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Given a small corpus $\mathcal D_T$ pertaining to a limited set of focusedtopics, our goal is to train embeddings that accurately capture the sense ofwords in the topic in spite of the limited size of $\mathcal D_T$. Theseembeddings may be used in various tasks involving $\mathcal D_T$. A popularstrategy in limited data settings is to adapt pre-trained embeddings $\mathcalE$ trained on a large corpus. To correct for sense drift, fine-tuning,regularization, projection, and pivoting have been proposed recently. Amongthese, regularization informed by a word's corpus frequency performed well, butwe improve upon it using a new regularizer based on the stability of itscooccurrence with other words. However, a thorough comparison across tentopics, spanning three tasks, with standardized settings of hyper-parameters,reveals that even the best embedding adaptation strategies provide small gainsbeyond well-tuned baselines, which many earlier comparisons ignored. In a bolddeparture from adapting pretrained embeddings, we propose using $\mathcal D_T$to probe, attend to, and borrow fragments from any large, topic-rich sourcecorpus (such as Wikipedia), which need not be the corpus used to pretrainembeddings. This step is made scalable and practical by suitable indexing. Wereach the surprising conclusion that even limited corpus augmentation is moreuseful than adapting embeddings, which suggests that non-dominant senseinformation may be irrevocably obliterated from pretrained embeddings andcannot be salvaged by adaptation.

Topic Sensitive Attention on Generic Corpora Corrects Sense Bias in Pretrained Embeddings