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Word embeddings

Practice & homework

The practice for this week takes place in notebooks. Just open them and follow instructions from there.

  • Seminar: ./seminar.ipynb
  • Homework: ./homework.ipynb

Unless explicitly said otherwise, all subsequent weeks follow the same pattern (notebook with instructions).

If you have any difficulties with notebooks, just open them in Open In Colab.

Embedding space walk

embedding_space_walk

More materials (optional)

  • On hierarchical & sampled softmax estimation for word2vec page
  • GloVe project page
  • FastText project repo
  • Semantic change over time - oberved through word embeddings - arxiv
  • Another cool link that you could have shared, but decided to hesitate. Or did you?

Related articles

Starting Point

  • Distributed Representations of Words and Phrases and their Compositionality Mikolov et al., 2013 [arxiv]

  • Efficient Estimation of Word Representations in Vector Space Mikolov et al., 2013 [arxiv]

  • Distributed Representations of Sentences and Documents Quoc Le et al., 2014 [arxiv]

  • GloVe: Global Vectors for Word Representation Pennington et al., 2014 [article]

  • Enriching word vectors with subword information Bojanowski et al., 2016 [arxiv] (FastText)

Explaination and Analysis

  • Word2vec Explained: Deriving Mikolov et al.’s Negative-Sampling Word-Embedding Method Yoav Goldberg, Omer Levy, 2014 [arxiv]

  • Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors Marco Baroni, Georgiana Dinu, Germa ́n Kruszewski, ACL 2014, [paper]

  • The strange geometry of skip-gram with negative sampling, David Mimno, Laure Thompson, EMNLP 2017, [paper]

  • Characterizing Departures from Linearity in Word Translation Ndapa Nakashole, Raphael Flauger, ACL 2018, [paper]

  • On the Dimensionality of Word Embedding Zi Yin, Yuanyuan Shen, NIPS 2018, [arxiv]

  • Analogies Explained: Towards Understanding Word Embeddings Carl Allen, Timothy Hospedales, ICML 2019, [arxiv] [official blog post]

Multilingual Embeddings. Unsupervised MT.

  • Exploiting similarities between languages for machine translation Mikolov et al., 2013 [arxiv]

  • Improving vector space word representations using multilingual correlation Faruqui and Dyer, EACL 2014 [pdf]

  • Learning principled bilingual mappings of word embeddings while preserving monolingual invariance Artetxe et al., EMNLP 2016 [pdf]

  • Offline bilingual word vectors, orthogonal transformations and the inverted softmax [arxiv] Smith et al., ICLR 2017

  • Word Translation Without Parallel Data Conneau et al., 2018 [arxiv]