CMU Multilingual NLP 2020 - Multilingual Training and Cross-Lingual Transfer
Explore multilingual NLP techniques, including cross-lingual transfer, zero-shot adaptation, and meta-learning. Learn strategies for balancing data, improving zero-shot performance, and addressing challenges in multilingual training.
CMU Multilingual NLP: Language Contact and Similarity Across Languages
Explore computational models of language contact, lexical borrowing, and cross-lingual similarities in this multilingual NLP lecture on language evolution and interaction.
CMU Multilingual NLP 2020 - Data Augmentation for Machine Translation
Explore data augmentation techniques for machine translation, including back-translation, multilingual training, and dictionary-based methods to improve low-resource language performance.
CMU Multilingual NLP - Machine Translation-Sequence-to-Sequence Models
Explore conditional language modeling, sequence-to-sequence models, generation methods, attention mechanisms, and transformers in multilingual natural language processing.
CMU Multilingual NLP 2020 - Advanced Text Classification-Labeling
Explore advanced text classification and sequence labeling methods, including subword models, unsupervised training, and structured prediction for multilingual NLP applications.
CMU Multilingual NLP 2020 - Text Classification and Sequence Labeling
Explore text classification and sequence labeling in multilingual NLP, covering models, datasets, and neural networks for various language processing tasks.
CMU Low Resource NLP Bootcamp 2020 - Neural Representation Learning
Explore neural representation learning in NLP, covering word and sentence representations, various approaches, and case studies on NNLM, GloVe, ELMO, BERT, RNN, CNN, and Transformers.
Neural Nets for NLP - Models with Latent Random Variables
Explore neural network models with latent random variables, covering variational autoencoders and their applications in NLP. Learn about generative vs. discriminative models and handling discrete latent variables.
Neural Nets for NLP 2020 - Structured Prediction with Local Independence Assumptions
Explore structured prediction in NLP, covering local independence assumptions and Conditional Random Fields for improved sequence labeling and output consistency.
Neural Nets for NLP 2020 - Attention
Explore attention mechanisms in neural networks for NLP, covering types, improvements, and specialized varieties. Dive into the "Attention is All You Need" paper and its impact on the field.
Neural Nets for NLP 2020: Conditioned Generation
Learn about encoder-decoder models, conditional generation, search techniques, and evaluation methods for neural networks in natural language processing.
Neural Nets for NLP 2020 - Efficiency Tricks for Neural Nets
Efficiency techniques for neural networks in NLP, covering GPU training, parallel processing, and softmax approximations like negative sampling and hierarchical softmax.
Neural Nets for NLP 2020 - Convolutional Neural Networks for Text
Comprehensive exploration of convolutional neural networks for text processing, covering key concepts, applications, and advanced techniques in natural language processing.
Neural Nets for NLP 2019 - Document Level Models
Explore document-level NLP models, covering coreference resolution and discourse parsing techniques. Learn about entity representations, neural coreference, and hierarchical attention networks for discourse analysis.
Neural Nets for NLP - Transition-based Parsing
Explore transition-based parsing for NLP, covering shift-reduce methods, Stack LSTM, phrase structure models, and linearized trees. Gain insights into advanced parsing techniques.
Neural Nets for NLP - Latent Random Variables
Explore latent random variables in neural networks for NLP, covering variational autoencoders, discrete latent variables, and applications in language processing and text generation.
Multilingual NLP: Words, Parts of Speech, Morphology
Explore words, parts of speech, and morphology in multilingual NLP. Learn about linguistic subfields, morphemes, and morphological processes across diverse languages.
CMU Low Resource NLP Bootcamp 2020 - Speech Synthesis
Explore speech synthesis techniques, from unit selection to neural networks, with insights on data sets, challenges, and multilingual applications in this comprehensive lecture.
CMU Low Resource NLP Bootcamp 2020 - Linguistics: Phonology and Morphology
Explore linguistic foundations in phonology and morphology, covering articulation, IPA, sound patterns, and morpheme variations. Gain insights into language structure and representation.
CMU Neural Nets for NLP: Model Interpretation
Learn techniques for interpreting neural networks in NLP, including probing sentence embeddings, analyzing model behavior, and generating explanations for model decisions.
Neural Nets for NLP 2020 - Machine Reading with Neural Nets
Explore machine reading techniques for NLP, covering datasets, context encoding, multi-hop reasoning, and dataset caveats. Learn about attention mechanisms and question-answering models.