1. No category

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General
Introduction
to Needs in
the Field Now:
Needs of
multilingualism in
language acquisition
María
Blume
Isabelle
Barriere
Cristina
Dye
Carissa
Kang
LSA
& Jonathan Linguistic
Masci
Summer
for the VCLA Institute 2015
U. Of Chicago
Development of Linguistic Linked Open Data
(LLOD) Resources for Collaborative DataIntensive Research in the Language Sciences
Saturday, July 25th, 2015
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Main goals
Requirements for conducting research
with multilingual populations.
Challenges
for the development of
Linked Open Data (LOD) in the field of
multilingual acquisition.
Capacities and needs of any primary
research tool that would allow us to
achieve the vision of LOD.
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Importance of research with
multilingual populations
Multilinguals
constitute the majority of
the world population and a growing
proportion of the US population (e.g.
McCabe et al., 2013)
 Theories of language and cognitive
models of language development must
account for language use, processing and
acquisition in multilinguals.
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Main methodological issues
i.
Criteria for participant recruitement.
ii.
Assessment of the degree of
multilingualism of the participants.
iii.
Issues of working with multilingual
data.
Challenges
for the data markup that
must become part of the metadata and
the data which is the object of study.
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Problems
 There is no clear single definition of bilingualism, (Grosjean
2010, Mackey 2012)
 The amount of individual variation in the many factors that
affect language acquisition and use makes it very difficult to
compare participants.
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Problems
 Nevertheless, research on bilingualism (its cognitive
effects, its relationship with literacy, comparisons
with L1 acquisition), are based on having
established the nature of the participant’s
bilingualism.
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Participant recruitement
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Participant recruitement
Main issue: Large variability of bilingual
speakers.
Need to classify them so that we know
what data are comparable.
Need to gather extensive metadata
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Typical situations that involve
bilingualism


Group A: Children who are
bilingual from birth either
having two languages at
home or one at home and
one outside the home
Group B: Early bilinguals
who start learning a second
language sometime after
birth, normally having one
language at home and one
outside of home.

Group C: People who learned a
second language in adulthood
and use it for work-related
activities,

Group D: Immigrants who must
learn a second language to
survive in the new country or
speakers of a minority language
in their own country who must
learn the dominant language.
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Main issues related to choosing and
classifying bilingual speakers
•
Insufficient factors are considered when
bilingual speakers are selected for a study (e.g.
speakers are classified according to age of
acquisition, but patterns of use are not
considered).
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Main issues related to choosing and
classifying bilingual speakers
•
Different researchers rely on different criteria
when selecting the participants of their study
and use various assessment tools to collect
information about the speaker’s language
proficiency and language history. This causes
that different groups of bilinguals are analyzed
in different studies. (Grosjean, 2008).
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Main issues related to choosing and
classifying bilingual speakers
•
There are no clear standards for classifying
bilingual/L2 speakers across studies, and
therefore it is impossible to tell if the results of
a given study will generalize to other groups of
bilingual/L2 speakers.
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Possible Solutions
Researchers should provide as much
information as possible about the
participants in their studies, the criteria
used to classify bilinguals in different
groups and the language assessment
tools used in the study.
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What should one document?
Grosjean 2008
•
Language history and language relationship:
Which languages were acquired (when and how)? What
was the pattern of language use?
• Language stability: Are one or several languages still
being acquired? Has a certain language stability been
reached?
• Function of languages: Which languages are used for
what purposes, in what context and to what extent?
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What should one document?
Grosjean 2008
•
Language proficiency: What is the bilinguals
proficiency in the four skills in each language?
• Language modes: How often and for how long is a
bilingual in monolingual and bilingual mode? When in a
bilingual mode, how much code-switching and borrowing
takes place?
• Biographical data: What is the bilingual’s sex, age,
socio-economic status, etc.
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A minimal questionnaire
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The Multilingualism Questionnaire
The
VCLA members have created a
much more extensive questionnaire.
CORNELL UNIVERSITY LANGUAGE ACQUISITION LAB AND
VIRTUAL CENTER FOR LANGUAGE ACQUISITION
VIRTUAL LINGUISTICS LABORATORY
Research Methods Manual: Scientific Methods
for the Study of Language Acquisition
1
Appendix Ill
Virtual Linguistics Lab
Child Multilingualism Questionnaire
Prepared by the Cornell Language Acquisition Lab and Virtual Center for Language
Acquisition Members and affiliates
2015
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DTA subject and session screens
Detailed data
Multilingualism
questionnaire is just an
attachment, so not searchable.
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Subject screen

ID, name and gender

DOB

Nationality, ethnicity, place of birth.

