@inproceedings{hedderich-etal-2019-using,
title = "Using Multi-Sense Vector Embeddings for Reverse Dictionaries",
author = "Hedderich, Michael A. and
Yates, Andrew and
Klakow, Dietrich and
de Melo, Gerard",
editor = "Dobnik, Simon and
Chatzikyriakidis, Stergios and
Demberg, Vera",
booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
month = may,
year = "2019",
address = "Gothenburg, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-0421/",
doi = "10.18653/v1/W19-0421",
pages = "247--258",
abstract = "Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings instead provide different vectors for each sense of a word. However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word. In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries. We propose a technique to easily integrate them into an existing neural network architecture using an attention mechanism. Our experiments demonstrate that large improvements can be obtained when employing multi-sense embeddings both in the input sequence as well as for the target representation. An analysis of the sense distributions and of the learned attention is provided as well."
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<abstract>Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings instead provide different vectors for each sense of a word. However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word. In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries. We propose a technique to easily integrate them into an existing neural network architecture using an attention mechanism. Our experiments demonstrate that large improvements can be obtained when employing multi-sense embeddings both in the input sequence as well as for the target representation. An analysis of the sense distributions and of the learned attention is provided as well.</abstract>
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%0 Conference Proceedings
%T Using Multi-Sense Vector Embeddings for Reverse Dictionaries
%A Hedderich, Michael A.
%A Yates, Andrew
%A Klakow, Dietrich
%A de Melo, Gerard
%Y Dobnik, Simon
%Y Chatzikyriakidis, Stergios
%Y Demberg, Vera
%S Proceedings of the 13th International Conference on Computational Semantics - Long Papers
%D 2019
%8 May
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F hedderich-etal-2019-using
%X Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings instead provide different vectors for each sense of a word. However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word. In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries. We propose a technique to easily integrate them into an existing neural network architecture using an attention mechanism. Our experiments demonstrate that large improvements can be obtained when employing multi-sense embeddings both in the input sequence as well as for the target representation. An analysis of the sense distributions and of the learned attention is provided as well.
%R 10.18653/v1/W19-0421
%U https://aclanthology.org/W19-0421/
%U https://doi.org/10.18653/v1/W19-0421
%P 247-258
Markdown (Informal)
[Using Multi-Sense Vector Embeddings for Reverse Dictionaries](https://aclanthology.org/W19-0421/) (Hedderich et al., IWCS 2019)
ACL
- Michael A. Hedderich, Andrew Yates, Dietrich Klakow, and Gerard de Melo. 2019. Using Multi-Sense Vector Embeddings for Reverse Dictionaries. In Proceedings of the 13th International Conference on Computational Semantics - Long Papers, pages 247–258, Gothenburg, Sweden. Association for Computational Linguistics.