second language learner theories on productivity ... · 3.1 causative verbs/causatives causal core:...

Language, Communication & Cognition – Brighton, August 2008

Second language learner theories on productivity:

Morphological causatives in English and German

Christoph Haase Chemnitz University of Technology


Structure:

1. Introduction

2. Aims and methodology

3. Linguistic causativity

4. Data discussion

5. Conclusion


1. Introduction 1.1 Problem, why care?

They keenen its edge and bolden its vigor, Simplen our language, and besten its tone, Exacten the meaning, shorten most sentences, These punchy verbs have a life of their own. (Lois Grosse in Australian Style 13,1 (2005): 7)

Grammaticalization of cause-effect relationships: • few studies employ comparative methods (exceptions: Comrie, 1989; Song, 1996 or

Wolff et al., 2001). • extensive research in L1 causative acquisition (cf. Brooks/Tomasello, 1999) • few studies about acquisition of causativity for L2 learners.


1.2 Focus • morphological causative verbs deadjectival blacken vs. German schwärzen. • data evaluation → L2 learner theories about the formation productivity

grammatical ungrammatical sharpen *bolden soften *braven cheapen *stupiden sweeten *souren ripen *unripen deafen *colden

Tab. 1 Possible and impossible morphological causatives • findings suggests consecutive refinement of learner theories

→ lead to a generalized model of morphological causatives.


1.3 Cognitive relevance of causation Causation: human recognition/use of cause-effect relationships → bases on experienced spatio-temporal contiguity of cause and effect

effects follow causes temporally

• shared by all speakers of all languages • cognitive and linguistic universal

→ all languages deal with causation in different grammaticalization patterns → ontological causation: acquired phenomenon as is linguistic causativity. • age of L1 acquisition of causativity: 36 months, (Gelman/Koenig, 2001)


1.4 Causative competence • higher cognitive faculty, critical for the processing of temporal information First language acquisition: so-called epistemic causativity → constructions where the temporal order is congruent with natural order but conjunction reverses this (1) ?The chair was poorly made, because it broke temporal competence: fully developed (manufacturing the chair precedes its breaking) causative competence: inhibited (conjunction reverses temporal order)


2. Aims and methodology

2.1 Rationale • investigation of L2 learner models about productivity of English morphological –en causatives. • hypothesized: models are complex, influenced by parameters of L1 interference, frequency, productivity and syntactic variance concerning transitivity, inchoativity, unaccusativity = typical causee processes → process can be coerced, but not without a causer → *redden! *roughen! *redder, *whiter… → Insight provide learner judgment elicitation tasks on category membership


2.2 Methodology Informants: 144 students L1 German, English B3 (1st semester) Material: word/non-word elicitation test • subjects rate 20 existent (like soften) and 20 nonexistent (like freshen) causatives • follow-up test subjects rate acceptability of lexical causatives in clauses (The soup cools) vs. (*The price cheaps) • scores used to calculate predictors of acceptability of causatives in L2 • further parameter values obtained via corpus lemma queries (BNCweb) • identified hypothetical parameters used to find predictors for learner decisions


3. Linguistic causativity 3.1 Causative verbs/causatives causal core: expressed via analytic/auxiliary morphological and lexical features cause and effect: a) grammaticalized as two independent propositions b) conflated within one clause → monoclausal causativity: degree of conflation creates a 3-way (a-c) causative system of grammaticalization patterns


3.2 Causative grammaticalization patterns a) generic/periphrastic/auxiliary/analytic make/have/let/get b) synthetic 1 develop, drown, break = allow make-paraphrase: make break/ make drown b) synthetic 2/morphological soften, redden = allow make-paraphrase: make soft/ make red c) lexical/suppletive kill, repair = disallow make-paraphrase, *make kill,

but: make dead/whole


3.3 Morphological causatives.

• selected for two reasons:

a) the lexical class of –en causatives can be considered relatively complete → negotiable for corpus search as well as complying to questionnaire design limitations

b) for German learners of English they pose obvious problems of interference with

their German morphological counterparts → theory formation of learners responsible for competence in this type of causatives


