Papers by Wojciech Stokowiec

2 papers
Better Document-Level Machine Translation with Bayes’ Rule (2020.tacl-1)

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Challenge: Existing document translation models are based on autoregressive language models, but they are not able to be learned from monolingual documents.
Approach: They propose to use Bayes' rule to create document translation models that can be learned from only parallel sentences and monolingual documents.
Outcome: The proposed model outperforms existing document translation approaches and is based on a novel left-to-right beam-search algorithm.
A Natural Bias for Language Generation Models (2023.acl-short)

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Challenge: a standard probabilistic model for language generation has likely not yet learnt many semantic or syntactic rules of natural language, making it difficult to estimate the probability distribution over next tokens.
Approach: They propose to initialise bias terms in a model's final linear layer with the log-unigram distribution and use it to output the unigram frequency statistics as prior knowledge.
Outcome: The proposed method improves learning efficiency and improves overall performance.

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