Language or cognitive impairment.

Info on Human Subjects requirements.

Multilingualism questionnaire completed.

Contact info for subject

Comments.

Language(s), dialect(s), and comprehension
and production levels in each language.

Info on caregivers.
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Session metadata












Session ID
Date
Interviewer
Assistants
Length of session
Tasks
Lenguages used
Session location
Subject
 Age
 Number and position among siblings
 Address, length of residency
 Education, occupation, schoolescuela
Name and ID in transcription for other
participants in session.
General activities
Analysis performed on data
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Sample size
 Since bilingual speakers vary so much, it is
difficult to find speakers who have the same
characteristics.
 Research studies involving bilingual
populations usually have small sample
sizes.
 A tool that allows for meta-analysis studies
of would be enormously beneficial.
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Language and language variety
detailed information
 Research
in multilingual populations frequently
involves working with better know Indo-European
languages as well as lesser studied languages such
as Haitian Creole, Yiddish and Quechua.
 In this type
of research, as in cross-linguistic studies,
there is an additional need to include detailed and
calibrated information on the language variety so
that cross-linguistic development can be compared.
 However,
such a capability requires individual
researchers to store and analyze data in compatible
ways.
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Assessment of the degree of
multilingualism of the participants
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The Definition Problem
With
respect to their language, bilinguals are
usually classified in terms of:
 language
knowledge
 amount of use of both languages
 domains of use of both languages and their skills
(reading and writing).
However, someone’s level of command may
change in all these areas throughout his/her
lifetime; so bilinguals need to be assessed at
different points in their development.
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How is language
competence/proficiency assessed?
 Different studies and labs use different tests or
instruments to measure the degree of
bilingualism.
 To enable comparison
across multilinguals, we
need data on how their level of bilingualism was
determined:
 Specific measures
 Specific tests
 Task modality (e.g., comprehension or production)
 The linguistic domain tested (e.g., vocabulary,
grammar).
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Different experimental designs and
coding systems
 One needs to be able to compare results of
different extensive markup systems
indicating
 The timing of exposure to relevant stimuli (e.g.
the point at which a child hears verbal stimuli
when presented with visual stimuli in a
picture/video-matching task
 The data source (total looking time versus first
long gaze in an Intermodal preferential looking
paradigm).
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Problems
 Many
evaluations of bilingualism in children are
based only in parental report through
questionnaires (Gutierrez-Clellen & Kreiter 2003;
Squires, Bricker, &Potter, 1997; Thordardottir &
Weismer, 1996).
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Lust, Flynn, Blume, Park, Kang, Yang,
& Kim 2014.

This study showed that it is fundamental to evaluate
children’s competence directly as a complement to parental
report.

We compared two Korean (L1), English (L2) bilingual fouryear-olds who participated in different case studies at
Cornell and MIT.

Parental report and general linguistic history showed the
children were very similar.

However, results of studies using the Elicited Imitation task
showed differences in the children’s language production
that the questionnaires did not predict.
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Level of bilingualism according to
questionnaire.
 Korean was the main language at home.
 They used Korean 80% of the time and English 20%
of the time.
 They were both sequential bilinguals.
 They were both Korean dominant. They undestood
and produced Korean with more proficiency than
English.
 They felt more comfortable in Korean than in
English in all contexts.
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Level of bilingualism according to
questionnaire.
Both mothers judged the child proficiency
as being in level 2 out of 4 for English and 4
out of 4 for Korean.
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Differences between the children
according to the questionnaire.

MJ spent 40 hours a week in
daycare, where he was
exposed to and spoke English
only.

