Transfer learning in nlp
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The document discusses transfer learning in natural language processing (NLP), highlighting techniques such as tokenization, stemming, and lemmatization. It explains the importance of word embeddings and the limitations associated with them, including handling out-of-vocabulary words and context diversity. Additionally, it covers the universal language model fine-tuning (ULMFiT) approach and its benefits across various datasets and tasks.
Transfer learning in nlp
- 1. Transfer Learning in NLP Navneet Kumar Chaudhary Data Scientist Aasaanjobs.com
- 2. Recent State of The Arts Models SOTA NLP Models Image Sourced from https://jalammar.github.io/illustrated-bert/
- 3. Transfer Learning in CV and how we use embeddings
- 4. What is NLTK ❖ NLTK or The Natural Language ToolKit is a suite of libraries and programs for a variety of academic Text processing tasks: ❖ It has in built functionalities for Removing Stop words, Tokenization, Stemming, Lemmatizing
- 5. Stemming vs Lemmatization Lemmatisation is closely related to stemming. The difference is that a stemmer operates on a single word without knowledge of the context, and therefore cannot discriminate between words which have different meanings depending on part of speech. However, stemmers are typically easier to implement and run faster, and the reduced accuracy may not matter for some applications. For instance: 1. The word "better" has "good" as its lemma. This link is missed by stemming, as it requires a dictionary look-up. 2. The word "walk" is the base form for word "walking", and hence this is matched in both stemming and lemmatisation. 3. The word "meeting" can be either the base form of a noun or a form of a verb ("to meet") depending on the context, e.g., "in our last meeting" or "We are meeting again tomorrow". Unlike stemming, lemmatisation can in principle select the appropriate lemma depending on the context.
- 6. Word Embeddings Recap ❖ For words to be processed by machine learning models, they need some form of numeric representation that models can use in their calculation. ❖ Word2Vec showed that we can use a vector (a list of numbers) to properly represent words in a way that captures semantic or meaning-related relationships. ❖ Queen = King - Man + Woman ❖ Relationship between Country and their respective Capitals
- 7. Limitations/Isuues in Word Embeddings ❖ Out of Vocabulary/Unknown words as we need to fix the vocabulary size(when a word is not known vector cannot be constructed deterministically) ❖ Cannot handle the shared representation of the same word. Meaning of a word depends on the context it is used. ❖ Our model won’t be robust for new Languages, and thus we cannot use for incremental learning.
- 8. ELMO Context Matters Context Aware Embeddings by ELMO
- 9. ULMFiT Approach to pre-training
- 10. The idea for converting this to Transfer Learning
- 13. Step:1 Finding Context aware Embeddings
- 14. Step 2: Finding Context aware Embeddings
- 15. Why is ULMFiT Universal? ❖ Dataset independent. You start with wiki text LM and fine-tune for your dataset. ❖ Works across all documents and datasets of varying lengths. ❖ Architecture is consistent, same as we use ResNets for many CV tasks. ❖ Can work on very small datasets as well, as we already have a good LM to start with.
- 16. Classifier fine-tuning for Task Specific Weights ❖ Two additional linear blocks have been added. Each block uses batch normalization and a lower value of dropout ❖ ReLU is used as activation function in between the linear blocks. ❖ Softmax is used to provide the probability distribution over the target classes. ❖ Classifiers only take the embeddings provided by the LM and are always trained from scratch.
- 17. Results from ULMFiT Validation Error Rate ULMFiT vs Scratch
- 18. Acknowledgements ❖ "Images speak louder than words” and they were sourced from other blogposts and Google results. ❖ A lot of them are taken from this great blogpost by Jay Alammar https://jalammar.github.io/illustrated-bert/ ❖ The results image is taken from the ULMFiT paper.
- 19. –Navneet Kumar Chaudhary Thanks a Lot!!!