Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Santu, Shubhra Kanti Karmaker"'
Autor:
Santu, Shubhra Kanti Karmaker, Sinha, Sanjeev Kumar, Bansal, Naman, Knipper, Alex, Sarkar, Souvika, Salvador, John, Mahajan, Yash, Guttikonda, Sri, Akter, Mousumi, Freestone, Matthew, Williams Jr, Matthew C.
One of the most important yet onerous tasks in the academic peer-reviewing process is composing meta-reviews, which involves understanding the core contributions, strengths, and weaknesses of a scholarly manuscript based on peer-review narratives fro
Externí odkaz:
http://arxiv.org/abs/2402.15589
Learning meaningful word embeddings is key to training a robust language model. The recent rise of Large Language Models (LLMs) has provided us with many new word/sentence/document embedding models. Although LLMs have shown remarkable advancement in
Externí odkaz:
http://arxiv.org/abs/2402.11094
As spiking neural networks receive more attention, we look toward applications of this computing paradigm in fields other than computer vision and signal processing. One major field, underexplored in the neuromorphic setting, is Natural Language Proc
Externí odkaz:
http://arxiv.org/abs/2401.17911
Similar Narrative Retrieval is a crucial task since narratives are essential for explaining and understanding events, and multiple related narratives often help to create a holistic view of the event of interest. To accurately identify semantically s
Externí odkaz:
http://arxiv.org/abs/2309.04823
The rise of big data has amplified the need for efficient, user-friendly automated machine learning (AutoML) tools. However, the intricacy of understanding domain-specific data and defining prediction tasks necessitates human intervention making the
Externí odkaz:
http://arxiv.org/abs/2305.13657
While LLMs have shown great success in understanding and generating text in traditional conversational settings, their potential for performing ill-defined complex tasks is largely under-studied. Indeed, we are yet to conduct comprehensive benchmarki
Externí odkaz:
http://arxiv.org/abs/2305.11430
Autor:
Sarkar, Souvika, Babar, Mohammad Fakhruddin, Hassan, Md Mahadi, Hasan, Monowar, Santu, Shubhra Kanti Karmaker
Publikováno v:
ICPE 2024
This paper presents a performance study of transformer language models under different hardware configurations and accuracy requirements and derives empirical observations about these resource/accuracy trade-offs. In particular, we study how the most
Externí odkaz:
http://arxiv.org/abs/2304.11520
Sentence encoders have indeed been shown to achieve superior performances for many downstream text-mining tasks and, thus, claimed to be fairly general. Inspired by this, we performed a detailed study on how to leverage these sentence encoders for th
Externí odkaz:
http://arxiv.org/abs/2304.07382
This paper presents a high-quality dataset for evaluating the quality of Bangla word embeddings, which is a fundamental task in the field of Natural Language Processing (NLP). Despite being the 7th most-spoken language in the world, Bangla is a low-r
Externí odkaz:
http://arxiv.org/abs/2304.04613
In this paper, we present a novel perspective towards IR evaluation by proposing a new family of evaluation metrics where the existing popular metrics (e.g., nDCG, MAP) are customized by introducing a query-specific lower-bound (LB) normalization ter
Externí odkaz:
http://arxiv.org/abs/2209.05007