Fake news causes significant damage to society. To deal with these fake news, several studies on building detection models and arranging datasets have been conducted. Most of the fake news datasets depend on a specific time period. Consequently, the detection models trained on such a dataset have difficulty detecting novel fake news generated by political changes and social changes; they may possibly result in biased output from the input, including specific person names and organizational names. We refer to this problem as Diachronic Bias because it is caused by the creation date of news in each dataset. In this study, we confirm the bias, especially proper nouns including person names, from the deviation of phrase appearances in each dataset. Based on these findings, we propose masking methods using Wikidata to mitigate the influence of person names and validate whether they make fake news detection models robust through experiments with in-domain and out-of-domain data.

Formalization for Example-based Machine Translation Example-based machine translation(EBMT)systems,so far,rely on heuristic measures in retrieving translation examples.Such a heuristic measure costs time to adjust,and might make its algorithm unclear.This paper presents a probabilistic model for EBMT.Under the proposed model,the system searches the translation example combination which has the highest probability.The proposed model clearly formalizes EBMT process.In addition,the model can naturally incorporate the context similarity of translation examples.The experimental results demonstrate that the proposed model has a slightly better translation quality than state-of-the-art EBMT systems.

Character information may be a good alternative in terms of simplicity to morphological information for abbreviation expansion in English medical abbreviations appeared in Japanese texts on the Internet.

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