7/17/17 acquisi’onofsignlanguages …...7/17/17 4 need$to$know$ •...

7/17/17

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Diane Lillo-‐Mar0n University of Connec/cut & Haskins Laboratories

Acquisi'on of Sign Languages

Class 3 14 July 2017

Methods in research on SL acquisi0on

•  Longitudinal corpora •  Analyzing corpus data •  Experiments •  Assessments

7/14/17 LSA Summer Ins0tute 2

Methodological considera0ons

•  What are the research ques0ons? •  What kind of data will best address the ques0ons?

•  Analysis of corpora vs. experiments •  Group data vs. (mul0ple) case studies •  Age range of interest

7/14/17 3 LSA Summer Ins0tute

ASL ACQUISITION ASSESSMENTS


Currently available assessments

hQp://www.signlang-‐assessment.info/

•  MacArthur-‐Bates CDI •  Kendall Conversa0onal Proficiency Levels •  ASL Recep0ve Skills Test •  Visual Communica0on and Sign Language Checklist


MacArthur-‐Bates Communica0ve Development Inventory

•  Parent check-‐list of vocabulary items their child ‘uses’

•  Ages 8-‐36 mos


Anderson & Reilly (2002)

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Kendall Conversa0onal Proficiency Levels

•  Measures child communica0ve skills •  Ages 0-‐5+ •  Completed by teacher or parent


French (1999)

ASL Recep0ve Skills Test

•  Comprehension test using picture-‐choice method

•  Ages 3-‐13 •  Norm-‐referenced


Visual Communica0on and Sign Language Checklist

•  List of VCSL traits – check ‘not yet emerging’, ‘emerging’, ‘inconsistent use’, ‘mastered’

•  Ages 0-‐5 years •  Must be administered by trained assessor •  Norm referenced

7/14/17 LSA Summer Ins0tute 9 Simms et al. (2013)

Adult Assessments

•  ASL-‐PI: Proficiency interview (Gallaudet) •  SLPI: Proficiency interview (NTID) •  ASL Sentence Repe00on test (Hauser et al. 2008) •  ASL Comprehension test (Hauser et al. 2016) •  ASL Discrimina0on test (Bochner et al. 2011)


EXPERIMENTAL STUDIES


Methods used in experiments

•  Comprehension – Picture choice – TVJT

•  Produc0on – Targeted elicita0on – Picture descrip0on – Narra0ve elicita0on


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Elicited Produc0on: WH-‐ques0ons •  Experimenter 1 – storyteller; Experimenter 2 – ‘cat’. •  Exp 1 tells a story with toys. Some part of the story is

missing, so the Exp prompts the child to ask the cat for informa0on.

•  Example lead-‐in: –  “It’s lunch 0me under the sea. SpongeBob could eat the pineapple or the banana. So, SpongeBob will eat something. Ask the cat what.”

13 Thornton (1990; Lillo-‐Mar0n (2000)

Elicited Produc0on: WH-‐ques0ons

Target long-‐distance ques0ons example lead-‐in (translated): – “Olivia’s family has finished dinner. Someone has to wash the dishes. I think it’s Dad’s turn to wash the dishes. Ask the cat who she thinks.”

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Elicited Produc0on: WH-‐ques0ons

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Par0cipants: 17 Deaf children (na0ve signers), 4;01-‐6;09

Lillo-‐Mar0n (2000)

Elicited Produc0on: WH-‐ques0ons Results with Kodas


0% 20% 40% 60% 80% 100%

7-‐year-‐olds (n=2)




Subject WH-‐Ques'ons -‐ ASL

Ini0al

Final

Double

0% 20% 40% 60% 80% 100%





Object WH-‐Ques'ons -‐ ASL

Ini0al

Final

Double

0% 20% 40% 60% 80% 100%





Adjunct WH-‐Ques'ons -‐ ASL

Ini0al

Final

Double

LONGITUDINAL CORPORA


Longitudinal studies of child language (child language corpora)

•  Address a wide variety of research ques0ons •  Each dataset can be mined in many ways •  Complements experimental/cross-‐sec0onal study nicely


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Need to know

•  Whether you are building your own corpus, using a corpus constructed by someone else, or simply reading research reports based on corpora data, there are some points about corpora building you need to know.


Challenges of conduc0ng child longitudinal studies

•  Balance child’s comfort zone and need for a representa0ve sample of language

•  Requires real crea0vity to coax a rich and varied sample out of child –  Invest in 0me, get to know child and family, learn what gets them talking/signing

– Thinking on your feet to follow the child’s lead and expand on what the child says


Challenges of conduc0ng child longitudinal studies

•  Let child do what she wants, yet make sure that condi0ons are maximized for later transcribability – Monitor ambient ligh0ng and sound – Film child in rooms without places to hide or too much off-‐camera space


Drawbacks of longitudinal spontaneous corpora

•  MacWhinney’s (2001) three-‐headed monster of corpus transcrip0on: – Lack of standard format + rapid prolifera0on of alterna0ve formats

–  Indeterminancy •  Difficult to determine what •  was really said/signed

– Tedium •  Highly labor-‐intensive, con0nually subject to revision and expansion


Corpus building

•  Factors to consider in building (or using – or interpre0ng) corpus data

•  Subject selec0on – Age, linguis0c background, other factors – Other par0cipants (parents, siblings, etc.)

