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        <title>Memory, Show the Way: Memory Based Few Shot Word Representation Learning</title>
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        <namePart type="given">Jingyuan</namePart>
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    <abstract>Distributional semantic models (DSMs) generally require sufficient examples for a word to learn a high quality representation. This is in stark contrast with human who can guess the meaning of a word from one or a few referents only. In this paper, we propose Mem2Vec, a memory based embedding learning method capable of acquiring high quality word representations from fairly limited context. Our method directly adapts the representations produced by a DSM with a longterm memory to guide its guess of a novel word. Based on a pre-trained embedding space, the proposed method delivers impressive performance on two challenging few-shot word similarity tasks. Embeddings learned with our method also lead to considerable improvements over strong baselines on NER and sentiment classification.</abstract>
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%0 Conference Proceedings
%T Memory, Show the Way: Memory Based Few Shot Word Representation Learning
%A Sun, Jingyuan
%A Wang, Shaonan
%A Zong, Chengqing
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F sun-etal-2018-memory
%X Distributional semantic models (DSMs) generally require sufficient examples for a word to learn a high quality representation. This is in stark contrast with human who can guess the meaning of a word from one or a few referents only. In this paper, we propose Mem2Vec, a memory based embedding learning method capable of acquiring high quality word representations from fairly limited context. Our method directly adapts the representations produced by a DSM with a longterm memory to guide its guess of a novel word. Based on a pre-trained embedding space, the proposed method delivers impressive performance on two challenging few-shot word similarity tasks. Embeddings learned with our method also lead to considerable improvements over strong baselines on NER and sentiment classification.
%R 10.18653/v1/D18-1173
%U https://aclanthology.org/D18-1173
%U https://doi.org/10.18653/v1/D18-1173
%P 1435-1444

Markdown (Informal)

[Memory, Show the Way: Memory Based Few Shot Word Representation Learning](https://aclanthology.org/D18-1173) (Sun et al., EMNLP 2018)

• Memory, Show the Way: Memory Based Few Shot Word Representation Learning (Sun et al., EMNLP 2018)

ACL

• Jingyuan Sun, Shaonan Wang, and Chengqing Zong. 2018. Memory, Show the Way: Memory Based Few Shot Word Representation Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1435–1444, Brussels, Belgium. Association for Computational Linguistics.