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The Topography of Variability in Cross-Linguistic Semantics

mllewis
July 14, 2018

The Topography of Variability in Cross-Linguistic Semantics

mllewis

July 14, 2018
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  1. The Topography of Variability in
    Cross-Linguistic Semantics
    Molly Lewis
    University of Chicago
    University of Wisconsin-Madison
    In collaboration with James A. Evans
    IC2S2- 4 July 2018

    View Slide

  2. 0
    2
    4
    6
    0.0 0.4 0.8
    Strength of association of
    occupation word vector and female gender
    count
    Distribution of effect sizes across languages
    Language semantics reflects
    (and influences) culture.
    Languages vary in their
    semantics.
    (Lewis & Lupyan, 2018)
    German
    Japanese
    Career-family bias in language

    View Slide

  3. How do languages differ in their semantics?

    View Slide

  4. Regier et al. (2007)
    Case study: Color Semantics

    View Slide

  5. A few domains well-described
    Most domains not understood
    Little understanding of the general principles that govern
    variability -- “topographies” of variability
    Kay & Regier (2006)
    COLOR
    Kemp et al. (2007)
    KINSHIP
    Youn et al. (2016)
    NATURAL KINDS
    Cross-linguistic semantic variability

    View Slide

  6. Our work
    Using word embedding models
    TOOLS, FOOD, EMOTION,
    GENDER, POLITICS …
    Characterize principles that govern that
    semantic variability
    English text written by speakers learning
    English as a second language

    View Slide

  7. Second language
    semantics have
    traces of native
    language
    Language 1
    Language 2
    Second-language learner text as a window
    into cross-linguistic semantics

    View Slide

  8. The Plan
    Study 1: Using second-language text as a window into
    cross-linguistic semantics
    Study 2: Characterizing the topography of differences
    Study 3: Topography in the native language

    View Slide

  9. Study 1: Dataset
    38,500 “essays“ in English by L2 learners of English
    35 L1s
    ~ 360 words/essay
    28 prompts, e.g.,:
    - People's behavior is largely determined by forces not of their own making.
    - A nation should require all of its students to study the same national
    curriculum until they enter college.
    - Nations should pass laws to preserve any remaining wilderness areas in their
    natural state, even if these areas could be developed for economic gain.

    View Slide

  10. Standard Arabic Bengali
    Bulgarian Mandarin Chinese
    Dutch
    English
    French
    German
    Modern Greek
    Gujarati
    Hindi
    Igbo
    Indonesian
    Italian
    Japanese
    Kannada
    Korean
    Malayalam
    Marathi
    Nepali
    Eastern Panjabi
    Polish
    Portuguese
    Romanian
    Russian
    Spanish
    Tagalog
    Tamil
    Telugu
    Thai
    Turkish
    Urdu
    Vietnamese
    Yoruba
    Western Farsi

    Language Family











    Afro−Asiatic
    Atlantic−Congo
    Austroasiatic
    Austronesian
    Dravidian
    Indo−European
    Japonic
    Korean
    Sino−Tibetan
    Tai−Kadai
    Turkic
    Language Typology
    ● VSO
    SVO
    SOV
    other
    Languages

    View Slide

  11. Essay 1
    (French)
    Essay 2
    (French)
    Essay 3
    (Korean)
    ...
    Method
    Train Doc2Vec on corpus of 38,500 (Le & Mikolov, 2014)
    “train”: [1 x 200 vector ]
    “love”: [1 x 200 vector ]
    Doc2vec

    View Slide

  12. Study 1: Are essays structured in semantic space
    by language?

























































































































































































































































































































































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    −30
    −20
    −10
    0
    10
    20
    −30 −20 −10 0 10 20 30
    All essays
    prompt
    essay

