Jason Riesa


2020

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Improving Multilingual Models with Language-Clustered Vocabularies
Hyung Won Chung | Dan Garrette | Kiat Chuan Tan | Jason Riesa
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

State-of-the-art multilingual models depend on vocabularies that cover all of the languages the model will expect to see at inference time, but the standard methods for generating those vocabularies are not ideal for massively multilingual applications. In this work, we introduce a novel procedure for multilingual vocabulary generation that combines the separately trained vocabularies of several automatically derived language clusters, thus balancing the trade-off between cross-lingual subword sharing and language-specific vocabularies. Our experiments show improvements across languages on key multilingual benchmark tasks TyDi QA (+2.9 F1), XNLI (+2.1%), and WikiAnn NER (+2.8 F1) and factor of 8 reduction in out-of-vocabulary rate, all without increasing the size of the model or data.

2019

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Small and Practical BERT Models for Sequence Labeling
Henry Tsai | Jason Riesa | Melvin Johnson | Naveen Arivazhagan | Xin Li | Amelia Archer
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We propose a practical scheme to train a single multilingual sequence labeling model that yields state of the art results and is small and fast enough to run on a single CPU. Starting from a public multilingual BERT checkpoint, our final model is 6x smaller and 27x faster, and has higher accuracy than a state-of-the-art multilingual baseline. We show that our model especially outperforms on low-resource languages, and works on codemixed input text without being explicitly trained on codemixed examples. We showcase the effectiveness of our method by reporting on part-of-speech tagging and morphological prediction on 70 treebanks and 48 languages.

2018

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A Fast, Compact, Accurate Model for Language Identification of Codemixed Text
Yuan Zhang | Jason Riesa | Daniel Gillick | Anton Bakalov | Jason Baldridge | David Weiss
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We address fine-grained multilingual language identification: providing a language code for every token in a sentence, including codemixed text containing multiple languages. Such text is prevalent online, in documents, social media, and message boards. We show that a feed-forward network with a simple globally constrained decoder can accurately and rapidly label both codemixed and monolingual text in 100 languages and 100 language pairs. This model outperforms previously published multilingual approaches in terms of both accuracy and speed, yielding an 800x speed-up and a 19.5% averaged absolute gain on three codemixed datasets. It furthermore outperforms several benchmark systems on monolingual language identification.

2012

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Automatic Parallel Fragment Extraction from Noisy Data
Jason Riesa | Daniel Marcu
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2011

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Feature-Rich Language-Independent Syntax-Based Alignment for Statistical Machine Translation
Jason Riesa | Ann Irvine | Daniel Marcu
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Hierarchical Search for Word Alignment
Jason Riesa | Daniel Marcu
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics