Titlebook: Natural Language Processing and Information Systems; 29th International C Amon Rapp,Luigi Di Caro,Vijayan Sugumaran Conference proceedings

鳥(niǎo)場(chǎng)

書(shū)目名稱Natural Language Processing and Information Systems影響因子(影響力)




書(shū)目名稱Natural Language Processing and Information Systems影響因子(影響力)學(xué)科排名




書(shū)目名稱Natural Language Processing and Information Systems網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Natural Language Processing and Information Systems網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Natural Language Processing and Information Systems被引頻次




書(shū)目名稱Natural Language Processing and Information Systems被引頻次學(xué)科排名




書(shū)目名稱Natural Language Processing and Information Systems年度引用




書(shū)目名稱Natural Language Processing and Information Systems年度引用學(xué)科排名




書(shū)目名稱Natural Language Processing and Information Systems讀者反饋




書(shū)目名稱Natural Language Processing and Information Systems讀者反饋學(xué)科排名

心神不寧

說(shuō)笑

減至最低

石墨

,The Influence of?Iconicity in?Transfer Learning for?Sign Language Recognition,Perceptron architecture and the temporal information with a Gated Recurrent Unit. Experimental results showed a 7.02% improvement for Arabic and 1.07% for Flemish when conducting iconic TL from Chinese and Greek respectively.

MOTTO

背帶

,Semi-supervised Named Entity Recognition for?Low-Resource Languages Using Dual PLMs,eriments using seven low-resource, but widely spoken African languages, demonstrating that augmented datasets generated by our approach achieve better results in six out of the seven languages. Furthermore, we conduct cross-lingual zero-shot experiments between language pairs and multi-lingual experiments to validate the robustness of our method.

蛤肉

,Grounding Toxicity in?Real-World Events Across Languages,fferent events and language communities, showing that toxicity is a complex phenomenon in which many different factors interact and still need to be investigated. We released the data and code for further research (.).

有機(jī)體

Adaptive Greedy Layer Pruning: Iterative Layer Pruning with Subsequent Model Repurposing,ious degradation of task performance. Indeed, we were able to omit up to 2.2x more layers from the investigated PLMs (depending on the backbone model) compared to a strong layer pruning baseline when preserving no less than 95% of the performance of the full backbone model.

合法