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pro vyhledávání: '"Tourni, Isidora"'
This paper explores the problems of Question Answering (QA) and Named Entity Recognition (NER) in five diverse languages. We tested five Large Language Models with various prompting methods, including zero-shot, chain-of-thought reasoning, and transl
Externí odkaz:
http://arxiv.org/abs/2410.21501
Autor:
Tourni, Isidora Chara, Guo, Lei, Hu, Hengchang, Halim, Edward, Ishwar, Prakash, Daryanto, Taufiq, Jalal, Mona, Chen, Boqi, Betke, Margrit, Zhafransyah, Fabian, Lai, Sha, Wijaya, Derry Tanti
News media structure their reporting of events or issues using certain perspectives. When describing an incident involving gun violence, for example, some journalists may focus on mental health or gun regulation, while others may emphasize the discus
Externí odkaz:
http://arxiv.org/abs/2406.17213
Autor:
Tourni, Isidora Chara, Wijaya, Derry
Unsupervised Neural Machine Translation (UNMT) focuses on improving NMT results under the assumption there is no human translated parallel data, yet little work has been done so far in highlighting its advantages compared to supervised methods and an
Externí odkaz:
http://arxiv.org/abs/2312.12588
Autor:
Tourni, Isidora Chara, Wijaya, Derry
With the advent of the Transformer architecture, Neural Machine Translation (NMT) results have shown great improvement lately. However, results in low-resource conditions still lag behind in both bilingual and multilingual setups, due to the limited
Externí odkaz:
http://arxiv.org/abs/2312.00214
Direct neural machine translation (direct NMT) is a type of NMT system that translates text between two non-English languages. Direct NMT systems often face limitations due to the scarcity of parallel data between non-English language pairs. Several
Externí odkaz:
http://arxiv.org/abs/2310.12236
Autor:
Tourni, Isidora, Grigorakis, Georgios, Marougkas, Isidoros, Dafnis, Konstantinos, Tassopoulou, Vassiliki
The advances of Generative AI models with interactive capabilities over the past few years offer unique opportunities for socioeconomic mobility. Their potential for scalability, accessibility, affordability, personalizing and convenience sets a firs
Externí odkaz:
http://arxiv.org/abs/2304.13728
Autor:
Kuwanto, Garry, Akyürek, Afra Feyza, Tourni, Isidora Chara, Li, Siyang, Jones, Alexander Gregory, Wijaya, Derry
We conduct an empirical study of neural machine translation (NMT) for truly low-resource languages, and propose a training curriculum fit for cases when both parallel training data and compute resource are lacking, reflecting the reality of most of t
Externí odkaz:
http://arxiv.org/abs/2103.13272