Applying Occam's Razor to Transformer-Based Dependency Parsing: What Works, What Doesn't, and What is Really Necessary

• URL: http://arxiv.org/abs/2010.12699v3
• Date: Thu, 29 Jul 2021 12:30:13 GMT
• Title: Applying Occam's Razor to Transformer-Based Dependency Parsing: What Works, What Doesn't, and What is Really Necessary
• Authors: Stefan Gr\"unewald, Annemarie Friedrich, Jonas Kuhn
• Abstract summary: We study the choice of pre-trained embeddings and whether they use LSTM layers in graph-based dependency schemes. We propose a simple but widely applicable architecture and configuration, achieving new state-of-the-art results (in terms of LAS) for 10 out of 12 diverse languages.
• Score: 9.347252855045125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The introduction of pre-trained transformer-based contextualized word embeddings has led to considerable improvements in the accuracy of graph-based parsers for frameworks such as Universal Dependencies (UD). However, previous works differ in various dimensions, including their choice of pre-trained language models and whether they use LSTM layers. With the aims of disentangling the effects of these choices and identifying a simple yet widely applicable architecture, we introduce STEPS, a new modular graph-based dependency parser. Using STEPS, we perform a series of analyses on the UD corpora of a diverse set of languages. We find that the choice of pre-trained embeddings has by far the greatest impact on parser performance and identify XLM-R as a robust choice across the languages in our study. Adding LSTM layers provides no benefits when using transformer-based embeddings. A multi-task training setup outputting additional UD features may contort results. Taking these insights together, we propose a simple but widely applicable parser architecture and configuration, achieving new state-of-the-art results (in terms of LAS) for 10 out of 12 diverse languages.

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