Related papers: Learning compositional structures for semantic graph parsing

Learning compositional structures for semantic graph parsing

URL: http://arxiv.org/abs/2106.04398v1
Date: Tue, 8 Jun 2021 14:20:07 GMT
Title: Learning compositional structures for semantic graph parsing
Authors: Jonas Groschwitz, Meaghan Fowlie and Alexander Koller
Abstract summary: We show how AM dependency parsing can be trained directly on a neural latent-variable model. Our model picks up on several linguistic phenomena on its own and achieves comparable accuracy to supervised training.
Score: 81.41592892863979
License: http://creativecommons.org/licenses/by/4.0/
Abstract: AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accurate across several graphbanks, they require explicit annotations of the compositional tree structures for training. In the past, these were obtained using complex graphbank-specific heuristics written by experts. Here we show how they can instead be trained directly on the graphs with a neural latent-variable model, drastically reducing the amount and complexity of manual heuristics. We demonstrate that our model picks up on several linguistic phenomena on its own and achieves comparable accuracy to supervised training, greatly facilitating the use of AM dependency parsing for new sembanks.

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