Papers with DSMs
Cross-Topic Distributional Semantic Representations Via Unsupervised Mappings (N19-1)
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| Challenge: | Existing distributional semantic models cannot capture the distinct meanings of polysemous words, resulting in conflated word representations of diverse contextual semantics. |
| Approach: | They propose a distributional semantic model that learns multiple representations of a word based on different topics. |
| Outcome: | The proposed model outperforms single-prototype models on NLP downstream tasks. |
Memory, Show the Way: Memory Based Few Shot Word Representation Learning (D18-1)
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| Challenge: | Existing word embedding methods for distributed semantic models require limited examples to learn a high quality representation. |
| Approach: | They propose a memory-based embedding learning method capable of acquiring word representations from limited context. |
| Outcome: | The proposed method delivers impressive performance on two challenging few-shot word similarity tasks. |
A Formidable Ability: Detecting Adjectival Extremeness with DSMs (2021.findings-acl)
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| Challenge: | Existing studies on distributional semantic models capture abstract semantic properties across domains . abstract properties can form the basis for abstract semantic classes . |
| Approach: | They propose to use distributional semantic models to capture cross-domain properties . they use extremeness to model emergence of intensifier meaning in adverbs . |
| Outcome: | The proposed model can capture extremeness and intensifier meaning in adverbs. |