Papers with Distributional semantic models
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. |
What are the Goals of Distributional Semantics? (2020.acl-main)
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| Challenge: | a new paper examines distributional semantic models' ability to deal with semantic challenges . authors argue that assessing progress in any field requires explicit long-term goals . |
| Approach: | They propose a broad linguistic perspective to assess distributional semantic models' ability to deal with various semantic challenges. |
| Outcome: | The proposed models can handle various semantic challenges, but they need to be explicit . a top-down approach is largely bottom-up, while a bottom-down one is mainly top-up . the authors argue that the goal is unclear and that the models are not scalable . |