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Yves Peirsman - Deep Learning for NLP
- 1. Deep Learning for NLP Yves Peirsman
- 2. About me 2012 NLP engineer 2011 Post-doctoral researcher, Stanford University 2014 NLP Town 2010 PhD Computational Linguistics, KULeuven
- 3. Deep Learning in NLP 2012 Deep Learning ● Comeback of neural networks ● Unified framework for many problems 1990 Statistical NLP ● Machine learning from data ● Many different models 1950s Rule-based NLP ● Hand-written linguistic rules ● Knowledge models 20?? ??
- 4. Deep Learning in NLP Basic models The basic NLPer’s toolkit Main advantages Why has DL become so popular in NLP? Beyond the hype Deeper dive & recent trends
- 5. Word embeddings Words as atomic units Words as dense embeddings The movie has an excellent cast. M I like the cover of the book. B There were too many pages in the novel. ? book novel movie
- 6. POS syntaxcapitaliz ation prefix suffix bigrams lemmas Feature engineering Major feature engineering Little feature engineering (if any) strings strings
- 7. Models Distinct models for different problems Unified toolkit Text classification NER MT ... PP PP PP SVM CRF LM TM decoder Text classification NER MT ... LSTM (or similar)
- 8. NLP Toolkit: LSTM for classification Applications: text classification, language modelling LSTM LSTM LSTM LSTM LSTM embeddings dense layer weights biases The movie was boring . positive neutral negative
- 9. NLP Toolkit: inside the LSTM forget input tanh output tanh was boring The movie ...
- 10. NLP Toolkit: LSTM for sequence labelling Applications: named entity recognition LSTM LSTM LSTM LSTM LSTM embeddings dense layer logits B-PER O O B-LOC O weights biases John lives in London .
- 11. NLP Toolkit: Encoder-Decoder Architectures Applications: machine translation, text summarization, dialogue modelling, etc. LSTM LSTM LSTM LSTM source embeddings LSTM LSTM LSTM LSTM LSTM target embeddings Je t’ aime . <END> I love you .
- 12. NLP Toolkit: Attention Applications: machine translation, question answering, etc. LSTM LSTM LSTM LSTM source embeddings LSTM LSTM LSTM LSTM LSTM target embeddings Je t’ aime . <END> I love you . attention
- 13. NLP under threat? Deep learning models have taken NLP by storm, achieving superior results across many applications. Many DL approaches do not model any linguistic knowledge. They view language as a sequence of strings. Is this the end of NLP as a separate discipline?
- 14. NLP under threat? Deep learning models have taken NLP by storm, achieving superior results across many applications.
- 15. Language models Rajeswar et al. 2017, https://arxiv.org/pdf/1705.10929.pdf
- 16. Language models ● Great performance when explicitly trained for the task: 99% correct ○ > 120,000 sentence starts, labelled with singular or plural. ○ 50-dimensional LSTM followed by logistic regression. ○ In > 95% of the cases, the last noun determines the number. ● Performance drop for generic language models: 93% correct ○ Worse than chance on cases where a noun of the “incorrect” number occurs between the subject and the verb Linzen, Dupoux & Goldberg 2016, https://arxiv.org/pdf/1611.01368.pdf
- 17. Machine Translation ● NMT can behave strangely ● Problems for languages with a very different syntax, such as English and Chinese: ○ 25% of Chinese noun phrases are translated into discontinuous phrases in English ○ Chinese noun phrases are often translated twice Li et al. 2017, https://arxiv.org/abs/1705.01020
- 18. Question Answering Jia & Liang 2017, https://arxiv.org/pdf/1707.07328.pdf
- 20. Deep Learning in NLP ● Deep Learning produces great results on many tasks. ● But: ○ Race to the bottom on standard data sets: ■ Language models: Penn Treebank, WikiText-2 ■ Machine Translation: WMT datasets ■ Question Answering: SQuAD ○ Its ignorance of linguistic structure is problematic in the evolution towards NLU ● So: ○ What do neural networks model? ○ How can we make them better?
- 21. Linguistic knowledge in MT ● What linguistic knowledge does MT model? ● Simple syntactic labels ○ Encoder output + logistic regression ■ Word-level output: part-of-speech ■ Sentence-level output: voice (active or passive), tense (past or present) ● Deep syntactic structure ○ Encoder output + decoder to predict parse trees ● Two benchmarks: ○ Upper bound: neural parser ○ Lower bound: English-to-English “MT” auto-encoder Shi et al. 2016, https://www.isi.edu/natural-language/mt/emnlp16-nmt-grammar.pdf
- 22. Linguistic knowledge in MT
- 23. Linguistic knowledge in MT
- 24. Linguistic knowledge in MT Solution 1: present the encoder with both syntactic and lexical information Li et al. 2017, https://arxiv.org/abs/1705.01020
- 25. Linguistic knowledge in MT Li et al. 2017, https://arxiv.org/abs/1705.01020
- 26. Linguistic knowledge in MT Solution 2: combine MT with parsing in multi-task learning Eriguchi et al. 2017 http://www.aclweb.org/anthology/P/P17/P17-2012.pdf
- 27. Linguistic knowledge in MT Eriguchi et al. 2017 http://www.aclweb.org/anthology/P/P17/P17-2012.pdf
- 28. Linguistic knowledge in QA ● Most answers to questions are constituents in the sentence. ● Restricting our candidate answers to constituents reduces the search spaces. ● Instead of feeding the network flat sequences, we need to feed it syntax trees. Xie and Xing 2017, http://www.aclweb.org/anthology/P/P17/P17-1129.pdf
- 29. Linguistic knowledge in QA Xie and Xing 2017, http://www.aclweb.org/anthology/P/P17/P17-1129.pdf
- 30. Conclusions ● Deep Learning works great for NLP, but it is not a silver bullet. ● For simple tasks, simple string input may suffice, but for deeper natural language understanding likely not. ● To tackle this challenge, we need to: ○ Better understand what neural networks model, ○ Help them model more linguistic knowledge, ○ Combine language with other modalities. yves@nlp.town