Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Rony, Md Rashad Al Hasan"'
Autor:
Rony, Md Rashad Al Hasan, Shaha, Sudipto Kumar, Hasan, Rakib Al, Dey, Sumon Kanti, Rafi, Amzad Hossain, Sirajee, Ashraf Hasan, Lehmann, Jens
Bengali is the seventh most spoken language on earth, yet considered a low-resource language in the field of natural language processing (NLP). Question answering over unstructured text is a challenging NLP task as it requires understanding both ques
Externí odkaz:
http://arxiv.org/abs/2410.10229
Autor:
Friedl, Ken E., Khan, Abbas Goher, Sahoo, Soumya Ranjan, Rony, Md Rashad Al Hasan, Germies, Jana, Süß, Christian
The assessment of advanced generative large language models (LLMs) poses a significant challenge, given their heightened complexity in recent developments. Furthermore, evaluating the performance of LLM-based applications in various industries, as in
Externí odkaz:
http://arxiv.org/abs/2311.07469
Autor:
Rony, Md Rashad Al Hasan, Suess, Christian, Bhat, Sinchana Ramakanth, Sudhi, Viju, Schneider, Julia, Vogel, Maximilian, Teucher, Roman, Friedl, Ken E., Sahoo, Soumya
Large language models (LLMs) have demonstrated remarkable performance by following natural language instructions without fine-tuning them on domain-specific tasks and data. However, leveraging LLMs for domain-specific question answering suffers from
Externí odkaz:
http://arxiv.org/abs/2310.09536
Autor:
Nayyeri, Mojtaba, Wang, Zihao, Akter, Mst. Mahfuja, Alam, Mirza Mohtashim, Rony, Md Rashad Al Hasan, Lehmann, Jens, Staab, Steffen
Knowledge Graphs, such as Wikidata, comprise structural and textual knowledge in order to represent knowledge. For each of the two modalities dedicated approaches for graph embedding and language models learn patterns that allow for predicting novel
Externí odkaz:
http://arxiv.org/abs/2208.02743
Task-oriented dialogue generation is challenging since the underlying knowledge is often dynamic and effectively incorporating knowledge into the learning process is hard. It is particularly challenging to generate both human-like and informative res
Externí odkaz:
http://arxiv.org/abs/2204.09149
Autor:
Rony, Md Rashad Al Hasan, Kovriguina, Liubov, Chaudhuri, Debanjan, Usbeck, Ricardo, Lehmann, Jens
Evaluating Natural Language Generation (NLG) systems is a challenging task. Firstly, the metric should ensure that the generated hypothesis reflects the reference's semantics. Secondly, it should consider the grammatical quality of the generated sent
Externí odkaz:
http://arxiv.org/abs/2203.09183
Knowledge Graph Embedding models have become an important area of machine learning.Those models provide a latent representation of entities and relations in a knowledge graph which can then be used in downstream machine learning tasks such as link pr
Externí odkaz:
http://arxiv.org/abs/2203.04703
In the last years, there have been significant developments in the area of Question Answering over Knowledge Graphs (KGQA). Despite all the notable advancements, current KGQA datasets only provide the answers as the direct output result of the formal
Externí odkaz:
http://arxiv.org/abs/2105.11407
Generating knowledge grounded responses in both goal and non-goal oriented dialogue systems is an important research challenge. Knowledge Graphs (KG) can be viewed as an abstraction of the real world, which can potentially facilitate a dialogue syste
Externí odkaz:
http://arxiv.org/abs/2103.16289
Non-goal oriented, generative dialogue systems lack the ability to generate answers with grounded facts. A knowledge graph can be considered an abstraction of the real world consisting of well-grounded facts. This paper addresses the problem of gener
Externí odkaz:
http://arxiv.org/abs/1910.07834