We propose a simple and robust training-free approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based on two aspects. One is its relatedness to the word vector subspace already spanned by its contextual words. The other is its novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace.   Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representation. This approach requires zero training and zero parameters, along with efficient inference performance. We evaluate our approach on 11 downstream NLP tasks. Experimental results show that our model outperforms all existing zero-training alternatives in all the tasks and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time.
A simple and training-free approach for sentence embeddings with competitive performance compared with sophisticated models requiring either large amount of training data or prolonged training time. Presented a new training-free way of generating sentence embedding with systematic analysis Proposes a new geometry-based method for sentence embedding from word embedding vectors by quantifying the novelty, significance, and corpus-uniqueness of each word This paper explores sentence embedding based on orthogonal decomposition of the spanned space by word embeddings