CH spent only 9 hours a week
in daycare, where he used
English el 80% of the time
and Korean 20%.

Scale of linguistic abilities in
comprehension and
producción (range 1 to 6):
 CH had a 6 in Korean and 3
in English.
 MJ had a 6 in Korean and 5
in English.
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Results
MJ imitated all types of coordinate
structures perfectly.
CH only had 50% correct in both types of
coordination.
A large
proportion of CH’s errors where
due to the omission of Korean Case
markers (-ul/-lul)
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Issues of working with
multilingual data.
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Cross-linguistic data
 Well-studied Indo-European languages as
well as less-studied languages such as
Haitian Creole, Yiddish and Quechua.
 Include detailed and calibrated information
on the language variety so that crosslinguistic development can be compared.
 This requires
individual researchers to
store and analyze data in compatible ways.
+
Enabling cross-linguistic comparisons
requires rich markup capacity.
Example: acquisition of relative clauses across languages
English relative clause conversion in type:
(1) Experimental stimulus (lexically headed verb form):
Big Bird pushes the balloon which bumps Ernie.
(2) Child structure (free relative):
Big Bird pushes what bumps Ernie
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Enabling cross-linguistic comparisons
requires rich markup capacity.
French relative clause conversion in type:
(3) Experimental stimulus (lexically headed relative clause):
a. Aladdin choisit
la
chose que Fifi achète
b. Aladdin choose-3S the.FEM thing that Fifi buy-3S
c. ‘Aladdin chooses the thing that Fifi buys.’
(2) Child structure (free relative) (age 4;2)
a. Aladdin choisit
ce que Fifi achète
b. Aladdin choose-3S ce that Fifi buy-3S
c. ‘Aladdin chooses what Fifi buys.’
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37
Enabling cross-linguistic comparisons
requires rich markup capacity.
(5) Experimental stimulus (correlative form) verb form:
a. …maltenaa …
b. …malten- aa
c. …make- past(3.masc.sg) nQ
(6) Tulu relative clause form
(by child, 3;2, verbal adjective form with null head)
a. pada paND(i)na porl(u)ullall
b. pada ti paN- D(i)- na
ti porl(u)ullaali
c. song ti say- past- rel ti
beautiful.be.3.sg.fem
d. ‘She who sang is beautiful.’
(Somashekar 1999, 216)
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Enabling cross-linguistic comparisons
requires rich markup capacity.
 Enabling
cross-linguistic comparisons requires rich
markup capacity.
Different languages require different data markup.
 Coding must account for not only single data fields, but
relations among them.

Addressing this challenge requires a tool that
offers a wide range of markup and coding options
that are nevertheless standardized as much as
possible to permit comparisons across languages.
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39
What does the tool need?
 Standardization with flexibility
 Linkage across projects and data sets
 Efficient
data capture
 Inventory of fields informed by past
research
 Capacity to query fields and relations
among fields
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Code-switched, code-mixed data
Systems dealing with multilingual
data:
Identify the languages at multiple
levels.
Mark instances where determining the
language is not possible.
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Study on Code-Switching & Attention:
Adults & Children
 Relationship between Code-switching (CS) and
attention in English-Mandarin bilinguals
 Adults
and children
 How does depleting attention affect subsequent CS?
 Measuring
CS in an experimental context
 Transcribe  Reliability checks
 Language
background/CS practices
 Organizing critical questionnaire data
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Study on Code-Switching & Attention:
Adults & Children
Topic: Describe your ideal vacation
Uh... I, uh... definitely want to go taste some good wines from Europe, uh from Germany and
France maybe, but uh they're all pretty close, so it should be, I should be able to drive
around in these countries. Uh, I definite... want someone to
<BEEP>
如果有人跟我一起去度假会很好，总之我希望我的假期可以安排得比较
<BEEP>
It’s more about good people you hangout with so like the people u come to vacation so it’s
not about the it’s it’s about the view but it’s more about the people and as long as like you
have good like mental state
<BEEP>
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Challenges:
Working with CS Data
1.
CS transcripts
Different transcribing methods
 Issues with transcribing


