Frustratingly Hard Domain Adaptation for Dependency Parsing

By Mark Dredze, John Blitzer, Partha P. Talukdar, Kuzman Ganchev, João Graça, Fernando C Pereira. 2007

88 citations recorded by the source corpus.

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Citations in the corpus

A Theory of Multiple-Source Adaptation with Limited Target Labeled Data (2020, background): “…of the domain adaptation problem that arises in a variety of di↵erent applications, such as in natural language processing (Blitzer et al., 2007; Dredze et al., 2007; Jiang and Zhai, 2007), speech processing (Gauvain and Lee, 1994; Jelinek, 1997), and computer vision (Leggetter and Woodland,…”
Tagging Performance Correlates with Author Age (2015, background): “Selection biases are often thought of in terms of textual domains, motivating work in domain adaptation of NLP models (Daume III and Marcu, 2006; Ben-David et al., 2007; Daume III, 2007; Dredze et al., 2007; Chen et al., 2009; Chen et al., 2011, inter alia).”
The CoNLL 2007 Shared Task on Dependency Parsing (2007, methodology): “Instead of assigning HEAD and DEPREL in a single step, some systems use a two-stage approach for attaching and labeling dependencies (Chen et al., 2007; Dredze et al., 2007).”
Adaptation Based on Generalized Discrepancy (2019, background): “This problem is known as domain adaptation and arises in a variety of applications such as natural language processing and computer vision (Dredze et al., 2007; Blitzer et al., 2007; Jiang and Zhai, 2007; Leggetter and Woodland, 1995; Mart́ınez, 2002; Hoffman et al., 2014).”
Multi-domain learning by confidence-weighted parameter combination (2010, background): “Dredze et al. (2007) showed how domain adaptation for parsing is difficult when annotation guidelines differ for different domains.”
Domain Adaptation in Regression (2011, background): “But, a more challenging problem of domain adaptation arises in a variety of applications, including natural language processing, speech processing, or computer vision [7, 3, 9, 10, 17, 18, 12].”
Adaptation Algorithm and Theory Based on Generalized Discrepancy (2014, background): “A common problem arising in a variety of applications such as natural language processing and computer vision is that of domain adaptation (Dredze et al., 2007; Blitzer et al., 2007b; Jiang and Zhai, 2007; Leggetter and Woodland, 1995; Rosenfeld, 1996): quite often little or no labeled data from…”