Lacking standardized extrinsic evaluation methods for vector representations
of words, the NLP community has relied heavily on word similarity tasks as a
proxy for intrinsic evaluation of word vectors. Word similarity evaluation,
which correlates the distance between vectors and human judgments of semantic
similarity is attractive, because it is computationally inexpensive and fast.
In this paper we present several problems associated with the evaluation of
word vectors on word similarity datasets, and summarize existing solutions. Our
study suggests that the use of word similarity tasks for evaluation of word
vectors is not sustainable and calls for further research on evaluation
methods.

使用词相似度任务作为词向量内在评估的代理，但没有标准化的词向量外部评估方法。本文探讨了使用词相似度数据集进行词向量评估时存在的问题，并总结已有的解决方案，最后指出这种方法不具可持续性，需要进一步研究词向量的评估方法。

使用词相似性任务评估词嵌入存在的问题

Problems With Evaluation of Word Embeddings Using Word Similarity Tasks

This paper presents a scalable method for integrating compositional
morphological representations into a vector-based probabilistic language model.
Our approach is evaluated in the context of log-bilinear language models,
rendered suitably efficient for implementation inside a machine translation
decoder by factoring the vocabulary. We perform both intrinsic and extrinsic
evaluations, presenting results on a range of languages which demonstrate that
our model learns morphological representations that both perform well on word
similarity tasks and lead to substantial reductions in perplexity. When used
for translation into morphologically rich languages with large vocabularies,
our models obtain improvements of up to 1.2 BLEU points relative to a baseline
system using back-off n-gram models.

本论文提出了一种可扩展的方法，将组成形态学表示集成到基于向量的概率语言模型中，并在语言模型因词汇而被适当地实现以实现内部和外部评估，在一些语言上进行实验研究并展示结果，表明我们的模型学习了形态学表示，在词相似性任务上表现出色且使困惑度大幅降低，用于转化大词汇量的形态丰富语言时，我们的模型相对于使用回退 N-gram 模型的基线系统，可以获得高达 1.2 的蓝点改进。