Self-Training for Enhancement and Domain Adaptation of Statistical Parsers Trained on Small Datasets

By Roi Reichart, Ari Rappoport. 2007

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In Natural Language Processing

Citations in the corpus

Toolformer: Language Models Can Teach Themselves to Use Tools (2023, methodology): “Bootstrapping The idea of using self-training and bootstrapping techniques to improve models has been investigated in various contexts, ranging from word sense disambiguation (Yarowsky, 1995), relation extraction (Brin, 1999; Agichtein and Gravano, 2000), parsing (McClosky et al., 2006; Reichart and”
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference (2020, background): “, 2015), parsing (McClosky et al., 2006; Reichart and Rappoport, 2007; Huang and Harper, 2009), machine translation (Hoang et al.”
Revisiting Self-Training for Neural Sequence Generation (2019, methodology): “In the ﬁeld of natural language processing, some early work have successfully applied self-training to word sense disambiguation (Yarowsky, 1995) and parsing (McClosky et al., 2006; Reichart & Rappoport, 2007; Huang & Harper, 2009).”
Demographic Factors Improve Classification Performance (2015, background): “The resulting performance drops have often been addressed via various domain adaptation approaches (Blitzer et al., 2006; Daume III and Marcu, 2006; Reichart and Rappoport, 2007; Chen et al., 2009; Daum´e et al., 2010; Chen et al., 2011; Plank and Moschitti, 2013; Plank et al., 2014; Hovy et al.,…”
Confidence Driven Unsupervised Semantic Parsing (2011, methodology): “Our unsupervised approach follows a self training protocol (Yarowsky, 1995; McClosky et al., 2006; Reichart and Rappoport, 2007b) enhanced with constraints restricting the output space (Chang et al., 2007; Chang et al., 2009).”
Heterogeneous Transfer Learning for Image Clustering via the SocialWeb (2009, background): “Various machine learning strategies have been proposed to address this problem, including semi-supervised learning (Zhu, 2007), domain adaptation (Wu and Dietterich, 2004; Blitzer et al., 2006; Blitzer et al., 2007; Arnold et al., 2007; Chan and Ng, 2007; Daume, 2007; Jiang and Zhai, 2007; Reichart ”
Deep Contextualized Self-training for Low Resource Dependency Parsing (2019, background): “This pattern is in line with previous literature on the impact of training methods designed for the lightly supervised setup, and particularly for selftraining when applied to constituency parsing (Reichart and Rappoport, 2007).”
Neural Structural Correspondence Learning for Domain Adaptation (2016, background): “, 2011b), POS tagging (Schnabel and Schütze, 2013), syntactic parsing (Reichart and Rappoport, 2007; McClosky et al., 2010; Rush et al., 2012) and relation extraction (Jiang and Zhai, 2007; Bollegala et al.”
Strong Baselines for Neural Semi-Supervised Learning under Domain Shift (2018, background): “In parsing it proved successful only with small datasets (Reichart and Rappoport, 2007) or when a generative component is used together with a reranker in high-data conditions (McClosky et al.”
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains (2021, background): “through labeled or unlabeled data) but is yet under-represented (Roark and Bacchiani, 2003; Daumé III and Marcu, 2006; Reichart and Rappoport, 2007; McClosky et al., 2010; Rush et al., 2012; Schnabel and Schütze, 2014).”
Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples (2018, methodology): “Much of this work (McClosky et al., 2006b; Reichart and Rappoport, 2007; Sagae and Tsujii, 2007; Kawahara and Uchimoto, 2008; McClosky et al., 2010; Sagae, 2010; Baucom et al., 2013; Yu et al., 2015) has focused on how to best use co-training (Blum and Mitchell, 1998) or self-training to augment a s”
Neural Unsupervised Domain Adaptation in NLP—A Survey (2020, methodology): “Bootstrapping methods for domain adaptation are well-studied in parsing (McClosky et al., 2006; Reichart and Rappoport, 2007; Yu et al., 2015).”