Using Multi-Sense Vector Embeddings for Reverse Dictionaries
Table 3: Median rank, accuracy @10 and @100 and mean reciprocal rank for the experiments with different input vectors. The multi-sense vectors are used as target vectors.
['[BOLD] Input Vectors', '[BOLD] MR ↓', '[BOLD] Acc@10 ↑', '[BOLD] Acc@100 ↑', '[BOLD] MRR ↑']
As target vectors, we use multi-sense vectors. Picking a random sense vector tends to perform slightly worse than using the single-sense vector embedding and both are outperformed by picking the first multi-sense vector of every word. This might be due to the fact that the first sense-vector tends to correspond to the most frequently occurring sense and that the representation of this sense is better in the multi-sense setting because it can focus on this meaning.