Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications

• URL: http://arxiv.org/abs/2103.15330v1
• Date: Mon, 29 Mar 2021 04:54:28 GMT
• Title: Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications
• Authors: Haw-Shiuan Chang, Amol Agrawal, Andrew McCallum
• Abstract summary: We propose a novel embedding method for a text sequence (a phrase or a sentence) where each sequence is represented by a distinct set of codebook embeddings. Our experiments show that the per-sentence codebook embeddings significantly improve the performances in unsupervised sentence similarity and extractive summarization benchmarks.
• Score: 34.71597411512625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most unsupervised NLP models represent each word with a single point or single region in semantic space, while the existing multi-sense word embeddings cannot represent longer word sequences like phrases or sentences. We propose a novel embedding method for a text sequence (a phrase or a sentence) where each sequence is represented by a distinct set of multi-mode codebook embeddings to capture different semantic facets of its meaning. The codebook embeddings can be viewed as the cluster centers which summarize the distribution of possibly co-occurring words in a pre-trained word embedding space. We introduce an end-to-end trainable neural model that directly predicts the set of cluster centers from the input text sequence during test time. Our experiments show that the per-sentence codebook embeddings significantly improve the performances in unsupervised sentence similarity and extractive summarization benchmarks. In phrase similarity experiments, we discover that the multi-facet embeddings provide an interpretable semantic representation but do not outperform the single-facet baseline.

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