Rebecca Marvin


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Perceptual Models of Machine-Edited Text
Elizabeth Merkhofer | Monica-Ann Mendoza | Rebecca Marvin | John Henderson
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Predicting Twitter User Demographics from Names Alone
Zach Wood-Doughty | Nicholas Andrews | Rebecca Marvin | Mark Dredze
Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media

Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends. These tools often require hundreds of user-authored messages for each user, which may be prohibitive to obtain when analyzing millions of users. We explore character-level neural models that learn a representation of a user’s name and screen name to predict gender and ethnicity, allowing for demographic inference with minimal data. We release trained models1 which may enable new demographic analyses that would otherwise require enormous amounts of data collection

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Exploring Word Sense Disambiguation Abilities of Neural Machine Translation Systems (Non-archival Extended Abstract)
Rebecca Marvin | Philipp Koehn
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation
Brian Thompson | Huda Khayrallah | Antonios Anastasopoulos | Arya D. McCarthy | Kevin Duh | Rebecca Marvin | Paul McNamee | Jeremy Gwinnup | Tim Anderson | Philipp Koehn
Proceedings of the Third Conference on Machine Translation: Research Papers

To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space) and consider each component’s contribution to, and capacity for, domain adaptation. We find that freezing any single component during continued training has minimal impact on performance, and that performance is surprisingly good when a single component is adapted while holding the rest of the model fixed. We also find that continued training does not move the model very far from the out-of-domain model, compared to a sensitivity analysis metric, suggesting that the out-of-domain model can provide a good generic initialization for the new domain.

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Targeted Syntactic Evaluation of Language Models
Rebecca Marvin | Tal Linzen
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present a data set for evaluating the grammaticality of the predictions of a language model. We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence. The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items. We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one. In an experiment using this data set, an LSTM language model performed poorly on many of the constructions. Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM’s accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online. This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model.