_2402_13433
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Structured Tree Alignment for Evaluation of (Speech) Constituency Parsing
Shi, Freda
Gimpel, Kevin
Livescu, Karen
Computation and Language
Data Structures and Algorithms
We present the structured average intersection-over-union ratio (STRUCT-IOU), a similarity metric between constituency parse trees motivated by the problem of evaluating speech parsers. STRUCT-IOU enables comparison between a constituency parse tree (over automatically recognized spoken word boundaries) with the ground-truth parse (over written words). To compute the metric, we project the ground-truth parse tree to the speech domain by forced alignment, align the projected ground-truth constituents with the predicted ones under certain structured constraints, and calculate the average IOU score across all aligned constituent pairs. STRUCT-IOU takes word boundaries into account and overcomes the challenge that the predicted words and ground truth may not have perfect one-to-one correspondence. Extending to the evaluation of text constituency parsing, we demonstrate that STRUCT-IOU can address token-mismatch issues, and shows higher tolerance to syntactically plausible parses than PARSEVAL (Black et al., 1991).
title Structured Tree Alignment for Evaluation of (Speech) Constituency Parsing
topic Computation and Language
Data Structures and Algorithms
url https://arxiv.org/abs/2402.13433