Jonathan Wright


2020

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A Progress Report on Activities at the Linguistic Data Consortium Benefitting the LREC Community
Christopher Cieri | James Fiumara | Stephanie Strassel | Jonathan Wright | Denise DiPersio | Mark Liberman
Proceedings of the 12th Language Resources and Evaluation Conference

This latest in a series of Linguistic Data Consortium (LDC) progress reports to the LREC community does not describe any single language resource, evaluation campaign or technology but sketches the activities, since the last report, of a data center devoted to supporting the work of LREC attendees among other research communities. Specifically, we describe 96 new corpora released in 2018-2020 to date, a new technology evaluation campaign, ongoing activities to support multiple common task human language technology programs, and innovations to advance the methodology of language data collection and annotation.

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Call My Net 2: A New Resource for Speaker Recognition
Karen Jones | Stephanie Strassel | Kevin Walker | Jonathan Wright
Proceedings of the 12th Language Resources and Evaluation Conference

We introduce the Call My Net 2 (CMN2) Corpus, a new resource for speaker recognition featuring Tunisian Arabic conversations between friends and family, incorporating both traditional telephony and VoIP data. The corpus contains data from over 400 Tunisian Arabic speakers collected via a custom-built platform deployed in Tunis, with each speaker making 10 or more calls each lasting up to 10 minutes. Calls include speech in various realistic and natural acoustic settings, both noisy and non-noisy. Speakers used a variety of handsets, including landline and mobile devices, and made VoIP calls from tablets or computers. All calls were subject to a series of manual and automatic quality checks, including speech duration, audio quality, language identity and speaker identity. The CMN2 corpus has been used in two NIST Speaker Recognition Evaluations (SRE18 and SRE19), and the SRE test sets as well as the full CMN2 corpus will be published in the Linguistic Data Consortium Catalog. We describe CMN2 corpus requirements, the telephone collection platform, and procedures for call collection. We review properties of the CMN2 dataset and discuss features of the corpus that distinguish it from prior SRE collection efforts, including some of the technical challenges encountered with collecting VoIP data.

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LanguageARC: Developing Language Resources Through Citizen Linguistics
James Fiumara | Christopher Cieri | Jonathan Wright | Mark Liberman
Proceedings of the LREC 2020 Workshop on "Citizen Linguistics in Language Resource Development"

This paper introduces the citizen science platform, LanguageARC, developed within the NIEUW (Novel Incentives and Workflows) project supported by the National Science Foundation under Grant No. 1730377. LanguageARC is a community-oriented online platform bringing together researchers and “citizen linguists” with the shared goal of contributing to linguistic research and language technology development. Like other Citizen Science platforms and projects, LanguageARC harnesses the power and efforts of volunteers who are motivated by the incentives of contributing to science, learning and discovery, and belonging to a community dedicated to social improvement. Citizen linguists contribute language data and judgments by participating in research tasks such as classifying regional accents from audio clips, recording audio of picture descriptions and answering personality questionnaires to create baseline data for NLP research into autism and neurodegenerative conditions. Researchers can create projects on Language ARC without any coding or HTML required using our Project Builder Toolkit.

2018

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Introducing NIEUW: Novel Incentives and Workflows for Eliciting Linguistic Data
Christopher Cieri | James Fiumara | Mark Liberman | Chris Callison-Burch | Jonathan Wright
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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From ‘Solved Problems’ to New Challenges: A Report on LDC Activities
Christopher Cieri | Mark Liberman | Stephanie Strassel | Denise DiPersio | Jonathan Wright | Andrea Mazzucchi
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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Multi-language Speech Collection for NIST LRE
Karen Jones | Stephanie Strassel | Kevin Walker | David Graff | Jonathan Wright
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The Multi-language Speech (MLS) Corpus supports NIST’s Language Recognition Evaluation series by providing new conversational telephone speech and broadcast narrowband data in 20 languages/dialects. The corpus was built with the intention of testing system performance in the matter of distinguishing closely related or confusable linguistic varieties, and careful manual auditing of collected data was an important aspect of this work. This paper lists the specific data requirements for the collection and provides both a commentary on the rationale for those requirements as well as an outline of the various steps taken to ensure all goals were met as specified. LDC conducted a large-scale recruitment effort involving the implementation of candidate assessment and interview techniques suitable for hiring a large contingent of telecommuting workers, and this recruitment effort is discussed in detail. We also describe the telephone and broadcast collection infrastructure and protocols, and provide details of the steps taken to pre-process collected data prior to auditing. Finally, annotation training, procedures and outcomes are presented in detail.