•  Data collec0on –  Interlocutors, serng, materials, recording devices, number & frequency of recordings, dura0on, coopera0on of child

•  Feasibility 7/14/17 LSA Summer Ins0tute 23

Human subjects considera0ons

•  Privacy •  Confiden0ality •  Minimal risks •  Special considera0ons for children •  Data sharing •  IRB approval


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Metadata Categories Example

Child pseudonym JIL

Session # 037

Date of recording 1998-‐05-‐17

Child’s age 2;05.17

Child’s gender F

Child’s status D/D

Dura0on 1:19.47

Adults MOT, FAT

Cameraperson DCP

Transcrip0on FMP

Informa0on on each session

Annota0on •  Nota0on: the system of graphic symbols used to represent the phenomena

•  Transcrip0on: the graphic representa0on of an extended uQerance in face-‐to-‐face language (using nota0on or script)

•  “One of the major purposes of nota0on and transcrip0on systems is to enable the reader of the graphic symbols to know, with greater or lesser accuracy according to the degree of detail in the system being used, the form of what was originally spoken or signed.”


Johnston, Trevor (2010) From Archive to Corpus Intl J Corpus Linguis/cs 15, 106-‐131

Annota0on

•  Annota0on: commentary appended to a text. –  In sign linguis0cs: annota0ons iden0fy aspects of the signed text, 0me-‐aligned to the recording.

– Minimally: one wriQen word for each signed (or spoken) word.

•  Tag: other types of commentary appended to the text –  (e.g., modifica0ons of signs, phonological informa0on, part-‐of-‐speech, etc.)


Annota0on using ID glosses

•  We employ annota0on using ID glosses (rather than transcrip0on).

•  ID Gloss: a word that “uniquely names and iden0fies a sign” (Johnston 2001, 148)


What is an ID gloss?

•  English word used to label a sign consistently – regardless of meaning / modifica0on in context


ELIMINATE DELETE REJECT REMOVE GET-‐RID-‐OF WEED-‐OUT

What is an ID gloss (NOT)?

•  ID gloss is NOT the ‘official’ name for a sign •  Annota0on using ID glossing ≠ transcrip0on •  Immediate goal is NOT for reader to be able to reproduce the signing with all nuance

•  Immediate goal: – Annota0on linked to video – Can consistently find right class of signs – Further detailed informa0on recorded on subsequent passes


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Mul0ple annota0on passes


Johnston, Trevor & de Beuzeville, Louise (2008) Researching the linguis0c use of space in AUSLAN: Guidelines for annotators using the Auslan corpus. Ms., Macquarie University.

Why use ID glosses?

•  Consistency within corpus

•  Searchability

•  Understandabil-‐ity by others


Annota0on

•  What gets annotated? – Choice of sessions – Which par0cipants – Selec0on of communica0ve ac0ons to be annotated (signs, speech, gestures, etc.)

– Level of detail (non-‐manuals, pho, etc.) – How to treat non-‐adult forms – Non-‐linguis0c elements (context, comments)


Annota0on

•  Further considera0ons •  Who does the annota0on? •  Splirng the responsibili0es across coders •  Reliability checking •  Conven0ons manual •  Documen0ng decisions


The SLAAASh project

•  Sign Language Acquisi0on: Annota0on, Archiving and Sharing

•  Grew out of CLESS: Cross-‐Linguis0c Early Syntax Study

•  Longitudinal spontaneous produc0on data – various adult interlocutors interact with children

•  L1 ASL signers – Monolingual Deaf


UConn CLESS Child ASL Data

Lillo-‐Mar0n & Chen Pichler (2008) •  Spontaneous produc0on data from 4 Deaf children of Deaf parents, ages 1;04-‐4;01

•  Interlocutors: Deaf parents; Deaf or hearing, signing experimenters

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ANALYZING CORPUS DATA


Mean Length of UQerance (MLU)

•  Measure of language growth over 0me •  Brown (1973) provided specific guidelines for calcula0ng MLU in English

•  MLUm – MLU in morphemes •  MLUw – MLU in words •  Frequently used in spoken language research – but use cau0on comparing across languages

MLU – English Miller & Chapman (1981)

MLU – Cantonese Klee et al. (2004)

N=70

MLU – ASL Pe0Qo (1987)

•  UQerances containing 2 or more signs

•  Excluding uQerances with points

•  Very few analyzable uQerances in youngest sessions

Mean Length of UQerance (MLU)

•  Applicable to analysis of sign language samples?

•  Previous studies highly variable •  Need to resolve issues regarding inclusion criteria

•  UQerance – Where to make uQerance breaks

•  Length – How to define morphemes

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MLUm – SLAAASh data (preliminary)

(cf. Lillo-‐Mar0n, Quadros, Berk & Hopewell-‐Albert 2015)

IPSyn (Scarborough 1990)

•  Index of Produc0ve Syntax •  Scoresheet containing 60 phrase types known to develop over ages in English (NP, VP, S, Q/Neg)

•  Child receives 1 or 2 points for use of each structure

•  Based on 100 uQerances; conversion from 50 uQerances available

ASL-‐IPSyn

•  Based on a language sample (100 uQerances) •  Current version has 64 structures (NP, VP, S, DS)

ASL-‐IPSyn

(Lillo-‐Mar0n, Goodwin & Prunier 2017)

Other types of analyses

•  Vocabulary (types, tokens)

•  Phonology (inventory, subs0tu0ons)

•  Morphology (verb agreement, classifiers)

•  Syntax (word order, specific structures)


FOR NEXT TIME

•  Reading: SLADHC, ch. 3 •  Post discussion on Canvas

•  To contact me: – diane.lillo-‐[email protected]

•  Office hours: Monday 12-‐2 – Starbucks


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