Papers › Embedding Imputation with Grounded Language Information
Embedding Imputation with Grounded Language Information
Ziyi Yang, Chenguang Zhu, Sachidan, Vin a, Eric Darve
Due to the ubiquitous use of embeddings as input representations for a wide range of natural language tasks, imputation of embeddings for rare and unseen words is a critical problem in language processing. Embedding imputation involves learning representations for rare or unseen words during the training of an embedding model, often in a post-hoc manner. In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph. This is in contrast to existing approaches which typically make use of vector space properties or subword information. We propose an online method to construct a graph from grounded information and design an algorithm to map from the resulting graphical structure to the space of the pre-trained embeddings. Finally, we evaluate our approach on a range of rare and unseen word tasks across various domains and show that our model can learn better representations. For example, on the Card-660 task our method improves Pearson{'}s and Spearman{'}s correlation coefficients upon the state-of-the-art by 11{\%} and 17.8{\%} respectively using GloVe embeddings.
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