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        <title>A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing</title>
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        <namePart type="given">Chunchuan</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
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    <abstract>Abstract Meaning Representations (AMR) are a broad-coverage semantic formalism which represents sentence meaning as a directed acyclic graph. To train most AMR parsers, one needs to segment the graph into subgraphs and align each such subgraph to a word in a sentence; this is normally done at preprocessing, relying on hand-crafted rules. In contrast, we treat both alignment and segmentation as latent variables in our model and induce them as part of end-to-end training. As marginalizing over the structured latent variables is infeasible, we use the variational autoencoding framework. To ensure end-to-end differentiable optimization, we introduce a differentiable relaxation of the segmentation and alignment problems. We observe that inducing segmentation yields substantial gains over using a ‘greedy’ segmentation heuristic. The performance of our method also approaches that of a model that relies on the segmentation rules of Lyu and Titov (2018), which were hand-crafted to handle individual AMR constructions.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.714</identifier>
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%0 Conference Proceedings
%T A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing
%A Lyu, Chunchuan
%A Cohen, Shay B.
%A Titov, Ivan
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F lyu-etal-2021-differentiable
%X Abstract Meaning Representations (AMR) are a broad-coverage semantic formalism which represents sentence meaning as a directed acyclic graph. To train most AMR parsers, one needs to segment the graph into subgraphs and align each such subgraph to a word in a sentence; this is normally done at preprocessing, relying on hand-crafted rules. In contrast, we treat both alignment and segmentation as latent variables in our model and induce them as part of end-to-end training. As marginalizing over the structured latent variables is infeasible, we use the variational autoencoding framework. To ensure end-to-end differentiable optimization, we introduce a differentiable relaxation of the segmentation and alignment problems. We observe that inducing segmentation yields substantial gains over using a ‘greedy’ segmentation heuristic. The performance of our method also approaches that of a model that relies on the segmentation rules of Lyu and Titov (2018), which were hand-crafted to handle individual AMR constructions.
%R 10.18653/v1/2021.emnlp-main.714
%U https://aclanthology.org/2021.emnlp-main.714/
%U https://doi.org/10.18653/v1/2021.emnlp-main.714
%P 9075-9091

Markdown (Informal)

[A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing](https://aclanthology.org/2021.emnlp-main.714/) (Lyu et al., EMNLP 2021)

• A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing (Lyu et al., EMNLP 2021)

ACL

• Chunchuan Lyu, Shay B. Cohen, and Ivan Titov. 2021. A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9075–9091, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.