2015

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From Light to Rich ERE: Annotation of Entities, Relations, and Events
Zhiyi Song | Ann Bies | Stephanie Strassel | Tom Riese | Justin Mott | Joe Ellis | Jonathan Wright | Seth Kulick | Neville Ryant | Xiaoyi Ma
Proceedings of the The 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation

2014

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Transliteration of Arabizi into Arabic Orthography: Developing a Parallel Annotated Arabizi-Arabic Script SMS/Chat Corpus
Ann Bies | Zhiyi Song | Mohamed Maamouri | Stephen Grimes | Haejoong Lee | Jonathan Wright | Stephanie Strassel | Nizar Habash | Ramy Eskander | Owen Rambow
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

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RESTful Annotation and Efficient Collaboration
Jonathan Wright
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

As linguistic collection and annotation scale up and collaboration across sites increases, novel technologies are necessary to support projects. Recent events at LDC, namely the move to a web-based infrastructure, the formation of the Software Group, and our involvement in the NSF LAPPS Grid project, have converged on concerns of efficient collaboration. The underlying design of the Web, typically referred to as RESTful principles, is crucial for collaborative annotation, providing data and processing services, and participating in the Linked Data movement. This paper outlines recommendations that will facilitate such collaboration.

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Collecting Natural SMS and Chat Conversations in Multiple Languages: The BOLT Phase 2 Corpus
Zhiyi Song | Stephanie Strassel | Haejoong Lee | Kevin Walker | Jonathan Wright | Jennifer Garland | Dana Fore | Brian Gainor | Preston Cabe | Thomas Thomas | Brendan Callahan | Ann Sawyer
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The DARPA BOLT Program develops systems capable of allowing English speakers to retrieve and understand information from informal foreign language genres. Phase 2 of the program required large volumes of naturally occurring informal text (SMS) and chat messages from individual users in multiple languages to support evaluation of machine translation systems. We describe the design and implementation of a robust collection system capable of capturing both live and archived SMS and chat conversations from willing participants. We also discuss the challenges recruitment at a time when potential participants have acute and growing concerns about their personal privacy in the realm of digital communication, and we outline the techniques adopted to confront those challenges. Finally, we review the properties of the resulting BOLT Phase 2 Corpus, which comprises over 6.5 million words of naturally-occurring chat and SMS in English, Chinese and Egyptian Arabic.

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New Directions for Language Resource Development and Distribution
Christopher Cieri | Denise DiPersio | Mark Liberman | Andrea Mazzucchi | Stephanie Strassel | Jonathan Wright
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Despite the growth in the number of linguistic data centers around the world, their accomplishments and expansions and the advances they have help enable, the language resources that exist are a small fraction of those required to meet the goals of Human Language Technologies (HLT) for the world’s languages and the promises they offer: broad access to knowledge, direct communication across language boundaries and engagement in a global community. Using the Linguistic Data Consortium as a focus case, this paper sketches the progress of data centers, summarizes recent activities and then turns to several issues that have received inadequate attention and proposes some new approaches to their resolution.

