Vine Pruning for Efficient Multi-Pass Dependency Parsing

By Alexander M. Rush, Slav Petrov. North American Chapter of the Association for Computational Linguistics, 2012

321 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4f3807c0342adcbf7dbafa3c94c7ac6ccc8f5888

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Citations in the corpus

X -stance: A Multilingual Multi-Target Dataset for Stance Detection (2020, background): “Given the two languages it is possible to either train two monolingual models or to train a single model in a multi-source setup (McDonald et al., 2011).”
Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog (2018, background): “Early work trained part-of-speech taggers for individual languages and then trained delexicalized dependency parsers (e.g., Zeman and Resnik, 2008; McDonald et al., 2011).”
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization (2020, background): “Early work in this direction focused on single tasks, such as grammar induction (Snyder et al., 2009), part-of-speech (POS) tagging (T¨ackstr¨om et al., 2013), parsing (McDonald et al., 2011), and text classiﬁ-cation (Klementiev et al., 2012).”
Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning (2016, background): “…transfer has proven to be a promising way of inducing parsers for low-resource languages, either through data transfer (Hwa et al., 2005; Tiedemann, 2014; Rasooli and Collins, 2015) or model transfer (McDonald et al., 2011; T¨ackstr¨om et al., 2012; Guo et al., 2015; Zhang and Barzilay, 2015).”
75 Languages, 1 Model: Parsing Universal Dependencies Universally (2019, methodology): “To train the multilingual model, we concatenate all available training sets together, similar to McDonald et al. (2011). Before each epoch, we shuffle all sentences and feed mixed batches of sentences to the network, where each batch may contain sentences from any language or treebank, for a total o”
Efficient Higher-Order CRFs for Morphological Tagging (2013, unknown): “In this paper, we demonstrate that fast and accurate CRF training and tagging is possible for large tagsets of even thousands of tags by approximating the CRF objective function using coarse-to-fine decoding (Charniak and Johnson, 2005; Rush and Petrov, 2012).”
A Bayesian Model for Generative Transition-based Dependency Parsing (2015, background): “Unlexicalised parsing is also considered to be robust for applications such as crosslingual parsing (McDonald et al., 2011).”
Universal Dependency Annotation for Multilingual Parsing (2013, methodology): “To test this, we use a cross-lingual transfer parser similar to that of McDonald et al. (2011).”
Ten Pairs to Tag – Multilingual POS Tagging via Coarse Mapping between Embeddings (2016, background): “The approach is akin to direct transfer commonly applied in parsing (McDonald et al., 2011; Zeman and Resnik, 2008) though often with more information.”
A Survey of Cross-lingual Word Embedding Models (2017, background): “Further, multilingual shared embedding spaces enable multi-source learning and multi-source transfers: this results in a more general model and is less prone to data sparseness (McDonald et al., 2011; Agić, Johannsen, Plank, Alonso, Schluter, & Søgaard, 2016; Guo, Che, Yarowsky, Wang, & Liu, 2016; Z”
Massively Multilingual Word Embeddings (2016, methodology): “While previous work has used hand-engineered features that are cross-linguistically stable as the basis model transfer (Zeman and Resnik, 2008; McDonald et al., 2011; Tsvetkov et al., 2014), automatically learned embeddings offer the promise of better generalization at lower cost (Klementiev et…”
Low Resource Dependency Parsing: Cross-lingual Parameter Sharing in a Neural Network Parser (2015, methodology): “McDonald et al. (2011) and Ma and Xia (2014) exploit both delexicalized parsing and parallel data.”
Approximation-Aware Dependency Parsing by Belief Propagation (2015, unknown): “Following Rush and Petrov (2012), we also include a version of each part-of-speech (POS) tag feature, with the coarse POS tags from Petrov et al. (2012).”
A Universal Part-of-Speech Tagset (2011, background): “…part-of-speech taggers of Das and Petrov (2011) with the grammar induction system of Naseem et al. (2010) – which requires a universal tagset – to produce a completely unsupervised grammar induction system for multiple languages, that does not require gold POS tags in the target language.”