3.4 Morphological causatives in English and German frequent morphological causatives in English and German

English: A English: V German: A German: V black blacken schwarz schwärzen red redden rot röten blue * blau einbläuen weak weaken schwach schwächen sharp sharpen scharf schärfen cool cool kühl kühlen

Tab. 2: English and German adjectives and deadjectival morphological causatives

• plausible learner rule inferred from a simple pattern matching:

English/German: Vcaus + (ablaut) -en → [MAKE [X(A)]]


3.5 Naïve modelling vs grammar rules e.g. Plag, 2003 juxtaposes fatten/widen; *finen/lowen; *expensiven/*validen →suffixation is subject to segmental restriction → last segment can be /k/, /t/, /ɵ/, /s/, /d/ but not /n/, /ŋ/, /l/ or a vowel

top-down predictors monosyllabic consonant-final obstruent material properties

exceptions: *deaden, colden, bolden…

→ Do learners have explicit models in their minds, i.e. do they process hierarchies of constraints? → Do learners rely on segmental parsing?


3.6 Tentative assumptions → probably not, as evidenced by relatively high number of non-word targets with high acceptability scores Thus… → which quantitative arguments can be found?

bottom-up predictors frequency familiarity inchoativity productivity …

• predictors for learner judgment in word/non-word tests

Parameters: frequency, familiarity, keyness & correlation with acceptability scores


3.7 Caveat for German learners English and German causative systems: no 1:1 relationship • English:

lexical gap (there is no English word for make blue etc.) marking via stem vowel change in English is rare and often restricted to lexical causatives (fall/fell)

• German:

-en generic marker of infinitives and therefore no “surprise” in causatives causative meaning predominantly enforced via particles as in

German English abschwächen weaken verschärfen sharpen abkühlen cool aufhellen brighten erröten blush


3.8 Morphological causatives in translation Fig. 1 Results English-German Translation Corpus, http://www.tu-chemnitz.de/InternetGrammar

doc/doc11 - 98 Deregulation is not vague bureaucratic jargon. It is real action to brighten the prospects for 20 million European unemployed. Deregulierung ist kein vager Bürokratenjargon, sondern steht für reales Vorgehen zur Verbesserung der Chancen für 20 Millionen europäische Arbeitslose. ac/murdin - 1049 Over the next day the supernova would brighten to outshine the Moon and rival the Sun. Im Laufe des nächsten Tages würde die Supernova heller werden, den Mond überstrahlen und der Sonne den Rang streitig machen. ac/cros - 44 Other creatures had to wait for specific genetic changes to enable them to migrate into areas radically different from those of their ancestors - had to wait for incisors to lengthen into daggers before they could compete successfully with hyenas on the veldt, or had to wait for hair to thicken into fur Andere Lebewesen mußten erst bestimmte genetische Veränderungen durchlaufen, bevor sie in geographische Regionen überwechseln konnten, die grundlegend anders waren als die ihrer Ahnen: Ihre Schneidezähne mußten erst zu Dolchen werden, um in der Konkurrenz um die Nahrung zu bestehen; ihre Behaarung mußte ac/finl - 173 These are approximations, to be sure, but no reasonable margin of error one wishes to allow will weaken the implication of these staggering numbers, probably without parallel in history. Dies sind natürlich nur Annäherungswerte; aber keine Fehlertoleranz vernünftigen Ausmaßes wird etwas an der Bedeutung dieser erstaunlichen Zahlen ändern können, für die es in der Weltgeschichte


3.9 Morphological causatives in translation Tendency: translators use avoidance strategies for expressing morphological causation sample: different translation strategies apply for brighten, lengthen and weaken English strategy German corpus ref.

suppletion Verbesserung = improvement

doc/doc11-98 brighten

periphrastic causation heller werden = become brighter

ac/murdin-1049

lengthen (into daggers) ignore causation zu Dolchen werden = become daggers

ac/cros-44

weaken replacement ändern = change

ac/finl-173

Tab. 3 Translation strategies English → German

• relatively infrequent, 12 occurrences of weaken → 2 translations with schwächen


