A buzzer quiz is a genre of quiz in which multiple players simultaneously listen to a quiz being read aloud and respond it by buzzing in as soon as they can predict the answer. Because incorrect answers often result in penalties, a buzzer-quiz answering system must not only predict the answer from only part of a question but also estimate the predicted answer’s accuracy. In this paper, we introduce two types of buzzer-quiz answering systems: (1) a system that directly generates an answer from part of a question by using an autoregressive language model; and (2) a system that first reconstructs the entire question by using an autoregressive language model and then determines the answer according to the reconstructed question. We then propose a method to estimate the accuracy of the answers for each system by using the internal scores of each model.

For updating the translations of Japanese statutes based on their amendments, we need to consider the translation “focality;” that is, we should only modify expressions that are relevant to the amendment and retain the others to avoid misconstruing its contents. In this paper, we introduce an evaluation metric and a corpus to improve focality evaluations. Our metric is called an Inclusive Score for DIfferential Translation: (ISDIT). ISDIT consists of two factors: (1) the n-gram recall of expressions unaffected by the amendment and (2) the n-gram precision of the output compared to the reference. This metric supersedes an existing one for focality by simultaneously calculating the translation quality of the changed expressions in addition to that of the unchanged expressions. We also newly compile a corpus for Japanese partially amendment translation that secures the focality of the post-amendment translations, while an existing evaluation corpus does not. With the metric and the corpus, we examine the performance of existing translation methods for Japanese partially amendment translations.

This paper describes an evaluation experiment about a Japanese-Uighur machine translation system which consists of verbal suffix processing, case suffix processing, phonetic change processing, and a Japanese-Uighur dictionary including about 20,000 words. Japanese and Uighur have many syntactical and language structural similarities, including word order, existence and same functions of case suffixes and verbal suffixes, morphological structure, etc. For these reasons, we can consider that we can translate Japanese into Uighur in such a manner as word-by-word aligning after morphological analysis of the input sentences without complicated syntactical analysis. From the point of view of practical usage, we have chosen three articles about environmental issue appeared in Nippon Keizai Shinbun, and conducted a translation experiment on the articles with our MT system, for clarifying our argument. Here, we have counted the correctness of phrases in the Output sentences to be evaluating criteria. As a results of the experiment, 84.8% of precision has been achieved.