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.

本论文研究多义词汇的表示，提出使用注意力机制将其集成到神经网络架构中，在逆向字典任务中获得更好的性能。实验结果表明，多义词汇在输入序列和目标表示方面的应用可以获得大幅度的提高。还提供了对意义分布和学习注意力的分析。

使用多义词向量嵌入进行反向词典

Using Multi-Sense Vector Embeddings for Reverse Dictionaries

Distributional models that learn rich semantic word representations are a
success story of recent NLP research. However, developing models that learn
useful representations of phrases and sentences has proved far harder. We
propose using the definitions found in everyday dictionaries as a means of
bridging this gap between lexical and phrasal semantics. Neural language
embedding models can be effectively trained to map dictionary definitions
(phrases) to (lexical) representations of the words defined by those
definitions. We present two applications of these architectures: "reverse
dictionaries" that return the name of a concept given a definition or
description and general-knowledge crossword question answerers. On both tasks,
neural language embedding models trained on definitions from a handful of
freely-available lexical resources perform as well or better than existing
commercial systems that rely on significant task-specific engineering. The
results highlight the effectiveness of both neural embedding architectures and
definition-based training for developing models that understand phrases and
sentences.

使用字典中的定义作为桥梁，将定义（短语）映射到单词的词汇表达式上，利用这种方式进行训练的神经嵌入模型能够有效地解决短语和句子的语义表示问题，并具有广泛应用前景。