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Related Questions
- What characteristics of out-of-vocabulary words does entity-based attention take into consideration?
- How does entity-based attention leverage entities to cope with unseen tokens in its attention mechanisms?
- What limitations exist when training entity-based attention on its ability to deal with newly introduced OOV words compared to standard attention mechanisms?
- Are there ways to train a language model to make informed predictions beyond the space it has actually seen with regards to language attention based learning mechanisms?
- What advancements in out-of-vocab attention based have helped machine learning understand contextual relations?
- Does there exist more specialized literature specific to these type of inductive questions where machine performs on unsseend space, is OOV part of any relevant studies if so who contributed?
- How effectively generalizes beyond what OOV mechanism may use over training, by making educated estimation beyond input training corpus that contains tokens the LM uses?
- With out Of vocabulary issues such that occur, as what percentage change occurs?
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