Bootstrapping parsers via syntactic projection across parallel texts

By Rebecca Hwa, Philip Resnik, Amy Weinberg, Clara I. Cabezas, Okan Kolak. Natural Language Engineering, 2005

390 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4e14aa89e2a0d9ac2e6e80f9d618b2fdf07defd4

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In Artificial Intelligence

Citations in the corpus

Edinburgh Research Explorer A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages (positive): “Large annotated treebanks are available for only a tiny fraction of the world’s languages, and there is a wealth of literature on strategies for parsing with few resources (Hwa et al., 2005; Zeman and Resnik, 2008; McDonald et al., 2011; S ø gaard, 2011).”
Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning (2016, background): “Cross-lingual 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…”
Distributed Word Representation Learning for Cross-Lingual Dependency Parsing (2014, methodology): “…Petrov, 2011), improving projection performance by using auxiliary resources such as Wikipedia metadata (Kim and Lee, 2012) or WordNet (Khapra et al., 2010), or boosting projection performance by heuristically modifying or correcting the projected annotations (Hwa et al., 2005; Kim et al., 2010).”
Massively Multilingual Transfer for NER (2019, methodology): “…languages, which has typically taken the form of annotation projection over parallel corpora or other multilingual resources (Yarowsky et al., 2001; Hwa et al., 2005), or making use of trans-ferable representations, such as phonetic transcriptions (Bharadwaj et al., 2016), closely related…”
Posterior Regularization for Structured Latent Variable Models (2010, methodology): “But their importance has grown far beyond machine translation: for instance, transferring annotations between languages by projecting POS taggers, NP chunkers and parsers through word alignment (Yarowsky and Ngai, 2001; Rogati et al., 2003; Hwa et al., 2005; Ganchev et al., 2009); discovery of parap”
JW300: A Wide-Coverage Parallel Corpus for Low-Resource Languages (2019, background): “…many inﬂuential works in projecting sequential labels from multiple source languages (Das and Petrov, 2011; T ¨ ackstr ¨ om et al., 2013), as well as projecting more complex annotations such as syntactic and semantic dependencies (Hwa et al., 2005; Pad´o and Lapata, 2009; Agi´c et al., 2016).”
Improving Vector Space Word Representations Using Multilingual Correlation (2014, background): “…pairs can be treated as evidence that semantics or information at a conceptual level (since both of them basically model word cooccurrence counts) transfers well across languages (Dyvik, 2004) although syntax has been projected across languages as well (Hwa et al., 2005; Yarowsky and Ngai, 2001).”
Covariance in Unsupervised Learning of Probabilistic Grammars (2010, background): “Leveraging linguistic information from one language for the task of disambiguating another language has received considerable attention (Dagan, 1991; Yarowsky et al., 2001; Hwa et al., 2005; Smith and Smith, 2004; Snyder and Barzilay, 2008; Burkett and Klein, 2008).”
Challenges and Strategies in Cross-Cultural NLP (2022, methodology): “Using parallel data and a word alignment tool, annotation (for example a syntactic tree) in a source language can be transferred to a target language without extra annotation (Yarowsky et al., 2001; Hwa et al., 2005).”
English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too (2020, background): “Early work on cross-lingual transfer mostly relies on the availability of parallel data, where one can perform translation (Mayhew et al., 2017) or project annotations from one language into another (Hwa et al., 2005; Agi´c et al., 2016).”
Many Languages, One Parser (2016, background): “, 2001; Hwa et al., 2005) or via automatically-translated sentences (Tiedemann et al., 2014). Ma and Xia (2014) used entropy regularization to learn from both parallel data (with projected annotations) and unlabeled data in the target language.”
A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks (2016, background): “Cross-lingual supervision has proven highly beneﬁcial for parsing low-resource languages (Hwa et al., 2005; McDonald et al., 2011), implying that different languages have a great deal of common ground in grammars.”
Phrase Dependency Machine Translation with Quasi-Synchronous Tree-to-Tree Features (2014, methodology): “The projected parses can be improved by applying manually written rules (Hwa et al. 2005) or modeling the noisy projection process (Ganchev, Gillenwater, and Taskar 2009; Smith and Eisner 2009).”
Universal Dependency Annotation for Multilingual Parsing (2013, background): “In order to overcome these difﬁculties, some cross-lingual studies have resorted to heuristics to homogenize treebanks (Hwa et al., 2005; Smith and Eisner, 2009; Ganchev et al., 2009), but we are only aware of a few systematic attempts to create homogenous syntactic dependency annotation in…”
Low Resource Dependency Parsing: Cross-lingual Parameter Sharing in a Neural Network Parser (2015, background): “In contrast, projection approaches use parallel data to project source language dependency relations to the target language (Hwa et al., 2005).”