    View Slide

  13. Cross-linguistic relationships
    ARA
    BEN
    BUL
    CHI
    DUT
    ENG
    FAS
    FRE
    GER
    GRE
    GUJ
    HIN
    IBO
    IND
    ITA
    JPN
    KAN
    KOR
    MAL
    MAR
    NEP
    PAN
    POL
    POR
    RUM
    RUS
    SPA
    TAM
    TEL
    TGL
    THA
    TUR
    URD
    VIE
    YOR
    −1.00
    −0.75
    −0.50
    −0.25
    0.00
    −1.00 −0.75 −0.50 −0.25
    aff
    a
    a
    a
    a
    a
    a
    a
    a
    a
    a
    a
    Afro−Asiatic
    Atlantic−Congo
    Austroasiatic
    Austronesian
    Dravidian
    Indo−European
    Japonic
    Korean
    Sino−Tibetan
    TaiKadai
    Turkic
    Language centroids
    Italian
    French
    Spanish
    Portuguese
    Russian
    Polish
    Romanian
    Bulgarian
    Modern Greek
    German
    Dutch
    Tagalog
    Vietnamese
    Thai
    Indonesian
    Mandarin Chinese
    Korean
    Japanese
    Turkish
    Western Farsi
    Standard Arabic
    Yoruba
    Igbo
    English
    Tamil
    Malayalam
    Kannada
    Urdu
    Eastern Panjabi
    Telugu
    Marathi
    Hindi
    Bengali
    Nepali
    Gujarati

    View Slide

  14. Discussion
    L2’s semantic space contains traces of their native language
    (even though essays written in English!)
    The relationships between the semantic spaces of different
    languages are structured
    Structure predicted by linguistic and cultural dimensions of the
    native speakers

    View Slide

  15. Cross-linguistic relationships
    ARA
    BEN
    BUL
    CHI
    DUT
    ENG
    FAS
    FRE
    GER
    GRE
    GUJ
    HIN
    IBO
    IND
    ITA
    JPN
    KAN
    KOR
    MAL
    MAR
    NEP
    PAN
    POL
    POR
    RUM
    RUS
    SPA
    TAM
    TEL
    TGL
    THA
    TUR
    URD
    VIE
    YOR
    −1.00
    −0.75
    −0.50
    −0.25
    0.00
    −1.00 −0.75 −0.50 −0.25
    aff
    a
    a
    a
    a
    a
    a
    a
    a
    a
    a
    a
    Afro−Asiatic
    Atlantic−Congo
    Austroasiatic
    Austronesian
    Dravidian
    Indo−European
    Japonic
    Korean
    Sino−Tibetan
    TaiKadai
    Turkic
    Language centroids
    Italian
    French
    Spanish
    Portuguese
    Russian
    Polish
    Romanian
    Bulgarian
    Modern Greek
    German
    Dutch
    Tagalog
    Vietnamese
    Thai
    Indonesian
    Mandarin Chinese
    Korean
    Japanese
    Turkish
    Western Farsi
    Standard Arabic
    Yoruba
    Igbo
    English
    Tamil
    Malayalam
    Kannada
    Urdu
    Eastern Panjabi
    Telugu
    Marathi
    Hindi
    Bengali
    Nepali
    Gujarati
    How do the languages differ
    from each other?

    View Slide

  16. (Steyvers & Tenenbaum, 2005)
    family
    à
    parents
    home
    siblings
    Study 2: Characterizing the topography of
    differences
    Semantics are represented in memory as clusters
    of related meanings (e.g. Nelson, McEvoy, & Schreiber, 1998)

    View Slide

  17. family
    home
    parents
    siblings
    father
    mother
    house
    H1: Universal clusters
    H3: Universal
    H2: Universal macrostructure
    animal
    dog
    zoo
    farm
    mammal
    pets
    cat
    school
    university
    community
    teacher
    bus
    recess
    college
    H4: No universality
    Topographies of cross-linguistic variability

    View Slide

  18. Cluster word meanings using construct of concreteness
    Existing dataset of crowdsourced ratings for 40K English words
    (Brysbaert, et al., 2013)
    Study 2: Method
    Some words refer to things or actions in reality, which you can experience
    directly through one of the five senses. We call these words concrete
    words. Other words refer to meanings that cannot be experienced directly
    but which we know because the meanings can be defined by other words.
    These are abstract words.

