Zobrazeno 1 - 10
of 194
pro vyhledávání: '"Bhat, Riyaz"'
Autor:
Mishra, Mayank, Kumar, Prince, Bhat, Riyaz, Murthy V, Rudra, Contractor, Danish, Tamilselvam, Srikanth
Prompting with natural language instructions has recently emerged as a popular method of harnessing the capabilities of large language models. Given the inherent ambiguity present in natural language, it is intuitive to consider the possible advantag
Externí odkaz:
http://arxiv.org/abs/2305.11790
Autor:
Sil, Avirup, Sen, Jaydeep, Iyer, Bhavani, Franz, Martin, Fadnis, Kshitij, Bornea, Mihaela, Rosenthal, Sara, McCarley, Scott, Zhang, Rong, Kumar, Vishwajeet, Li, Yulong, Sultan, Md Arafat, Bhat, Riyaz, Florian, Radu, Roukos, Salim
The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrie
Externí odkaz:
http://arxiv.org/abs/2301.09715
Autor:
Murthy V, Rudra, Bhat, Riyaz, Gunasekara, Chulaka, Patel, Siva Sankalp, Wan, Hui, Dhamecha, Tejas Indulal, Contractor, Danish, Danilevsky, Marina
In this paper we explore the task of modeling semi-structured object sequences; in particular, we focus our attention on the problem of developing a structure-aware input representation for such sequences. Examples of such data include user activity
Externí odkaz:
http://arxiv.org/abs/2301.01015
Autor:
Gupta, Vivek, Bhat, Riyaz A., Ghosal, Atreya, Shrivastava, Manish, Singh, Maneesh, Srikumar, Vivek
Neural models command state-of-the-art performance across NLP tasks, including ones involving "reasoning". Models claiming to reason about the evidence presented to them should attend to the correct parts of the input avoiding spurious patterns there
Externí odkaz:
http://arxiv.org/abs/2108.00578
Publikováno v:
Proceedings of the 15th International Conference on Parsing Technologies, pages 61-66, Pisa, Italy; September 20-22, 2017. Association for Computational Linguistics
We investigate the problem of parsing conversational data of morphologically-rich languages such as Hindi where argument scrambling occurs frequently. We evaluate a state-of-the-art non-linear transition-based parsing system on a new dataset containi
Externí odkaz:
http://arxiv.org/abs/1902.05085
Code-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the benchmark corpora used in NLP community that the application of standard te
Externí odkaz:
http://arxiv.org/abs/1804.05868
In this paper, we propose efficient and less resource-intensive strategies for parsing of code-mixed data. These strategies are not constrained by in-domain annotations, rather they leverage pre-existing monolingual annotated resources for training.
Externí odkaz:
http://arxiv.org/abs/1703.10772
Autor:
Ul Haq, Rather Izhar, Muhee, Amatul, Parray, Oveas Raffiq, Bhat, Junaid Ahmad, Kawoosa, Majid Shafi, Magray, Suhail Nabi, Qureshi, Sabia, Bhat, Riyaz Ahmed, Ahmad, Raja Aijaz, Farooq, Ubaid, Abdullah, Muzamil, Yatoo, Mohd. Iqbal
Publikováno v:
Journal of Pure & Applied Microbiology; 2024, Vol. 18 Issue 3, p1807-1823, 17p
The mid-rapidity transverse momentum spectra of hadrons (p, p-bar, K+, K-, K0-short, phi, {\Lambda}, {\Lambda}-bar, {\Xi},{\Xi}-bar, ({\Xi} + {\Xi}-bar), {\Omega}, and {\Omega}-bar) and the available rapidity distributions of the strange hadrons K0-s
Externí odkaz:
http://arxiv.org/abs/1510.05894
The transverse momentum distributions of hadrons produced in p-p collisions at LHC energies of Root(sNN) = 0.9 TeV, 2.76 TeV and Root(sNN) = 7.0 TeV have been studied using a unified statistical thermal freeze-out model. A good agreement is seen betw
Externí odkaz:
http://arxiv.org/abs/1502.04185