Search Results for author: Pamela Shapiro

Found 7 papers, 1 papers with code

JHU System Description for the MADAR Arabic Dialect Identification Shared Task

no code implementations WS 2019 Tom Lippincott, Pamela Shapiro, Kevin Duh, Paul McNamee

Our submission to the MADAR shared task on Arabic dialect identification employed a language modeling technique called Prediction by Partial Matching, an ensemble of neural architectures, and sources of additional data for training word embeddings and auxiliary language models.

Dialect Identification Language Modelling +1

Comparing Pipelined and Integrated Approaches to Dialectal Arabic Neural Machine Translation

no code implementations WS 2019 Pamela Shapiro, Kevin Duh

When translating diglossic languages such as Arabic, situations may arise where we would like to translate a text but do not know which dialect it is.

Dialect Identification Machine Translation +1

Character-Aware Decoder for Translation into Morphologically Rich Languages

no code implementations WS 2019 Adithya Renduchintala, Pamela Shapiro, Kevin Duh, Philipp Koehn

Neural machine translation (NMT) systems operate primarily on words (or sub-words), ignoring lower-level patterns of morphology.

Machine Translation NMT +1

BPE and CharCNNs for Translation of Morphology: A Cross-Lingual Comparison and Analysis

no code implementations5 Sep 2018 Pamela Shapiro, Kevin Duh

Neural Machine Translation (NMT) in low-resource settings and of morphologically rich languages is made difficult in part by data sparsity of vocabulary words.

Machine Translation NMT +1

Hard Non-Monotonic Attention for Character-Level Transduction

2 code implementations EMNLP 2018 Shijie Wu, Pamela Shapiro, Ryan Cotterell

We compare soft and hard non-monotonic attention experimentally and find that the exact algorithm significantly improves performance over the stochastic approximation and outperforms soft attention.

Hard Attention Image Captioning

Morphological Word Embeddings for Arabic Neural Machine Translation in Low-Resource Settings

no code implementations WS 2018 Pamela Shapiro, Kevin Duh

Neural machine translation has achieved impressive results in the last few years, but its success has been limited to settings with large amounts of parallel data.

Machine Translation NMT +3

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