no code implementations • INLG (ACL) 2021 • Anya Belz, Anastasia Shimorina, Shubham Agarwal, Ehud Reiter
The NLP field has recently seen a substantial increase in work related to reproducibility of results, and more generally in recognition of the importance of having shared definitions and practices relating to evaluation.
no code implementations • INLG (ACL) 2020 • Anya Belz, Shubham Agarwal, Anastasia Shimorina, Ehud Reiter
Across NLP, a growing body of work is looking at the issue of reproducibility.
1 code implementation • 1 Sep 2024 • Eden Ship, Eitan Spivak, Shubham Agarwal, Raz Birman, Ofer Hadar
Accurate classification of weather conditions in images is essential for enhancing the performance of object detection and classification models under varying weather conditions.
no code implementations • 30 Aug 2024 • Shubham Agarwal, Thomas Searle, Mart Ratas, Anthony Shek, James Teo, Richard Dobson
Electronic Health Records are large repositories of valuable clinical data, with a significant portion stored in unstructured text format.
no code implementations • 22 Jul 2024 • Swetha Eppalapally, Daksh Dangi, Chaithra Bhat, Ankita Gupta, Ruiyi Zhang, Shubham Agarwal, Karishma Bagga, Seunghyun Yoon, Nedim Lipka, Ryan A. Rossi, Franck Dernoncourt
Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs).
no code implementations • 27 Jun 2024 • Ishita Kumar, Snigdha Viswanathan, Sushrita Yerra, Alireza Salemi, Ryan A. Rossi, Franck Dernoncourt, Hanieh Deilamsalehy, Xiang Chen, Ruiyi Zhang, Shubham Agarwal, Nedim Lipka, Chien Van Nguyen, Thien Huu Nguyen, Hamed Zamani
In this work, we demonstrate the importance of user-specific personalization for long-text generation tasks and develop the Long-text Language Model Personalization (LongLaMP) Benchmark.
1 code implementation • 2 Feb 2024 • Shubham Agarwal, Issam H. Laradji, Laurent Charlin, Christopher Pal
Conducting literature reviews for scientific papers is essential for understanding research, its limitations, and building on existing work.
1 code implementation • 17 Dec 2023 • Juan A. Rodriguez, Shubham Agarwal, Issam H. Laradji, Pau Rodriguez, David Vazquez, Christopher Pal, Marco Pedersoli
These visual tokens are pre-pended to the SVG token embeddings, and the sequence is modeled by the StarCoder model using next-token prediction, effectively learning to align the visual and code tokens.
no code implementations • 7 Dec 2023 • Shubham Agarwal, Subrata Mitra, Sarthak Chakraborty, Srikrishna Karanam, Koyel Mukherjee, Shiv Saini
Text-to-image generation using diffusion models has seen explosive popularity owing to their ability in producing high quality images adhering to text prompts.
no code implementations • 29 Sep 2023 • Shubham Agarwal, Sarthak Chakraborty, Shaddy Garg, Sumit Bisht, Chahat Jain, Ashritha Gonuguntla, Shiv Saini
Our proposed method, Outage-Watch, defines critical service outages as deteriorations in the Quality of Service (QoS) captured by a set of metrics.
1 code implementation • 8 Nov 2022 • Alessandro Suglia, José Lopes, Emanuele Bastianelli, Andrea Vanzo, Shubham Agarwal, Malvina Nikandrou, Lu Yu, Ioannis Konstas, Verena Rieser
As the course of a game is unpredictable, so are commentaries, which makes them a unique resource to investigate dynamic language grounding.
no code implementations • 19 Aug 2022 • Abhishek Mukhopadhyay, Shubham Agarwal, Patrick Dylan Zwick, Pradipta Biswas
With the increasing prevalence of video recordings there is a growing need for tools that can maintain the privacy of those recorded.
Optical Character Recognition Optical Character Recognition (OCR)
1 code implementation • EACL 2021 • Anya Belz, Shubham Agarwal, Anastasia Shimorina, Ehud Reiter
Against the background of what has been termed a reproducibility crisis in science, the NLP field is becoming increasingly interested in, and conscientious about, the reproducibility of its results.
2 code implementations • ACL 2020 • Shubham Agarwal, Trung Bui, Joon-Young Lee, Ioannis Konstas, Verena Rieser
Visual Dialog involves "understanding" the dialog history (what has been discussed previously) and the current question (what is asked), in addition to grounding information in the image, to generate the correct response.
no code implementations • 15 Jan 2020 • Shubham Agarwal, Raghav Goyal
This manuscript describes our approach for the Visual Dialog Challenge 2018.
no code implementations • 17 Oct 2019 • Christian Muise, Tathagata Chakraborti, Shubham Agarwal, Ondrej Bajgar, Arunima Chaudhary, Luis A. Lastras-Montano, Josef Ondrej, Miroslav Vodolan, Charlie Wiecha
Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft.
no code implementations • 2 Feb 2019 • Adi Botea, Christian Muise, Shubham Agarwal, Oznur Alkan, Ondrej Bajgar, Elizabeth Daly, Akihiro Kishimoto, Luis Lastras, Radu Marinescu, Josef Ondrej, Pablo Pedemonte, Miroslav Vodolan
Dialogue systems have many applications such as customer support or question answering.
no code implementations • WS 2018 • Shubham Agarwal, Marc Dymetman, Eric Gaussier
This paper describes our submission to the E2E NLG Challenge.
1 code implementation • WS 2018 • Shubham Agarwal, Ondrej Dusek, Ioannis Konstas, Verena Rieser
In this work, we investigate the task of textual response generation in a multimodal task-oriented dialogue system.
1 code implementation • WS 2018 • Shubham Agarwal, Ondrej Dusek, Ioannis Konstas, Verena Rieser
Multimodal search-based dialogue is a challenging new task: It extends visually grounded question answering systems into multi-turn conversations with access to an external database.
1 code implementation • WS 2017 • Shubham Agarwal, Marc Dymetman
We train a char2char model on the E2E NLG Challenge data, by exploiting {``}out-of-the-box{''} the recently released tfseq2seq framework, using some of the standard options offered by this tool.