Zobrazeno 1 - 10
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pro vyhledávání: '"Martins, Pedro Henrique"'
Autor:
Fernandes, Patrick, Madaan, Aman, Liu, Emmy, Farinhas, António, Martins, Pedro Henrique, Bertsch, Amanda, de Souza, José G. C., Zhou, Shuyan, Wu, Tongshuang, Neubig, Graham, Martins, André F. T.
Many recent advances in natural language generation have been fueled by training large language models on internet-scale data. However, this paradigm can lead to models that generate toxic, inaccurate, and unhelpful content, and automatic evaluation
Externí odkaz:
http://arxiv.org/abs/2305.00955
Semi-parametric models, which augment generation with retrieval, have led to impressive results in language modeling and machine translation, due to their ability to retrieve fine-grained information from a datastore of examples. One of the most prom
Externí odkaz:
http://arxiv.org/abs/2205.12230
Machine translation models struggle when translating out-of-domain text, which makes domain adaptation a topic of critical importance. However, most domain adaptation methods focus on fine-tuning or training the entire or part of the model on every n
Externí odkaz:
http://arxiv.org/abs/2204.12608
Transformers are unable to model long-term memories effectively, since the amount of computation they need to perform grows with the context length. While variations of efficient transformers have been proposed, they all have a finite memory capacity
Externí odkaz:
http://arxiv.org/abs/2109.00301
Autor:
Gehrmann, Sebastian, Adewumi, Tosin, Aggarwal, Karmanya, Ammanamanchi, Pawan Sasanka, Anuoluwapo, Aremu, Bosselut, Antoine, Chandu, Khyathi Raghavi, Clinciu, Miruna, Das, Dipanjan, Dhole, Kaustubh D., Du, Wanyu, Durmus, Esin, Dušek, Ondřej, Emezue, Chris, Gangal, Varun, Garbacea, Cristina, Hashimoto, Tatsunori, Hou, Yufang, Jernite, Yacine, Jhamtani, Harsh, Ji, Yangfeng, Jolly, Shailza, Kale, Mihir, Kumar, Dhruv, Ladhak, Faisal, Madaan, Aman, Maddela, Mounica, Mahajan, Khyati, Mahamood, Saad, Majumder, Bodhisattwa Prasad, Martins, Pedro Henrique, McMillan-Major, Angelina, Mille, Simon, van Miltenburg, Emiel, Nadeem, Moin, Narayan, Shashi, Nikolaev, Vitaly, Niyongabo, Rubungo Andre, Osei, Salomey, Parikh, Ankur, Perez-Beltrachini, Laura, Rao, Niranjan Ramesh, Raunak, Vikas, Rodriguez, Juan Diego, Santhanam, Sashank, Sedoc, João, Sellam, Thibault, Shaikh, Samira, Shimorina, Anastasia, Cabezudo, Marco Antonio Sobrevilla, Strobelt, Hendrik, Subramani, Nishant, Xu, Wei, Yang, Diyi, Yerukola, Akhila, Zhou, Jiawei
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this m
Externí odkaz:
http://arxiv.org/abs/2102.01672
Current state-of-the-art text generators build on powerful language models such as GPT-2, achieving impressive performance. However, to avoid degenerate text, they require sampling from a modified softmax, via temperature parameters or ad-hoc truncat
Externí odkaz:
http://arxiv.org/abs/2004.02644
Visual attention mechanisms are widely used in multimodal tasks, as visual question answering (VQA). One drawback of softmax-based attention mechanisms is that they assign some probability mass to all image regions, regardless of their adjacency stru
Externí odkaz:
http://arxiv.org/abs/2002.05556
Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks, since in order to perform EL, first the mentions to entities have to be detected. However, most entity linking approaches disregard the mention detection part
Externí odkaz:
http://arxiv.org/abs/1907.08243
Using natural language to give instructions to robots is challenging, since natural language understanding is still largely an open problem. In this paper we address this problem by restricting our attention to commands modeled as one action, plus ar
Externí odkaz:
http://arxiv.org/abs/1807.03053
Autor:
Alves, Vinicius1 (AUTHOR) viniciusalves@biof.ufrj.br, Martins, Pedro Henrique1 (AUTHOR) phmartinsdesouza@gmail.com, Miranda, Bruna1 (AUTHOR) brunamiranda@biof.ufrj.br, de Andrade, Iara Bastos1 (AUTHOR) iara.bastos@biof.ufrj.br, Pereira, Luiza1 (AUTHOR) luiza.giglio.pereira@gmail.com, Maeda, Christina Takiya2 (AUTHOR) takiyacm@biof.ufrj.br, de Sousa Araújo, Glauber Ribeiro1 (AUTHOR) glauber@biof.ufrj.br, Frases, Susana1,3 (AUTHOR) susanafrases@biof.ufrj.br
Publikováno v:
Journal of Fungi. Aug2023, Vol. 9 Issue 8, p783. 18p.