Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?
- 1. Explaining Character-Aware Neural Networks for Word-Level Prediction Frederic Godin, Kris Demuynck, Joni Dambre, Wesley Deneve and Thomas Demeester Department of Electronics and Information Systems Ghent University, Belgium Do They Discover Linguistic Rules?
- 3. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Example: Rule-based tagger for PoS tagging Brill (1994)’s transformation-based error-driven tagger 3 Template Change the most-likely tag X to Y if the last (1,2,3,4) characters of the word are x Rule Change the tag common noun to plural common noun if the word has suffix -s Easily interpretable
- 4. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Interpretability in NLP used to be easy Rule-based/Tree-based models Shallow statistical models (E.g., Logistic regression, CRF) 4 Very transparent: follow the trace Essentially: weight + feature
- 5. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Current NLP interpretability... 5
- 6. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Our proposed method 6 We present contextual decomposition (CD) for CNNs - Extends CD for LSTMs (Murdoch et al. 2018) - White box approach to interpretability We trace back morphological tagging decisions to the character-level - Which characters are important? - Same patterns as linguistically known? - Difference CNN and BiLSTM?
- 8. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Contextual decomposition Idea: every output value can be “decomposed” in - Relevant contributions originating from the input we are interested in (E.g., some characters) - Irrelevant contributions originating from all the other inputs (E.g., all the other characters in a word) 8 CNNeconomicas plural economicas economicas economicas economicas Relevant relevant irrelevantrelevant
- 9. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Contextual decomposition for CNNs Three main components of CNN ̶ Convolution ̶ Activation function ̶ Max-over-time pooling Classification layer 9 ^ e c o n o m i c a s $ ... Max over time FC Gender = feminine CNN filters
- 10. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Contextual decomposition for CNNs: Convolution Output of single convolutional filter at timestep t: 10 Relevant Irrelevant n = filter size S = Indexes of of relevant inputs Wi = i-th column of filter W ^ e c o n o m i c a s $ Indexes: 8, 9, 10, 11 9 8, 10, 11
- 11. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Contextual decomposition for CNNs: Activation func. Goal: Linearize activation function to be able to split output. Linearization formula: 11
- 12. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Contextual decomposition for CNNs: Max pooling Max-over-time pooling: Determine t first and just copy that split: 12
- 13. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Contextual decomposition of classification layer Probability of certain class: 13 We simplify: Relevant contribution to class j
- 14. Experiments 14
- 15. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Task 15 Morphological tagging: predict morphological labels for a word (gender, tense, singular/plural,..) economicas For a subset of words, we have manual segmentations and annotations lemma=económico gender=feminine number=plural economicas lemma=económico gender=feminine number=pluraleconomicas economicas
- 16. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Datasets Universal dependencies 1.4: ̶ Finnish, Spanish and Swedish ̶ Select all unique words and their morphological labels Manual annotations and segmentations of 300 test set words 16
- 17. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Architectures: CNN vs BiLSTM 17 ^ e c o n o m i c a s $ FC Gender = feminine ^ e c o n o m i c a s $ ... Max over time FC Gender = feminine CNN filters CNN BiLSTM
- 18. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Do the NN patterns follow manual segmentations? 18 All = every possible combination of characters Cons = all consecutive character n-grams
- 19. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Visualizing contributions: 1 character 19 Spanish ^ g r a t u i t a $ Label: Gender=feminine
- 20. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Visualizing contributions: 2 characters (Swedish) 20 CNN BiLSTM ^ k r o n o r $ ^ k r o n o r $ ^ k r o n o r $ ^ k r o n o r $ Label: number=plural
- 21. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Most important patterns per language: Spanish 21 Linguistic rules for feminine gender: - Feminine adjectives often end with “a” - Nouns ending with “dad” or “ión” are often feminine Found pattern: - “a” is a very important pattern - “dad” and “sió” are import trigrams
- 22. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Most important patterns per language: Swedish 22 Linguistic rules for plural form: - 5 suffixes: or, ar, (e)r, n, and no ending “na” is definite article in plural forms Found pattern: - “or” and “ar” - But also “na” and “rn”
- 23. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Interactions/compositions of patterns How do positive and negative patterns interact? Consider the Spanish verb “gusta” - Gender=Not Applicable (NA) - We know that suffix “a” is indicator for gender=feminine 23 Consider most positive/negative set of characters per class: The stem provides counterevidence for gender=feminine
- 24. Conclusion 24
- 25. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Summary We introduced a white box approach to understanding CNNs We showed that: ̶ BiLSTMs and CNNs sometimes choose different patterns ̶ The learned patterns coincide with our linguistic knowledge ̶ Sometimes other plausible patterns are used 25
- 26. Fréderic Godin - Explaining Character-Aware Neural Networks for Word-Level Prediction Questions? 26
- 27. Fréderic Godin Ph.D. Researcher Deep Learning and NLP IDLab E frederic.godin@ugent.be @frederic_godin www.fredericgodin.com idlab.technology / idlab.ugent.be