Together We Make Sense -- Learning Meta-Sense Embeddings from Pretrained Static Sense Embeddings
This addresses the issue of discrepancies in sense coverage for ambiguous words in natural language processing, offering an incremental improvement over existing methods.
The paper tackles the problem of incomplete sense coverage in existing sense embeddings by proposing Neighbour Preserving Meta-Sense Embeddings, which combines multiple source embeddings to preserve sense neighborhoods, resulting in consistent outperformance on Word Sense Disambiguation and Word-in-Context tasks.
Sense embedding learning methods learn multiple vectors for a given ambiguous word, corresponding to its different word senses. For this purpose, different methods have been proposed in prior work on sense embedding learning that use different sense inventories, sense-tagged corpora and learning methods. However, not all existing sense embeddings cover all senses of ambiguous words equally well due to the discrepancies in their training resources. To address this problem, we propose the first-ever meta-sense embedding method -- Neighbour Preserving Meta-Sense Embeddings, which learns meta-sense embeddings by combining multiple independently trained source sense embeddings such that the sense neighbourhoods computed from the source embeddings are preserved in the meta-embedding space. Our proposed method can combine source sense embeddings that cover different sets of word senses. Experimental results on Word Sense Disambiguation (WSD) and Word-in-Context (WiC) tasks show that the proposed meta-sense embedding method consistently outperforms several competitive baselines.