    View Slide

  19. Concreteness ratings
    (Brysbaert, et al., 2013)
    Abstract Concrete
    idea
    doubt
    belief
    choice
    perspective
    no
    friendship
    late
    train
    food
    tv
    doctor
    number
    performance
    name
    community
    early
    important
    fun
    basic

    View Slide

  20. Language A
    Language B
    Abstract Concrete

    View Slide











  21. 0.36
    0.40
    0.44
    0.48
    0.52
    1 2 3 4 5
    Concreteness Quintile 1
    Cross−linguistic word−pairwise correlation
    (Pearson's r)
    group_type

    ● different quintile
    same quintile
    Concreteness Quintile 2










    1
    2
    3
    4
    5

    View Slide

  22. Language A
    Language B
    Abstract Concrete

    View Slide











  23. 0.36
    0.40
    0.44
    0.48
    0.52
    1 2 3 4 5
    Concreteness Quintile 1
    Cross−linguistic word−pairwise correlation
    (Pearson's r)
    group_type

    ● different quintile
    same quintile
    Concreteness Quintile 2










    1
    2
    3
    4
    5










    0.36
    0.40
    0.44
    0.48
    0.52
    1 2 3 4 5
    Concreteness Quintile 1
    Cross−linguistic word−pairwise correlation
    (Pearson's r)
    group_type

    ● different quintile
    same quintile
    Concreteness Quintile 2










    1
    2
    3
    4
    5

    View Slide

  24. Discussion
    Within cluster distances less variable than
    across cluster differences
    Concreteness appears to be a organizing
    principle in the system
    Semantics highly correlated with concreteness
    If true, should generalize to native language
    semantics

    View Slide

  25. Study 3: Topography in native language
    Pre-trained word embedding models trained on corpus of
    Wikipedia in each of 35 languages (fastText; Bojanowski, Grave, Joulin,
    Mikolov, 2016)
    1000 words from each decile from the concreteness norms
    Translated them into all 35 languages using Google Translate
    API
    Same analysis as second language text, predict same pattern

    View Slide


  26. ● ●
    ● ● ●
    ● ● ●


    ● ●



    ● ●




    ● ●





    ● ●



    ● ●

    ● ●






    0.15
    0.20
    0.25
    0.30
    0.35
    0.40
    1 2 3 4 5 6 7 8 9 10
    Concreteness Quintile 1
    Cross−linguistic word−pairwise correlation
    (Pearson's r)
    Wikipedia Embeddings










    0.36
    0.40
    0.44
    0.48
    0.52
    1 2 3 4 5
    Concreteness Quintile 1
    Cross−linguistic word−pairwise correlation
    (Pearson's r)
    group_type

    ● different quintile
    same quintile
    Concreteness Quintile 2










    1
    2
    3
    4
    5

    ● ●






    0.15
    0.20
    0.25
    0.30
    0.35
    0.40
    1 2 3 4 5 6 7 8 9 10
    Concreteness Quintile 1
    Cross−linguistic word−pairwise correlation
    (Pearson's r)
    group_type

    ● different quintile
    same quintile
    Concreteness Quintile 2




















    1
    10
    2
    3
    4
    5
    6
    7
    8
    9

    ● ●






    0.15
    0.20
    0.25
    0.30
    0.35
    0.40
    1 2 3 4 5 6 7 8 9 10
    Concreteness Quintile 1
    Cross−linguistic word−pairwise correlation
    (Pearson's r)
    group_type

    ● different quintile
    same quintile
    Concreteness Quintile 2




















    1
    10
    2
    3
    4
    5
    6
    7
    8
    9

    ● ●






    0.15
    0.20
    0.25
    0.30
    0.35
    0.40
    ross−linguistic word−pairwise correlation
    (Pearson's r)
    group_type

    ● different quintile
    same quintile
    Concreteness Quintile 2




















    1
    10
    2
    3
    4
    5
    6
    7
    8
    9

    View Slide

  27. Conclusion
    Second language text reflects native
    language semantics
    The semantics of languages vary
    primarily in their macrostructure
    Concreteness organizing principle
    Tensor factorization methods to
    characterize macro-variability

    ● ●
    ● ● ●
    ● ● ●


    ● ●



    ● ●




    ● ●





    ● ●



    ● ●

    ● ●






    0.15
    0.20
    0.25
    0.30
    0.35
    0.40
    1 2 3 4 5 6 7 8 9 10
    Concreteness Quintile 1
    Cross−linguistic word−pairwise correlation
    (Pearson's r)