3.10 Learner model assumption • naïve, pattern-based and context free acquisition strategies → any deadjectival verb from a monosyllabic adjective describing color, material properties or shape could be a plausible candidate

blacken bluen brighten calmen coolen deaden deafen deepen diren drien dullen dusken dusten fasten freeen freshen greaten greenen greyen harden hasten heaten largen loosen realen redden richen ripen rotten roughenrushen sadden salten shalen sharpen shocken sicken soften sweeten tighten wetten whiten

Tab. 4 Tested word/non-word target items naïve assumption: unreal members of the category would be randomly distributed data show: choice of learners is biased towards some candidates


4. Data discussion 4.1 Elicited response data and absolute frequency

BNCweb lemma query frequency example

Blacken 149 ADA 1811 The pages blackened, crinkled, and

Brighten 402 A7A 141 they darken, in the East they brighten

Cheapen 40 FAJ 253 I ask you not to cheapen her life in your

Deafen 83 B3J 714 The noise was deafening in the small

Deepen 655 CBN 1093 His urge was to deepen.

Fasten 667 A65 1799 It opens at both ends and fastens with

Flatten 594 JY6 2445 She flattened herself against the door.

… … …………

Roughen 31 HH8 2513 His voice roughened.

Sadden 213 B77 2026 That saddens me.

Sharpen 547 B0U 1353 The little eyes sharpened at the top of

Sicken 107 FAT 2541 Critics sickened him.

Smarten 55 H0M 2922 It smartens its act.

Soften 886 A6L 126 There are ways in which you can soften

Straighten 948 A73 659 A sudden dignity made him straighten

Sweeten 126 HR9 2290 He needed me to sweeten Mrs Danby.

Tab. 5 Morphological causative frequencies in the BNCweb


4.2 Subject responses and absolute frequencies BNCweb lemma query Acceptability judgment score in %; n=144 Tighten 1424 34 *to soft vs. to soften 66

Straighten 948 39 *to fast vs. to fasten 61

Soften 886 48 *to tight vs. to tighten 52

Fasten 667 54 *to bright vs. to brighten 46

Harden 662 58 *to straight vs. to straighten 42

Deepen 655 59 *to sharp vs. to sharpen 41

Flatten 594 55 *to flat vs. to flatten 45

Loosen 561 72 *to red vs. to redden 28

Sharpen 547 48 *to hard vs. to harden 52

Brighten 402 53 *to deep vs. to deepen 47

Sadden 231 49 *to sad vs. to sadden 51

Ripen 201 61 *to black vs. to blacken 39

Blacken 149 75 *to deaf vs. to deafen 25

Sweeten 126 79 *to sick vs. to sicken 21

Redden 123 82 *to sweet vs. to sweeten 18

Sicken 107 89 *to ripe vs. to ripen 11

Deafen 83 90 *to smart vs. to smarten 10

Smarten 55 92 *to cheap vs. to cheapen 08

Cheapen 40 96 *to loose vs. to loosen 04

Roughen 31 96 *to rough vs. to roughen 04

Tab. 6 Frequencies and mean acceptability scores (frequency sorted) Boxplot acceptability, mean = 33.5


BNCweb lemma query Acceptability judgment score in %; n=144 Soften 886 34 *to soft vs. to soften 66

Fasten 667 39 *to fast vs. to fasten 61

Tighten 1424 48 *to tight vs. to tighten 52

Brighten 402 54 *to bright vs. to brighten 46

Straighten 948 58 *to straight vs. to straighten 42

Sharpen 547 59 *to sharp vs. to sharpen 41

Flatten 594 55 *to flat vs. to flatten 45

Redden 123 72 *to red vs. to redden 28

Harden 662 48 *to hard vs. to harden 52

Deepen 655 53 *to deep vs. to deepen 47

Sadden 231 49 *to sad vs. to sadden 51

Blacken 149 61 *to black vs. to blacken 39

Deafen 83 75 *to deaf vs. to deafen 25

Sicken 107 79 *to sick vs. to sicken 21

Sweeten 126 82 *to sweet vs. to sweeten 18

Ripen 201 89 *to ripe vs. to ripen 11

Smarten 55 90 *to smart vs. to smarten 10

Cheapen 40 92 *to cheap vs. to cheapen 08

Loosen 561 96 *to loose vs. to loosen 04

Roughen 31 96 *to rough vs. to roughen 04

Tab. 6 Frequencies and mean acceptability scores (acceptability sorted)


