Pre-trained word vectors are ubiquitous in Natural Language Processing
applications. In this paper, we show how training word embeddings jointly with
bigram and even trigram embeddings, results in improved unigram embeddings. We
claim that training word embeddings along with higher n-gram embeddings helps
in the removal of the contextual information from the unigrams, resulting in
better stand-alone word embeddings. We empirically show the validity of our
hypothesis by outperforming other competing word representation models by a
significant margin on a wide variety of tasks. We make our models publicly
available.

训练单词嵌入与高阶 n-gram 嵌入同时可以帮助消除上下文信息，从而得到更好的单词嵌入。通过在各种任务上显着优于其他竞争性单词表示模型，我们实证了我们的假设的有效性。

通过分离上下文 n 元信息来改进词向量嵌入

Better Word Embeddings by Disentangling Contextual n-Gram Information

Embedding-based Knowledge Base Completion models have so far mostly combined
distributed representations of individual entities or relations to compute
truth scores of missing links. Facts can however also be represented using
pairwise embeddings, i.e. embeddings for pairs of entities and relations. In
this paper we explore such bigram embeddings with a flexible Factorization
Machine model and several ablations from it. We investigate the relevance of
various bigram types on the fb15k237 dataset and find relative improvements
compared to a compositional model.

本论文研究了基于嵌入式模型的知识库完成方法，探讨了使用灵活的因式分解机模型和多种消融方法的大写字母嵌入表示法。通过实验表明，相对于组合模型，大写字母嵌入表示法在 fb15k237 数据集上有相对的改进。