Average word embeddings are a common baseline for more sophisticated sentence embedding techniques. An important advantage of average word embeddings is their computational and conceptual simplicity. However, they typically fall short of the performances of more complex models such as InferSent. Here, we generalize the concept of average word embeddings to $p$-mean word embeddings, which are (almost) as efficiently computable. We show that the concatenation of different types of $p$-mean word embeddings considerably closes the gap to state-of-the-art methods such as InferSent monolingually and substantially outperforms these more complex techniques cross-lingually. In addition, our proposed method outperforms different recently proposed baselines such as SIF and Sent2Vec by a solid margin, thus constituting a much harder-to-beat monolingual baseline for a wide variety of transfer tasks. Our data and code are publicly available.

该论文利用Power Mean word embeddings的概念将不同类型的平均词向量进行拼接，以提高自然语言处理中的问题解决。研究表明，这一方法在单种语言上比常规模型表现更佳，在跨语言处理上表现也优于更复杂的技术，并在实验中击败了其他基线模型。

串联幂平均词嵌入作为通用跨语言句子表示