    View Slide

  28. Thanks!
    Molly Lewis
    University of Chicago;
    University of Wisconsin-Madison
    http://home.uchicago.edu/~mollylewis/

    View Slide

  29. 1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    1 2 3 4 5
    Concreteness Quintile
    Semantics Cluster
    20
    40
    60
    n

    View Slide

  30. 1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    1 2 3 4 5 6 7 8 9 10
    Concreteness Decile
    Semantics Cluster
    100
    200
    300
    n

    View Slide

  31. For each language pair:
    [1] Environmental distance (Lupyan & Dale, 2011):
    - area of region in which the language is spoken (km2)
    - mean/sd temperature (celsius) and precipitation (cm)
    [2] Grammatical distance (Dryer & Haspelmath, 2013; Dediu, in prep.):
    Set of morphosyntactic features coded for each language (e.g.,
    word order, plural marking, tenses, etc.)
    [3] Lexical distance (Bakker, et al, 2009; Wichmann, Rama, & Holman, 2011):
    Distances between words using Levenshtein’s edit distance

    View Slide

  32. [4] Cultural Distance:
    Ethnographic Atlas – db of cultural traits for over 1200 cultural groups
    (Kirby, et al., 2016; Gray, 1999)
    10 categories of traits:
    Subsistence (11) – e.g., major crop
    Labor (24) – e.g., age or occupational specialization
    Gender (15) – e.g., differences in skills
    Kinship-Marriage (28) – e.g., transactions at marriage, marital residence with kin
    Politics (12) – e.g., political succession, castes
    Wealth/Property (44) – e.g., economy type, inheritance
    Ritual (7) – e.g., religion
    Community organization (26) – e.g., class differentiation, political succession
    Housing (11) – e.g., materials, layout
    Ecology (6) – e.g., domestic animals, agriculture

    View Slide
























































































  33. ● ●











































































    ● ●




















































































































































































    ● ●












































































    ● ●




















































































































































    ● ●



















    r = 0.23*


































































































































































































































    ● ●

















































































































    ● ●














    ● ●




























































    ● ●








































































    ● ●
























































































    r = 0.32*




































































































































































    ● ●




















































































































































































    ● ●












































































    ● ●




















































































































































    ● ●



















    r = 0.4*































































































    ● ●



































    ● ●










































































































































































    ● ●























    ● ●











































































    r = 0.38*
    Environmental Distance Grammatical Distance Lexical Distance Cultural Distance
    0 2 4 6 8 0 10 20 30 0.4 0.6 0.8 0.0 0.2 0.4 0.6
    1
    2
    semantic distance
    Grammatical, Lexical and Cultural distance predict independent variance (R2 = .25)
    Predicting of semantic distance

    View Slide

  34. Are language clusters coherent?
    0.00
    0.25
    0.50
    0.75
    1.00
    1.25
    Tagalog
    English
    Mandarin Chinese
    Romanian
    Portuguese
    Standard Arabic
    Turkish
    Western Farsi
    Polish
    Korean
    Indonesian
    Bulgarian
    Thai
    Igbo
    Urdu
    Russian
    Gujarati
    Spanish
    Japanese
    Telugu
    French
    Malayalam
    Modern Greek
    Tamil
    Dutch
    Italian
    Hindi
    Bengali
    German
    Nepali
    Vietnamese
    Kannada
    Marathi
    Eastern Panjabi
    Native language
    Coherence
    0.00
    0.25
    0.50
    0.75
    1.00
    1.25
    Tagalog
    English
    Mandarin Chinese
    Romanian
    Portuguese
    Standard Arabic
    Turkish
    Western Farsi
    Polish
    Korean
    Indonesian
    Bulgarian
    Thai
    Igbo
    Urdu
    Russian
    Gujarati
    Spanish
    Japanese
    Telugu
    French
    Malayalam
    Modern Greek
    Tamil
    Dutch
    Italian
    Hindi
    Bengali
    German
    Nepali
    Vietnamese
    Kannada
    Marathi
    Eastern Panjabi
    Native language
    Coherence
    =
    between
    ((within1
    + within2
    )/2)
    coherence =
    between
    ((within1
    + within2
    )/2)
    between > within
    within > between

    View Slide