PanParser: a Modular Implementation for Efficient Transition-Based Dependency Parsing

In this paper, we present a straightforward strategy for transferring dependency parsers across languages. The proposed method learns a parser from partially annotated data obtained through the projection of annotations across unambiguous word alignments. It does not rely on any modeling of the reliability of dependency and/or alignment links and is therefore easy to implement and parameter free. Experiments on six languages show that our method is at par with recent algorithmically demanding methods, at a much cheaper computational cost. It can thus serve as a fair baseline for transferring dependencies across languages with the use of parallel corpora.

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“…See(Aufrant and Wisniewski, 2016) for a detailed explanation.2 See Section 3.2 for more details on datasets.…”

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confidence: 99%

Frustratingly Easy Cross-Lingual Transfer for Transition-Based Dependency Parsing

Lacroix

Aufrant²,

Wisniewski

et al. 2016

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…Multi-Purpose Parsers. Other parsers with modular or extensible architectures include Alto (Gontrum et al, 2017), a prototyping tool for new grammar formalisms based on Interpreted Regular Tree Grammars (IRTGs), and PanParser (Aufrant and Wisniewski, 2018), a modular framework for transition-based dependency parsing. In contrast to these two, STEPS is a graph-based dependency parser that focuses on easy configuration of different transformer-based language models and neural architecture variants.…”

Section: Related Workmentioning

confidence: 99%

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

Grünewald¹,

Friedrich²,

Kuhn³

2021

Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing Into Enhanced

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The introduction of pre-trained transformerbased 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 transformerbased 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. 1

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Section: Related Workmentioning

confidence: 99%

Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)

2021

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When learned without exploration, local models for structured prediction tasks are subject to exposure bias and cannot be trained without detailed guidance. Active Imitation Learning (AIL), also known in NLP as Dynamic Oracle Learning, is a general technique for working around these issues by allowing the exploration of different outputs at training time.AIL requires oracle feedback: an oracle is any algorithm which can, given a partial candidate solution and gold annotation, find the correct (minimum loss) next output to produce. This paper describes a general finite state technique for deriving oracles. The technique described is also efficient and will greatly expand the tasks for which AIL can be used.

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PanParser: a Modular Implementation for Efficient Transition-Based Dependency Parsing

Cited by 10 publications

References 33 publications

Frustratingly Easy Cross-Lingual Transfer for Transition-Based Dependency Parsing

Frustratingly Easy Cross-Lingual Transfer for Transition-Based Dependency Parsing

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

Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)

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