Miguel Ballesteros
8 papers in the PaperMetrix corpus
Papers by this author
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Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
2019 · arXiv (Cornell University)
Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we also combined several AMR-to-text alignments …
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On the evolution of syntactic information encoded by BERT’s contextualized representations
2021
The adaptation of pretrained language models to solve supervised tasks has become a baseline in NLP, and many recent works have focused on studying how linguistic information is encoded in the pretrained sentence representations. Among …
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Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs
2015 · RECERCAT (Consorci de Serveis Universitaris de Catalunya)
We present extensions to a continuousstate dependency parsing method that makes it applicable to morphologically rich languages. Starting with a highperformance transition-based parser that uses long short-term memory (LSTM) recurrent neural networks to learn representations …
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Transition-Based Dependency Parsing with Stack Long Short-Term Memory
2015 · RECERCAT (Consorci de Serveis Universitaris de Catalunya)
Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, Noah A. Smith. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: …
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Neural Architectures for Named Entity Recognition
2016
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, Chris Dyer. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.
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Training with Exploration Improves a Greedy Stack LSTM Parser
2016
We adapt the greedy Stack-LSTM dependency parser of Dyer et al. (2015) to support a training-with-exploration procedure using dynamic oracles(Goldberg and Nivre, 2013) instead of cross-entropy minimization. This form of training, which accounts for model …
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What Do Recurrent Neural Network Grammars Learn About Syntax?
2017
Adhiguna Kuncoro, Miguel Ballesteros, Lingpeng Kong, Chris Dyer, Graham Neubig, Noah A. Smith. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. 2017.
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Neural language models as psycholinguistic subjects: Representations of syntactic state
2019
Richard Futrell, Ethan Wilcox, Takashi Morita, Peng Qian, Miguel Ballesteros, Roger Levy. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and …