- lecture slides
- Our videos: course intro, lecture, seminar
- Lecture video from Stanford CS224N - intro, embeddings (english)
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 colab.
- 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?
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Distributed Representations of Words and Phrases and their Compositionality Mikolov et al., 2013 [arxiv]
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Efficient Estimation of Word Representations in Vector Space Mikolov et al., 2013 [arxiv]
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Distributed Representations of Sentences and Documents Quoc Le et al., 2014 [arxiv]
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GloVe: Global Vectors for Word Representation Pennington et al., 2014 [article]
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Enriching word vectors with subword information Bojanowski et al., 2016 [arxiv]
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Exploiting similarities between languages for machine translation Mikolov et al., 2013 [arxiv]
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Improving vector space word representations using multilingual correlation Faruqui and Dyer, EACL 2014 [pdf]
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Learning principled bilingual mappings of word embeddings while preserving monolingual invariance Artetxe et al., EMNLP 2016 [pdf]
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Offline bilingual word vectors, orthogonal transformations and the inverted softmax [arxiv] Smith et al., ICLR 2017
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Word Translation Without Parallel Data Conneau et al., 2018 [arxiv]