2.
E.g., What constitutes as filler words?
Meta-data

Useful to have each participant’s language
background information on hand
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Challenges:
Working with CS Data
Advantages of sharing data:
i.
Facilitates discussion on issues
pertaining to transcribing
ii.
Making full use of data:

iii.
People from different fields may be
interested in different aspects of the data
Replicating study
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Conclusions
In sum, an LOD perspective and any
primary research tool which would aid
researchers to achieve linking of their
data in the study of multilingualism
would require a cyberinfrastructure to
support collaborative cross-linguistic
research, and calibration of complex
multilingual markup systems.
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Acknowledgments
 National Science Foundation




Barbara Lust. 2015. Workshop: Development of Linguistic
Linked Open Data Resources for Collaborative Data-Intensive
Research in the Language Sciences; University of Chicago, July
2015, under the direction of Barbara Lust, María Blume,
Antonio Pareja-Lora. Award ID 1463196,
María Blume and Barbara Lust. 2008. Transforming the Primary
Research Process Through Cybertool Dissemination: An
Implementation of a Virtual Center for the Study of Language
Acquisition. NSF OCI-0753415
Janet McCue and Barbara Lust 2004-2006. National Science
Foundation Award: Planning Information Infrastructure
Through a New Library-Research Partnership. (SGER=Small
Grant for Exploratory Research)
Lust, Barbara. 2003. Planning Grant: A Virtual Center for Child
Language Acquisition Research. National Science Foundation.
NSF BCS-0126546
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Acknowledgments
 American
Institute for Sri Lankan Studies,
Cornell University Einaudi Center.
 Cornell University Faculty Innovation in
Teaching Awards, Cornell Institute for
Social and Economic Research (CISER).
 New York State Hatch grant.
 The Cornell Institute for Social Science
 The Cornell Cognitive Science Program
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Acknowledgments
Funding for Dr. Barriere:
NSF,
USA/BCS#1251828 and 1251707
awarded to I. Barrière and G. Legendre;
ESRC, UK; PSC-CUNY
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Acknowledgments
Isabelle Barrière’s Collaborators
 Jennifer Culbertson, U. Edinburgh, Scotland
 Nayeli Gonzalez-Gomez, Oxford Brooks, UK
 Lisa Hsin, Tufts U., Boston, USA
 Sarah Kresh, Graduate Center CUNY, USA
 Geraldine Legendre, Johns Hopkins U., USA
 Gary Morgan, City U. London, UK
 Thierry Nazzi, U. Paris V & CNRS, France
 Bencie Woll, U. College London, UK
 Erin Zaroukian, Johns
Hopkins U., USA
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50
Acknowledgments
VCLA Founding Members
 Suzanne Flynn, MIT
 Claire Foley, Boston College.
 Marianella Casasola, Claire Cardie, James Gair, and Qi
Wang, Cornell University.
 Liliana Sánchez, Rutgers University at New
Brusnwick
 YuChin Chien, California State University at San
Bernardino
 Usha Lakshmanan, Southern Illinois University at
Carbondale
 Elise Temple, NeuroFocus
 Jennifer Austin, Rutgers University at Newark
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51
Acknowledgments
 VCLA affiliates:
 City University of New
Yors: Gita Martohardjono, and
Valerie Shafer
 Ben Gurion University at the Negev: Yarden Kedar
 Tyndale University College and Seminary: Sujin Yang
 Columbia University: Joy Hirsch
 University of California at San Diego: Sarah Callahan
 Kyungsung University: Kwee Ock Lee
 Central Institute of English and Foreign Languages:
R. Amritavalli
 Osmania University: A. Usha Rani
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52
Acknowledgments
 Our application
Lizzeth Pattison, María
developers Ted
Jiménez, and Mónica
Caldwell and Greg Kops Martínez at UTEP.
(GORGES).
 The students at all the
 Our consultants Cliff
participating
Crawford and Tommy
institutions who helped
Cusick.
us with comments and
 Our student RAs: Darlin suggestions.
Alberto, Gabriel
Clandorf, Natalia
Buitrago, Poornima
Guna, Jennie Lin, and
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
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
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
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
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