4.3 Verbal frequency and mean acceptability

r = 0.66 (critical value: r > 0.44 for p>.05 and n = 20) → overall, absolute frequency comes out a possible predictor


But: items identified as conversions from A (like *to rough) → familiarity of A as a predictor? (individual frequency, cf. Gernsbacher, 2001)

BNCweb lemma query Acceptability judgment score in %; n=144 Soft 6343 66

Fast 4981 61

Tight 2735 52

Bright 5917 46

Straight 3142 42

Sharp 4235 41

Flat 3684 45

Red 12193 28

Hard 17226 52

Deep 9278 47

Sad 3464 51

Black 21798 39

Deaf 2633 25

Sick 4227 21

Sweet 3384 18

Ripe 630 11

Smart 1554 10

Cheap 6666 08

Loose 2505 04

Rough 3405 04


4.4 Adjectival frequency and mean acceptability

r = 0.29 → adjectival frequency rejected as a predictor


4.5 Productivity and mean acceptability • can help explain low scores for roughen and cheapen

→ verbal use by far outnumbered by frequent adjectival use of e.g. rough, cheap → productivity parameter can account for the “readiness” of an A to take causative –en

BNCweb lemma query A/V ratioTighten 1424 Tight 2735 1.9Straighten 948 Straight 3142 3.3Soften 886 Soft 6343 7.2Fasten 667 Fast 4981 7.5Harden 662 Hard 17226 26Deepen 655 Deep 9278 14.1Flatten 594 Flat 3684 6.2Loosen 561 Loose 2505 4.5Sharpen 547 Sharp 4235 7.7Brighten 402 Bright 5917 14.7Sadden 231 Sad 3464 16.3Ripen 201 Ripe 630 3.1Blacken 149 Black 21798 146.3Sweeten 126 Sweet 3384 26.9Redden 123 Red 12193 99.1Sicken 107 Sick 4227 39.5Deafen 83 Deaf 2633 31.7Smarten 55 Smart 1554 28.2Cheapen 40 Cheap 6666 166.6Roughen 31 Rough 3405 109.8

Tab. 8 Frequencies of Vcaus, A and A/Vcaus scores


4.6 Productivity A → deadjectical Vcaus

r = -0.65 → weak negative correlation, makes inverse productivity a possible predictor


4.7 Keyness and acceptability → inverse productivity: low acceptability values correlate with high productivity (roughen, cheapen) → therefore salient items should be predictors • Keyness index (log-likelihood) cf. Scott, 1996 and WordSmith • TF*IDF (Oakes, 2007)

→ convenience-sampled corpus (3.5 mio words incl. HP, Austen, newspapers) reference corpus: BNC

Keyness index (log-likelyhood) Keyness index (TF*IDF) Soften 3.3 5Fasten 9 17.5Tighten 24.9 20Brighten 22 18.6Straighten 7.5 9Sweeten 79.1 78.7Ripen 93 81.8Smarten 82.9 76.2… … …


4.8 Keyness and TF*IDF scores for morphological causatives: comparison


4.9 Keyness and TF*IDF scores for morphological causatives: scatterplots r = -0.76 r = -0.86 → salience of items a possible predictor


5. Conclusion

• learner models are complex and influenced by different parameters • bottom-up predictors can be verified or falsified using quantitative methods

→ model of the attribution of category membership of morphological causatives is influenced by at least three different cues: • cue 1: frequency: a learning effect/parameter • cue 2: productivity; an inverse effect • cue 3: salience, measurable as keyness index

to be tested: syntactic and semantic cues, such as: • transitivity (causative-inchoative alternation) • unaccusativity • material properties • …


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