Search Results for author: Paul McNamee

Found 18 papers, 2 papers with code

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

Tagging Location Phrases in Text

no code implementations LREC 2020 Paul McNamee, James Mayfield, Cash Costello, Caitlyn Bishop, Shelby Anderson

Throughout this time the majority of such work has focused on detection and classification of entities into coarse-grained types like: PERSON, ORGANIZATION, and LOCATION.

Dragonfly: Advances in Non-Speaker Annotation for Low Resource Languages

no code implementations LREC 2020 Cash Costello, Shelby Anderson, Caitlyn Bishop, James Mayfield, Paul McNamee

Dragonfly is an open source software tool that supports annotation of text in a low resource language by non-speakers of the language.

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

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

Platforms for Non-speakers Annotating Names in Any Language

no code implementations ACL 2018 Ying Lin, Cash Costello, Boliang Zhang, Di Lu, Heng Ji, James Mayfield, Paul McNamee

We demonstrate two annotation platforms that allow an English speaker to annotate names for any language without knowing the language.

Using of heterogeneous corpora for training of an ASR system

no code implementations1 Jun 2017 Jan Trmal, Gaurav Kumar, Vimal Manohar, Sanjeev Khudanpur, Matt Post, Paul McNamee

The paper summarizes the development of the LVCSR system built as a part of the Pashto speech-translation system at the SCALE (Summer Camp for Applied Language Exploration) 2015 workshop on "Speech-to-text-translation for low-resource languages".

Large Vocabulary Continuous Speech Recognition Speech Recognition +2

Language-Independent Named Entity Analysis Using Parallel Projection and Rule-Based Disambiguation

no code implementations WS 2017 James Mayfield, Paul McNamee, Cash Costello

The 2017 shared task at the Balto-Slavic NLP workshop requires identifying coarse-grained named entities in seven languages, identifying each entity{'}s base form, and clustering name mentions across the multilingual set of documents.

Named Entity Recognition

Language and Dialect Discrimination Using Compression-Inspired Language Models

no code implementations WS 2016 Paul McNamee

The DSL 2016 shared task continued previous evaluations from 2014 and 2015 that facilitated the study of automated language and dialect identification.

Dialect Identification General Classification +3

Interactive Knowledge Base Population

no code implementations31 May 2015 Travis Wolfe, Mark Dredze, James Mayfield, Paul McNamee, Craig Harman, Tim Finin, Benjamin Van Durme

Most work on building knowledge bases has focused on collecting entities and facts from as large a collection of documents as possible.

Knowledge Base Population

Creating and Curating a Cross-Language Person-Entity Linking Collection

no code implementations LREC 2012 Dawn Lawrie, James Mayfield, Paul McNamee, Douglas Oard

To stimulate research in cross-language entity linking, we present a new test collection for evaluating the accuracy of cross-language entity linking in twenty-one languages.

Entity Linking Knowledge Base Population +1

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