oWord2Vec: Learns distributed representations of words oContinuous bag-of-words (CBOW) oPredicts current word from a window of surrounding context words oContinuous skip-gram oUses current word to predict surrounding window of context words oSlower but does a better job for infrequent words Yifeng Tao Carnegie Mellon University 5 [42] introduced ELMo (Embeddings from Language Models), a deep contextualized model for word representation. The mythos of model interpretability. This course aims to cover cutting-edge deep learning methods for natural language processing. Deep Contextualized Word Representations (Peters et al. We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Deep Contextualized Word Embeddings ELMo (Embeddings from Language Models) is introduced in (Peters et al., 2018). Authors:Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer. Because words mean different things in different contexts, ... (slide credit: Abigail See) CoVe Bidirectional encoder! ELMo: Embeddings from Language Models Queue 2018. For example, Peters et al. Able to easily replace any word embeddings, it improved the state of the art on six different NLP problems. 8 have a a nice nice day Peters et al., NAACL 2018. Some recent studies ( 8 , 24 ) have suggested that the position and part of speech (POS) of each word in the sentence are crucial to biomedical relation extraction. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Vol. 1) Overview 2) Background 3) Model Formulation & Architecture 4) Results 5) Discussion. Key benefits: •More refined semantic representations of lexemes •Automatically capturing polysemy •Apples have been grown for thousands of years in Asia and Europe. 2018 Deep contextualized word representations (ELMo paper) 8 Model Source Nearest Neighbor(s) ... •Use character CNN to build initial word representation (only) •2048 char n-gram filters and 2 highway layers, 512 dim projection ... described in the rest of these slides … From Peters et al. UAI 2019. [Efficient Estimation of Word Representations in Vector Space] A1 released: Jan 11: Assignment #1 released [Assignment #1] ... [Statistical Machine Translation slides (see lectures 2/3/4)] [Statistical Machine Translation Book] ... Contextualized Word Vectors] [Deep Contextualized Word Representations] • NAACL’18: Deep contextualized word representations • Key idea: • Train an LSTM-based language model on some large corpus • Use the hidden states of the LSTM for each token to compute a vector representation of each word Abstract. Wasserstein Fair Classification. (2019). Peters et al (2018) Deep contextualized word representations, NAACL (PDF, Slides (Liyuan Liu)) 04/26 : Knowledge Graphs : Yaghoobzadeh and Schütze (2017) Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities, EACL (PDF, Slides (Xiaotao Gu)) The deep contextualized representation layer will generate the contextualized representation vector for each word based on the sentence context. Deep contextualized word representations. COS 598C (Spring 2020): Deep Learning for Natural Language Processing. •Lample, G., & Conneau, A. DEEP CONTEXTUALIZED WORD REPRESENTATIONS or (Embeddings from Language Models) Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer Presented by Xiaoyan Wang (xiaoyan5@illinois.edu) 1 Deep contextualized word representations In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1, pp. Deep contextualized word representations (Peters et al., 2018, "ELMo") [reading] Easy-to-read blog post on transfer learning in NLP Quiz 3 released, due 9/25 on Gradescope Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. 2018) Reference: BERT: Bidirectional Transformers (Devlin et al. IEEE Big Data 2018. Deep Contextualized Word Representations. I quickly introduce three embeddings techniques: 1. We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). More specifically, we learn a linear combination of the vectors stacked above each input word … Deep contextualized word representations. •Models direct relationships between all words in a given sequence (e.g., sentence) •Does not concern a seq2seq (i.e., encoder-decoder RNN) framework •Each word in a sequence can be transformed into an abstract representation (embedding) based on the weighted sums of the other words … Towards a rigorous science of interpretable machine learning. Learned in Translation: Contextualized Word Vectors (McMann et al. of deep contextualized word representation that directly addresses both challenges, can be easily integrated into existing models, and signiÞcantly improves the state of the art in every considered case across a range of challenging language un-derstanding problems. ELMo comprises … DEEP CONTEXTUALIZED WORD REPRESENTATIONS A (surprisingly) simple method for task-specific tuning of language embeddings June 4, 2018 Chris Laver RBC. Word Representation Representword with distributed vectorswhileretaining their semantic meaning: 1. Peters et al. 5 A ton of unlabeled text A huge self- ... specialized model Labeled reviews from IMDB step 2: supervised fine-tuning. Main ideas: Jointly perform both forward and backward language modeling (i.e., bidirectional language models) Increase the number of RNN layers Employ character-level input representations to alleviate the out-of-vocabulary issue (Note: I use embeddings and representations interchangeably throughout this article) : ``The plot was not particularly original.’’ negative movie review •Typical setup for natural language processing (NLP) •Model starts with learned representations for words 2018) Bidirectional Transformers (Devlin et al. 2227-2237). 2018) Inference -> Generalization (Conneau et al. The ELMo architecture begins by training a fairly sophisticated neural network language model, heavily Representations from Transformers Idea: contextualized word representations Learn word vectors using long contexts using Transformer instead of LSTM Devlin et al., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, in NAACL-HLT, 2019. Fairgan: Fairness-aware generative adversarial networks. Skip-Gram (aka Word2Vec) 2. language models for transfer learning Deep contextualized word representations. Download PDF. Title:Deep contextualized word representations. Unlike previous approaches for learning contextualized word vectors (Peters et al., 2017; McCann et al., 2017), ELMo representations are deep, in the sense that they are a function of all of the internal layers of the biLM. ELMO is embeddings from langue models, it is deep contextualized word representation. Context Selection for Embedding Models Deep Contextualized Word Representations Papers Presented Context Selection for Embedding Models (NIPS 2017) LP Liu, FJR Ruiz, S Athey and DM Blei Deep Contextualized Word Representations (NAACL 2018) ME Peters, M Neumann, M Iyyer, M Gardner, C Clark, K Lee, L Zettlemoyer Deep Contextualized Word Representations has been one of the major breakthroughs in NLP in 2018. Attention Now with a contextualized language model, the embedding of the word apple would have a different vector representation which makes it even more powerful for NLP tasks. However, I will leave the details of how that works, out of the scope of this post just to keep it short and on point. ELMo: deep contextualised word representation (Peters et al., 2018) •“Instead of using a fixed embedding for each word,ELMo looks at the entire sentence before assigning each word in it an embedding.” Acknowledgement to Figure from http://jalammar.github.io/illustrated-bert/ [Deep Contextualized Word Representations] [Emonet: Fine-grained emotion detection with gated recurrent neural networks] [Is statistical machine translation approach dead?] . Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer Share this page: Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer Jieyu Zhao, Subhabrata Mukherjee, Saghar Hosseini, Kai-Wei Chang, and Ahmed Hassan Awadallah, in ACL, 2020. [Neural machine translation and sequence-to-sequence models: A tutorial] [Adversarial training methods for semi-supervised text classification] 2017) Deep Contextualized Word Representations (Peters et al. many slides from Jacob Devlin & Matt Peters ... on billions of words, transfer to every NLP task! Sentence Representation Slides Sample Code: Sentence Representation Code Examples <-- Back To Schedule. Contextualized Word Representations Natural Language Processing Based on slides from Stanford cs224n (Chris Manning and Abigail See), Graham Neubig and Ashish Vaswani. CS 335: Fair, Accountable, and Transparent (FAccT) Deep Learning. Resultingvectorsare usually treated as theinputlayerof Arxiv 2017. (Peters et al, 2018): Deep contextualized word representations “With hindsight, we can now see that by representing word types independent of context, we were solving a problem that was harder than it needed to be. Contextualized Word Embeddings Aggregating context information in a word vector with a pre-trained deep neural language model. 2 ELMo: Deep contextualized word embeddings 4 Key idea: context-dependent embedding for each word interpolates representations for that word from each layer Interpolation weights are task-specific (fine-tuned on supervised data.) This blog post consists of two parts, the first one, which is mainly pointers, simply refers to the classic word embeddings techniques, which can also be seen as static word embeddingssince the same word will always have the same representation regardless of the context where it occurs. Peters, Matthew E. and Neumann, Mark and Iyyer, Mo- hit and Gardner, Matt and Clark, Christopher and Lee, Kenton and Zettlemoyer, Luke. 4. Contextualised words embeddings aim at capturing word semantics in different contexts to address the issue of polysemous and the context-dependent nature of words. Language models compute the probability distribution of the next word in a sequence given the sequence of previous words. For this reason, we call them ELMo (Embeddings from Language Models) representations. Slight Detour ... gradients to flow in deep networks similar to what we saw in LSTMs and GRUs We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Abstract:We introduce a new type of deep contextualized word representation thatmodels both (1) complex characteristics of word use (e.g., syntax andsemantics), and (2) how these uses vary across … Representation learning = deep learning = neural networks •Learn higher-level abstractions •Non-linear functions can model interactions of lower-level representations •E.g. 2018. Idea: contextualized word representations Learn word vectors using long contexts instead of a context window Learn a deep Bi-NLM and use all its layers in prediction Peters et al., “Deep Contextualized Word Representations”, in NAACL-HLT, 2018.
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