Grammatical and topical gender in crosslinguistic word embeddings. Kate McCurdy Berlin NLP June

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1 Grammatical and topical gender in crosslinguistic word embeddings Kate McCurdy Berlin NLP June

2 Word embeddings: From (almost) scratch to NLP Goal: word representations that... capture maximal semantic/syntactic information, yet require minimal task-specific feature engineering Neural embeddings to the rescue! Input: barely processed, massive corpora In general: tokenization + trimming the long tail in vocab Collobert et al.: capitalization as feature + a few extra tweaks Mikolov et al: n-gram phrase identification Output: dense, magically performant vectors

3 but there are pitfalls

4 You shall know a word by the company it keeps. Firth 1957

5 Pitfall #1 What if your words keep company with some unsavory stereotypes?

6 Analogous relations in the GloVe word embedding; from Caliskan-Islam et al 2016

7 Stereotypes in word embeddings: Bolukbasi et al addiction : eating disorder accountant : paralegal pilot : flight attendant athlete : gymnast professor emeritus : associate professor

8 Bias in humans: the Implicit Association Test Standard psychological test to assess implicit bias Design: Greenwald et al Two sets of attribute words Male, man, boy, Female, woman, Two sets of target words Children, wedding,... Office, salary, Task: left vs right fast categorization of both sets Measurement: differential association in average response time

9 WEAT: the Word Embedding Association Test Parallels the Implicit Association Test Measures the differential association between paired target and attribute word sets via cosine distance Core finding: nearly every single prejudice uncovered by the IAT is replicated by the WEAT on Google News + GloVe word embeddings

10 Pitfall #1 What if your words keep company with some unsavory stereotypes?

11 Pitfall #2 What if your content words hang out with your function words and make weird artefacts?

12 Crosslinguistic word embeddings Work with Oguz Serbetci (not pictured)

13 Data Corpus: OpenSubtitles ~5.5K movies with subtitles in 4 languages ( m ws): German - grammatical gender Spanish - grammatical gender Dutch - grammatical gender orthogonal to natural gender English - natural gender Lemmatized each corpus to remove gender Trained 10 word2vec CBOW embeddings per condition: Language (4) x Corpus version (2 - unprocessed vs lemmatized)

14 Method Measurement: differential association using the Word Embedding Association Test (WEAT - Caliskan et al.) {career} {family} {male} {female}

15 Method Measurement: differential association using the Word Embedding Association Test (WEAT - Caliskan et al.) Comparisons: Topical semantic gender bias replicate IAT findings of Caliskan et al. on dimension male:career::female:family

16 Method Measurement: differential association using the Word Embedding Association Test (WEAT - Caliskan et al.) Comparisons: Topical semantic gender bias replicate IAT findings of Caliskan et al. on dimension male:career::female:family Grammatical gender bias use stimuli from Phillips & Boroditsky on dimension male:masculine::female:feminine e.g. Spanish el sol (m), German die Sonne (f)

17 Topical gender bias average increase in cosine similarity per word

18 Topical gender bias Grammatical gender bias

19 Pitfall #2 What if your content words hang out with your function words and make weird artefacts?

20 Words can keep strange company! And arbitrary properties like grammatical gender can distort your embeddings.

21 Thank! Q?

22 References Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016). Quantifying and reducing stereotypes in word embeddings. arxiv Preprint arxiv: Caliskan-Islam, A., Bryson, J. J., & Narayanan, A. (2016). Semantics derived automatically from language corpora necessarily contain human biases. arxiv Preprint arxiv: Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(Aug), Firth, John R A synopsis of linguistic theory In Studies in linguistic analysis, Oxford: Blackwell. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. (1998). Measuring individual differences in implicit cognition: the implicit association test. Journal of Personality and Social Psychology, 74(6), Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp ).

23 Appendix

24 Interaction between topical and grammatical gender effects in DE + ES

25 Stereotypes in word embeddings: Bolukbasi et al Define gender subspace

26 Stereotypes in word embeddings: Bolukbasi et al Define gender subspace 2. Project profession names onto subspace

27 Stereotypes in word embeddings: Bolukbasi et al addiction : eating disorder Define gender subspace Project profession names onto subspace 3. Generate analogies & get stereotype ratings from MTurk accountant : paralegal pilot : flight attendant athlete : gymnast professor emeritus : associate professor

28 Stereotypes in word embeddings: Bolukbasi et al Define gender subspace 2. Project profession names onto subspace 3. Generate analogies & get stereotype ratings from MTurk 4. Compute transformation matrix to debias designated words

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