Data Augmentation for Morphological Reinflection

By Miikka Silfverberg, Adam Wiemerslage, Ling Liu, L. Mao. 2017

76 citations recorded by the source corpus.

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Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kevin Gimpel
2. Soroush Vosoughi
3. Guangxuan Xu
4. Karen Livescu
5. Ruibo Liu
6. Mohit Bansal
7. Freda Shi
8. Jason Wei

In Computer Vision and Pattern Recognition

1. Kevin Gimpel
2. Karen Livescu
3. Jason Wei
4. Chao Zhang
5. Yue Yu
6. Jonas Kuhn
7. Chenyan Jia
8. Soroush Vosoughi

Citations in the corpus

• Automatically Learning Data Augmentation Policies for Dialogue Tasks (2019, neutral): “…Lemley et al., 2017) and NLP (F¨urstenau and Lapata, 2009; Sen-nrich et al., 2016; Wang and Yang, 2015; Zhang et al., 2015; Jia and Liang, 2016; Kim and Rush, 2016; Hu et al., 2017; Xu et al., 2017; Xia et al., 2017; Silfverberg et al., 2017; Kafle et al., 2017; Hou et al., 2018; Wang et al., 2018).”
• EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks (2019, methodology): “We use 300-dimensional Common-Crawl word embeddings trained us-ing GloVe (Pennington et al., 2014).”
• Ensemble Self-Training for Low-Resource Languages: Grapheme-to-Phoneme Conversion and Morphological Inflection (2020, methodology): “When annotation is difficult to obtain, data augmentation is a common practice to increase training data size with reasonable quality to feed to powerful models (Ragni et al., 2014; Bergmanis et al., 2017; Silfverberg et al., 2017).”
• Substructure Substitution: Structured Data Augmentation for NLP (2021, background): “, 2017; Kobayashi, 2018) or substitution with words having the same morphological features (Silfverberg et al., 2017).”
• Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations (2018, background): “, 2015; Wang and Yang, 2015), grammar induction (Jia and Liang, 2016), task-specific heuristic rules (Fürstenau and Lapata, 2009; Kafle et al., 2017; Silfverberg et al., 2017), or neural decoders of autoencoders (Bergmanis et al.”
• SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup (2020, methodology): “…Zou (2019) utilize heuristic approaches including synonym replancement, random insertion, swap and deletion for text augmentation, Kafle et al. (2017); Silfverberg et al. (2017) employ heuristic rules based on specific task, Hu et al. (2017) pro-pose to augment text data in an encoder-decoder manner.”
• Data Boost: Text Data Augmentation through Reinforcement Learning Guided Conditional Generation (2020, methodology): “Recent work leans towards translation-based methods for augmentation (Fadaee et al., 2017; Silfverberg et al., 2017).”