Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Shekhar, Ravi"'
In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task. Semi-automated comment moderation systems greatly aid human moderators by either automatically classifying
Externí odkaz:
http://arxiv.org/abs/2211.06053
Autor:
Shekhar, Ravi
Fractured reservoirs, especially in low permeable carbonate rocks, are important target for hydrocarbon exploration and production because fractures can control fluid flow inside the reservoir. Hence, quantitative knowledge of fracture attributes is
Externí odkaz:
http://hdl.handle.net/1969.1/ETD-TAMU-2752
Moderation of reader comments is a significant problem for online news platforms. Here, we experiment with models for automatic moderation, using a dataset of comments from a popular Croatian newspaper. Our analysis shows that while comments that vio
Externí odkaz:
http://arxiv.org/abs/2109.10033
Autor:
Ranathunga, Surangika, Lee, En-Shiun Annie, Skenduli, Marjana Prifti, Shekhar, Ravi, Alam, Mehreen, Kaur, Rishemjit
Neural Machine Translation (NMT) has seen a tremendous spurt of growth in less than ten years, and has already entered a mature phase. While considered as the most widely used solution for Machine Translation, its performance on low-resource language
Externí odkaz:
http://arxiv.org/abs/2106.15115
Autor:
Shekhar, Ravi
Most human language understanding is grounded in perception. There is thus growing interest in combining information from language and vision. Multiple models based on Neural Networks have been proposed to merge language and vision information. All t
Externí odkaz:
https://hdl.handle.net/11572/368314
The multimodal models used in the emerging field at the intersection of computational linguistics and computer vision implement the bottom-up processing of the `Hub and Spoke' architecture proposed in cognitive science to represent how the brain proc
Externí odkaz:
http://arxiv.org/abs/1904.06038
Autor:
Shekhar, Ravi, Venkatesh, Aashish, Baumgärtner, Tim, Bruni, Elia, Plank, Barbara, Bernardi, Raffaella, Fernández, Raquel
We propose a grounded dialogue state encoder which addresses a foundational issue on how to integrate visual grounding with dialogue system components. As a test-bed, we focus on the GuessWhat?! game, a two-player game where the goal is to identify a
Externí odkaz:
http://arxiv.org/abs/1809.03408
Autor:
Shekhar, Ravi, Baumgartner, Tim, Venkatesh, Aashish, Bruni, Elia, Bernardi, Raffaella, Fernandez, Raquel
Our goal is to explore how the abilities brought in by a dialogue manager can be included in end-to-end visually grounded conversational agents. We make initial steps towards this general goal by augmenting a task-oriented visual dialogue model with
Externí odkaz:
http://arxiv.org/abs/1805.06960
Autor:
Shekhar, Ravi, Pezzelle, Sandro, Klimovich, Yauhen, Herbelot, Aurelie, Nabi, Moin, Sangineto, Enver, Bernardi, Raffaella
In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities. To this end, we propose an extension of the MSCOCO dataset, FOIL-COCO, which associates images with both cor
Externí odkaz:
http://arxiv.org/abs/1705.01359
Autor:
Udawatta, Pasindu, Udayangana, Indunil, Gamage, Chathulanka, Shekhar, Ravi, Ranathunga, Surangika
Publikováno v:
World Wide Web; Sep2024, Vol. 27 Issue 5, p1-31, 31p
