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Titlebook: Artificial Intelligence and Natural Language; 9th Conference, AINL Andrey Filchenkov,Janne Kauttonen,Lidia Pivovarova Conference proceeding

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樓主: choleric
21#
發(fā)表于 2025-3-25 06:08:20 | 只看該作者
Proze?phasen von lernenden FuE-KooperationenThe task for participants was to train a general-purpose MT system which performs reasonably well on very diverse text domains and styles without additional fine-tuning. 11 teams participated in the competition, some of the submitted models showed reasonably good performance topping at ..
22#
發(fā)表于 2025-3-25 09:41:48 | 只看該作者
23#
發(fā)表于 2025-3-25 11:40:31 | 只看該作者
24#
發(fā)表于 2025-3-25 15:50:18 | 只看該作者
25#
發(fā)表于 2025-3-25 23:40:38 | 只看該作者
Advances of Transformer-Based Models for News Headline Generation,s, question answering, named entity recognition. Headline generation is a special kind of text summarization task. Models need to have strong natural language understanding that goes beyond the meaning of individual words and sentences and an ability to distinguish essential information to succeed i
26#
發(fā)表于 2025-3-26 00:46:06 | 只看該作者
An Explanation Method for Black-Box Machine Learning Survival Models Using the Chebyshev Distance,behind SurvLIME as well as SurvLIME-Inf is to apply the Cox proportional hazards model to approximate the black-box survival model at the local area around a test example. The Cox model is used due to the linear relationship of covariates. In contrast to SurvLIME, the proposed modification uses .-no
27#
發(fā)表于 2025-3-26 06:24:15 | 只看該作者
28#
發(fā)表于 2025-3-26 09:47:29 | 只看該作者
Predicting Eurovision Song Contest Results Using Sentiment Analysis,thods of sentiment analysis (English, multilingual polarity lexicons and deep learning) and translating the focus language tweets into English were used to determine the method that produced the best prediction for the contest. Furthermore, we analyzed the effect of sampling tweets during different
29#
發(fā)表于 2025-3-26 16:27:18 | 只看該作者
30#
發(fā)表于 2025-3-26 19:01:21 | 只看該作者
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