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Finding Concept-specific Biases in Form--Meaning Associations ...
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| 10 |
Searching for Search Errors in Neural Morphological Inflection ...
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| 11 |
Applying the Transformer to Character-level Transduction ...
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| 12 |
Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models ...
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| 16 |
Conditional Poisson Stochastic Beams ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.52/ Abstract: Beam search is the default decoding strategy for many sequence generation tasks in NLP. The set of approximate K-best items returned by the algorithm is a useful summary of the distribution for many applications; however, the candidates typically exhibit high overlap and may give a highly biased estimate for ex- pectations under our model. These problems can be addressed by instead using stochastic decoding strategies. In this work, we propose a new method for turning beam search into a stochastic process: Conditional Poisson stochastic beam search. Rather than taking the maximizing set at each iteration, we sample K candidates without replacement according to the conditional Poisson sampling design. We view this as a more natural alternative to Kool et al. (2019)’s stochastic beam search (SBS). Furthermore, we show how samples generated under the CPSBS design can be used to build consistent estimators and sample diverse sets from ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/n187-h976
https://underline.io/lecture/37840-conditional-poisson-stochastic-beams
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| 17 |
Examining the Inductive Bias of Neural Language Models with Artificial Languages ...
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