Search Results for author: Brian Thompson

Found 18 papers, 7 papers with code

Improving Arabic Diacritization by Learning to Diacritize and Translate

no code implementations IWSLT (ACL) 2022 Brian Thompson, Ali Alshehri

We propose a novel multitask learning method for diacritization which trains a model to both diacritize and translate.

Translation

Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity

1 code implementation WMT (EMNLP) 2020 Brian Thompson, Matt Post

Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language (Thompson and Post, 2020); however, attempting to generate paraphrases from such a model using standard beam search produces trivial copies or near copies.

Machine Translation Paraphrase Generation +3

Benchmarking Neural and Statistical Machine Translation on Low-Resource African Languages

no code implementations LREC 2020 Kevin Duh, Paul McNamee, Matt Post, Brian Thompson

In this study, we benchmark state of the art statistical and neural machine translation systems on two African languages which do not have large amounts of resources: Somali and Swahili.

Machine Translation Translation

Simulated Multiple Reference Training Improves Low-Resource Machine Translation

1 code implementation EMNLP 2020 Huda Khayrallah, Brian Thompson, Matt Post, Philipp Koehn

Many valid translations exist for a given sentence, yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings.

Machine Translation Translation

Exploiting Sentence Order in Document Alignment

1 code implementation EMNLP 2020 Brian Thompson, Philipp Koehn

We present a simple document alignment method that incorporates sentence order information in both candidate generation and candidate re-scoring.

Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing

1 code implementation EMNLP 2020 Brian Thompson, Matt Post

We frame the task of machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference.

Frame Machine Translation +1

Vecalign: Improved Sentence Alignment in Linear Time and Space

no code implementations IJCNLP 2019 Brian Thompson, Philipp Koehn

It substantially outperforms the popular Hunalign toolkit at recovering Bible verse alignments in medium- to low-resource language pairs, and it improves downstream MT quality by 1. 7 and 1. 6 BLEU in Sinhala-English and Nepali-English, respectively, compared to the Hunalign-based Paracrawl pipeline.

Machine Translation Sentence Embeddings +1

The JHU Machine Translation Systems for WMT 2018

no code implementations WS 2018 Philipp Koehn, Kevin Duh, Brian Thompson

We report on the efforts of the Johns Hopkins University to develop neural machine translation systems for the shared task for news translation organized around the Conference for Machine Translation (WMT) 2018.

Machine Translation Translation

Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation

1 code implementation WS 2018 Brian Thompson, Huda Khayrallah, Antonios Anastasopoulos, Arya D. McCarthy, Kevin Duh, Rebecca Marvin, Paul McNamee, Jeremy Gwinnup, Tim Anderson, Philipp Koehn

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.

Domain Adaptation Machine Translation +1

Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation

1 code implementation WS 2018 Huda Khayrallah, Brian Thompson, Kevin Duh, Philipp Koehn

Supervised domain adaptation{---}where a large generic corpus and a smaller in-domain corpus are both available for training{---}is a challenge for neural machine translation (NMT).

Domain Adaptation Machine Translation +1

Implicitly-Defined Neural Networks for Sequence Labeling

no code implementations ACL 2017 Michaeel Kazi, Brian Thompson

In this work, we propose a novel, implicitly-defined neural network architecture and describe a method to compute its components.

Part-Of-Speech Tagging

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