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The Language Application Grid
Nancy Ide | James Pustejovsky | Christopher Cieri | Eric Nyberg | Di Wang | Keith Suderman | Marc Verhagen | Jonathan Wright
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The Language Application (LAPPS) Grid project is establishing a framework that enables language service discovery, composition, and reuse and promotes sustainability, manageability, usability, and interoperability of natural language Processing (NLP) components. It is based on the service-oriented architecture (SOA), a more recent, web-oriented version of the “pipeline” architecture that has long been used in NLP for sequencing loosely-coupled linguistic analyses. The LAPPS Grid provides access to basic NLP processing tools and resources and enables pipelining such tools to create custom NLP applications, as well as composite services such as question answering and machine translation together with language resources such as mono- and multi-lingual corpora and lexicons that support NLP. The transformative aspect of the LAPPS Grid is that it orchestrates access to and deployment of language resources and processing functions available from servers around the globe and enables users to add their own language resources, services, and even service grids to satisfy their particular needs.

2012

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Annotation Trees: LDC’s customizable, extensible, scalable, annotation infrastructure
Jonathan Wright | Kira Griffitt | Joe Ellis | Stephanie Strassel | Brendan Callahan
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In recent months, LDC has developed a web-based annotation infrastructure centered around a tree model of annotations and a Ruby on Rails application called the LDC User Interface (LUI). The effort aims to centralize all annotation into this single platform, which means annotation is always available remotely, with no more software required than a web browser. While the design is monolithic in the sense of handling any number of annotation projects, it is also scalable, as it is distributed over many physical and virtual machines. Furthermore, minimizing customization was a core design principle, and new functionality can be plugged in without writing a full application. The creation and customization of GUIs is itself done through the web interface, without writing code, with the aim of eventually allowing project managers to create a new task without developer intervention. Many of the desirable features follow from the model of annotations as trees, and the operationalization of annotation as tree modification.

2010

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Technical Infrastructure at Linguistic Data Consortium: Software and Hardware Resources for Linguistic Data Creation
Kazuaki Maeda | Haejoong Lee | Stephen Grimes | Jonathan Wright | Robert Parker | David Lee | Andrea Mazzucchi
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Linguistic Data Consortium (LDC) at the University of Pennsylvania has participated as a data provider in a variety of governmentsponsored programs that support development of Human Language Technologies. As the number of projects increases, the quantity and variety of the data LDC produces have increased dramatically in recent years. In this paper, we describe the technical infrastructure, both hardware and software, that LDC has built to support these complex, large-scale linguistic data creation efforts at LDC. As it would not be possible to cover all aspects of LDC’s technical infrastructure in one paper, this paper focuses on recent development. We also report on our plans for making our custom-built software resources available to the community as open source software, and introduce an initiative to collaborate with software developers outside LDC. We hope that our approaches and software resources will be useful to the community members who take on similar challenges.

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The DARPA Machine Reading Program - Encouraging Linguistic and Reasoning Research with a Series of Reading Tasks
Stephanie Strassel | Dan Adams | Henry Goldberg | Jonathan Herr | Ron Keesing | Daniel Oblinger | Heather Simpson | Robert Schrag | Jonathan Wright
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

The goal of DARPA’s Machine Reading (MR) program is nothing less than making the world’s natural language corpora available for formal processing. Most text processing research has focused on locating mission-relevant text (information retrieval) and on techniques for enriching text by transforming it to other forms of text (translation, summarization) ― always for use by humans. In contrast, MR will make knowledge contained in text available in forms that machines can use for automated processing. This will be done with little human intervention. Machines will learn to read from a few examples and they will read to learn what they need in order to answer questions or perform some reasoning task. Three independent Reading Teams are building universal text engines which will capture knowledge from naturally occurring text and transform it into the formal representations used by Artificial Intelligence. An Evaluation Team is selecting and annotating text corpora with task domain concepts, creating model reasoning systems with which the reading systems will interact, and establishing question-answer sets and evaluation protocols to measure progress toward this goal. We describe development of the MR evaluation framework, including test protocols, linguistic resources and technical infrastructure.