Nikunj Saunshi
2018
A Large Self-Annotated Corpus for Sarcasm
Mikhail Khodak
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Nikunj Saunshi
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Kiran Vodrahalli
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Mikhail Khodak
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Nikunj Saunshi
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Yingyu Liang
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Tengyu Ma
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Brandon Stewart
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Sanjeev Arora
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Motivations like domain adaptation, transfer learning, and feature learning have fueled interest in inducing embeddings for rare or unseen words, n-grams, synsets, and other textual features. This paper introduces a la carte embedding, a simple and general alternative to the usual word2vec-based approaches for building such representations that is based upon recent theoretical results for GloVe-like embeddings. Our method relies mainly on a linear transformation that is efficiently learnable using pretrained word vectors and linear regression. This transform is applicable on the fly in the future when a new text feature or rare word is encountered, even if only a single usage example is available. We introduce a new dataset showing how the a la carte method requires fewer examples of words in context to learn high-quality embeddings and we obtain state-of-the-art results on a nonce task and some unsupervised document classification tasks.
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Co-authors
- Mikhail Khodak 2
- Kiran Vodrahalli 1
- Yingyu Liang 1
- Tengyu Ma 1
- Brandon M. Stewart 1
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