activity
20162024
most citedEvaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge

185 citations · 582 across the 36 of their papers we have counts for

collaborators
Showing 2022Show all

7 papers · 1 filter

cs.CV2022★ 2 cited

Going for GOAL: A Resource for Grounded Football Commentaries

Alessandro Suglia, José Lopes, Emanuele Bastianelli +6

Recent video+language datasets cover domains where the interaction is highly structured, such as instructional videos, or where the interaction is scripted, such as TV shows. Both…

cs.CL2022

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

BigScience Workshop, :, Teven Le Scao +391

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…

cs.CL2022★ 1 cited

Risk-graded Safety for Handling Medical Queries in Conversational AI

Gavin Abercrombie, Verena Rieser

Conversational AI systems can engage in unsafe behaviour when handling users' medical queries that can have severe consequences and could even lead to deaths. Systems therefore nee…

cs.LG2022★ 3 cited

Task Formulation Matters When Learning Continually: A Case Study in Visual Question Answering

Mavina Nikandrou, Lu Yu, Alessandro Suglia +2

Continual learning aims to train a model incrementally on a sequence of tasks without forgetting previous knowledge. Although continual learning has been widely studied in computer…

cs.CL2022

Why Robust Natural Language Understanding is a Challenge

Marco Casadio, Ekaterina Komendantskaya, Verena Rieser +4

With the proliferation of Deep Machine Learning into real-life applications, a particular property of this technology has been brought to attention: robustness Neural Networks noto…

cs.CV2022★ 1 cited

Adversarial Robustness of Visual Dialog

Lu Yu, Verena Rieser

Adversarial robustness evaluates the worst-case performance scenario of a machine learning model to ensure its safety and reliability. This study is the first to investigate the ro…

cs.CL2022★ 24 cited

Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges

Shikib Mehri, Jinho Choi, Luis Fernando D'Haro +13

This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog. The workshop explored the current state of the art along with its limitations and suggeste…