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pro vyhledávání: '"Santra, Bishal"'
Autor:
Prakash, Jatin, Buvanesh, Anirudh, Santra, Bishal, Saini, Deepak, Yadav, Sachin, Jiao, Jian, Prabhu, Yashoteja, Sharma, Amit, Varma, Manik
Extreme Classification (XC) aims to map a query to the most relevant documents from a very large document set. XC algorithms used in real-world applications learn this mapping from datasets curated from implicit feedback, such as user clicks. However
Externí odkaz:
http://arxiv.org/abs/2408.09585
The use of large language models (LLMs) in natural language processing (NLP) tasks is rapidly increasing, leading to changes in how researchers approach problems in the field. To fully utilize these models' abilities, a better understanding of their
Externí odkaz:
http://arxiv.org/abs/2305.14919
In the field of Natural Language Processing, there are many tasks that can be tackled effectively using the cross-entropy (CE) loss function. However, the task of dialog generation poses unique challenges for CE loss. This is because CE loss assumes
Externí odkaz:
http://arxiv.org/abs/2205.10558
We tackle the Dialogue Belief State Tracking(DST) problem of task-oriented conversational systems. Recent approaches to this problem leveraging Transformer-based models have yielded great results. However, training these models is expensive, both in
Externí odkaz:
http://arxiv.org/abs/2204.08167
Autor:
Santra, Bishal, Roychowdhury, Sumegh, Mandal, Aishik, Gurram, Vasu, Naik, Atharva, Gupta, Manish, Goyal, Pawan
Although many pretrained models exist for text or images, there have been relatively fewer attempts to train representations specifically for dialog understanding. Prior works usually relied on finetuned representations based on generic text represen
Externí odkaz:
http://arxiv.org/abs/2112.05787
Generative models for dialog systems have gained much interest because of the recent success of RNN and Transformer based models in tasks like question answering and summarization. Although the task of dialog response generation is generally seen as
Externí odkaz:
http://arxiv.org/abs/2011.08067
In this paper, we have explored the effects of different minibatch sampling techniques in Knowledge Graph Completion. Knowledge Graph Completion (KGC) or Link Prediction is the task of predicting missing facts in a knowledge graph. KGC models are usu
Externí odkaz:
http://arxiv.org/abs/2004.05553
Autor:
Sharma, Soumya, Santra, Bishal, Jana, Abhik, Santosh, T. Y. S. S., Ganguly, Niloy, Goyal, Pawan
Recently, biomedical version of embeddings obtained from language models such as BioELMo have shown state-of-the-art results for the textual inference task in the medical domain. In this paper, we explore how to incorporate structured domain knowledg
Externí odkaz:
http://arxiv.org/abs/1909.00160
Autor:
Krishna, Amrith, Santra, Bishal, Bandaru, Sasi Prasanth, Sahu, Gaurav, Sharma, Vishnu Dutt, Satuluri, Pavankumar, Goyal, Pawan
The configurational information in sentences of a free word order language such as Sanskrit is of limited use. Thus, the context of the entire sentence will be desirable even for basic processing tasks such as word segmentation. We propose a structur
Externí odkaz:
http://arxiv.org/abs/1809.01446
Autor:
Krishna, Amrith1 (AUTHOR) ak2329@cam.ac.uk, Santra, Bishal2 (AUTHOR) bsantraigi@gmail.com, Gupta, Ashim1,3 (AUTHOR) ashim@cs.utah.edu, Satuluri, Pavankumar4 (AUTHOR) pavankumarsatuluri@gmail.com, Goyal, Pawan2 (AUTHOR) pawang@cse.iitkgp.ac.in
Publikováno v:
Computational Linguistics. Dec2020, Vol. 46 Issue 4, p785-845. 61p.