Few-Shot Representation Learning for Out-Of-Vocabulary Words
Table 1: Performance on the Chimera benchmark dataset with different numbers of context sentences, which is measured by Spearman correlation. Baseline results are from the corresponding papers.
['Methods', '2-shot', '4-shot', '6-shot']
In particular, our method (HiCE+Morph+MAML) Compared with the current state-of-the-art method, `a la carte, the relative improvements (i.e., the performance difference divided by the baseline performance) of HiCE are 4.0%, 5.4% and 9.3% in terms of 2,4,6-shot learning, respectively. We also compare our results with that of the oracle embedding, which is the embeddings trained from DT, and used as ground-truth to train HiCE. This results can be regarded as an upper bound. As is shown, when the number of context sentences (K) is relatively large (i.e., K=6), the performance of HiCE is on a par with the upper bound (Oracle Embedding) and the relative performance difference is merely 2.7%. This indicates the significance of using an advanced aggregation model.