update literature

amu-literature.bib

@@ -381,6 +381,7 @@
   booktitle = {International Conference on Computational Linguistics},
   title     = {SLICE: Supersense-based Lightweight Interpretable Contextual Embeddings},
   year      = {2020},
+  file      = {:Aloui2020SLICESL - SLICE_ Supersense Based Lightweight Interpretable Contextual Embeddings.pdf:PDF:https\://aclanthology.org/2020.coling-main.298.pdf},
   groups    = {alexis' papers},
   url       = {https://api.semanticscholar.org/CorpusID:227230349},
 }
@@ -1349,6 +1350,72 @@
   publisher = {Springer},
 }
 
+@Misc{robinson2023chatgptmtcompetitivehigh,
+  author        = {Nathaniel R. Robinson and Perez Ogayo and David R. Mortensen and Graham Neubig},
+  title         = {ChatGPT MT: Competitive for High- (but not Low-) Resource Languages},
+  year          = {2023},
+  archiveprefix = {arXiv},
+  eprint        = {2309.07423},
+  file          = {:robinson2023chatgptmtcompetitivehigh - ChatGPT MT_ Competitive for High (but Not Low ) Resource Languages.pdf:PDF:https\://aclanthology.org/2023.wmt-1.40.pdf},
+  groups        = {ml-architechtures, read-asap},
+  primaryclass  = {cs.CL},
+  url           = {https://arxiv.org/abs/2309.07423},
+}
+
+@Misc{zhang2024hirelinguistlearningendangered,
+  author        = {Kexun Zhang and Yee Man Choi and Zhenqiao Song and Taiqi He and William Yang Wang and Lei Li},
+  title         = {Hire a Linguist!: Learning Endangered Languages with In-Context Linguistic Descriptions},
+  year          = {2024},
+  archiveprefix = {arXiv},
+  eprint        = {2402.18025},
+  file          = {:zhang2024hirelinguistlearningendangered - Hire a Linguist!_ Learning Endangered Languages with in Context Linguistic Descriptions.pdf:PDF:http\://arxiv.org/pdf/2402.18025v2},
+  groups        = {read-asap},
+  primaryclass  = {cs.CL},
+  url           = {https://arxiv.org/abs/2402.18025},
+}
+
+@InProceedings{rakhimova2024hybrid,
+  author       = {Rakhimova, Diana and Adali, E{\c{s}}ref and Karibayeva, Aidana},
+  booktitle    = {International Conference on Computational Collective Intelligence},
+  title        = {Hybrid Approach Text Generation for Low-Resource Language},
+  year         = {2024},
+  organization = {Springer},
+  pages        = {256--268},
+  abstract     = {Text generation is an important tool used by many companies in various fields such as chatbots, search engines, and question and answer systems, and is a hot trend in artificial intelligence. Generating texts and sentences can be used for both educational and entertainment purposes. Generating texts and sentences for children in natural language processing plays an important role in children's development. This helps them improve their reading, comprehension and communication skills in the language. Currently, many languages of the world belong to the class with the low resources. The field of text generation for low-resource languages is still at an early stage of development and there are many problems that need to be solved. One of the main problems is the lack of big data and linguistic resources in the public domain, which makes it difficult to effectively apply modern machine learning methods. As well as the lack of modern methods and tools for analyzing the processing of these languages. This article presents a hybrid approach to text generation on the example of the Turkish and Kazakh languages. These languages belong to a large group of Turkic languages along with Kyrgyz, Tatar, Uzbek and other languages. An approach based on neural learning using the LSTM model is proposed and implemented, considering the structural and semantic properties of the language. Training and testing are carried out on the assembled corpus (for various types of text genres). The quality of text generation was assessed based on the BLEU metric.},
+  groups       = {read-asap},
+}
+
+@Article{hussein2024lstm,
+  author  = {Hussein, Mustafa Abbas Hussein and Sava{\c{s}}, Serkan},
+  journal = {arXiv preprint arXiv:2403.07087},
+  title   = {Lstm-based text generation: A study on historical datasets},
+  year    = {2024},
+  file    = {:2403.07087v1.pdf:PDF},
+  groups  = {comparison-literary-texts},
+}
+
+@Article{shahriar2022gan,
+  author    = {Shahriar, Sakib},
+  journal   = {Displays},
+  title     = {GAN computers generate arts? A survey on visual arts, music, and literary text generation using generative adversarial network},
+  year      = {2022},
+  pages     = {102237},
+  volume    = {73},
+  file      = {:2108.03857v2.pdf:PDF},
+  groups    = {comparison-literary-texts},
+  publisher = {Elsevier},
+}
+
+@InProceedings{daza2016automatic,
+  author    = {Daza, Angel and Calvo, Hiram and Figueroa-Nazuno, Jes{\'u}s},
+  booktitle = {Proceedings of the Fifth Workshop on Computational Linguistics for Literature},
+  title     = {Automatic text generation by learning from literary structures},
+  year      = {2016},
+  pages     = {9--19},
+  file      = {:W16-0202.pdf:PDF},
+  groups    = {comparison-literary-texts},
+}
+
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