2 code implementations • EMNLP 2021 • Richard Plant, Dimitra Gkatzia, Valerio Giuffrida
Deep learning-based language models have achieved state-of-the-art results in a number of applications including sentiment analysis, topic labelling, intent classification and others.
no code implementations • 26 Oct 2016 • Dimitra Gkatzia
Data-to-text systems are powerful in generating reports from data automatically and thus they simplify the presentation of complex data.
no code implementations • ACL 2016 • Dimitra Gkatzia, Oliver Lemon, Verena Rieser
Decision-making is often dependent on uncertain data, e. g. data associated with confidence scores or probabilities.
no code implementations • 9 Jun 2015 • Dimitra Gkatzia, Helen Hastie
We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation.
no code implementations • WS 2017 • Cristina Barros, Dimitra Gkatzia, Elena Lloret
We present a flexible Natural Language Generation approach for Spanish, focused on the surface realisation stage, which integrates an inflection module in order to improve the naturalness and expressivity of the generated language.
no code implementations • WS 2017 • Cristina Barros, Dimitra Gkatzia, Elena Lloret
We present a novel supervised approach to inflection generation for verbs in Spanish.
no code implementations • WS 2018 • Jekaterina Belakova, Dimitra Gkatzia
One of the most natural ways for human robot communication is through spoken language.
no code implementations • LREC 2016 • Phil Bartie, William Mackaness, Dimitra Gkatzia, Verena Rieser
Our interest is in people{'}s capacity to efficiently and effectively describe geographic objects in urban scenes.
no code implementations • INLG (ACL) 2021 • Emiel van Miltenburg, Miruna-Adriana Clinciu, Ondřej Dušek, Dimitra Gkatzia, Stephanie Inglis, Leo Leppänen, Saad Mahamood, Emma Manning, Stephanie Schoch, Craig Thomson, Luou Wen
We observe a severe under-reporting of the different kinds of errors that Natural Language Generation systems make.
no code implementations • INLG (ACL) 2021 • Carl Strathearn, Dimitra Gkatzia
Conversational systems aim to generate responses that are accurate, relevant and engaging, either through utilising neural end-to-end models or through slot filling.
no code implementations • EACL (HumEval) 2021 • Miruna-Adriana Clinciu, Dimitra Gkatzia, Saad Mahamood
Common sense is an integral part of human cognition which allows us to make sound decisions, communicate effectively with others and interpret situations and utterances.
no code implementations • INLG (ACL) 2020 • Nikolaos Panagiaris, Emma Hart, Dimitra Gkatzia
In this paper we consider the problem of optimizing neural Referring Expression Generation (REG) models with sequence level objectives.
no code implementations • INLG (ACL) 2020 • David M. Howcroft, Anya Belz, Miruna-Adriana Clinciu, Dimitra Gkatzia, Sadid A. Hasan, Saad Mahamood, Simon Mille, Emiel van Miltenburg, Sashank Santhanam, Verena Rieser
Human assessment remains the most trusted form of evaluation in NLG, but highly diverse approaches and a proliferation of different quality criteria used by researchers make it difficult to compare results and draw conclusions across papers, with adverse implications for meta-evaluation and reproducibility.
no code implementations • 3 Apr 2022 • Carl Strathearn, Dimitra Gkatzia
This paper proposes a novel task on commonsense-enhanced task-based dialogue grounded in documents and describes the Task2Dial dataset, a novel dataset of document-grounded task-based dialogues, where an Information Giver (IG) provides instructions (by consulting a document) to an Information Follower (IF), so that the latter can successfully complete the task.
no code implementations • 20 Apr 2022 • Richard Plant, Valerio Giuffrida, Dimitra Gkatzia
Large scale adoption of large language models has introduced a new era of convenient knowledge transfer for a slew of natural language processing tasks.
no code implementations • dialdoc (ACL) 2022 • Carl Strathearn, Dimitra Gkatzia
This paper proposes a novel task on commonsense-enhanced task-based dialogue grounded in documents and describes the Task2Dial dataset, a novel dataset of document-grounded task-based dialogues, where an Information Giver (IG) provides instructions (by consulting a document) to an Information Follower (IF), so that the latter can successfully complete the task.
no code implementations • EAMT 2022 • Anabela Barreiro, José GC de Souza, Albert Gatt, Mehul Bhatt, Elena Lloret, Aykut Erdem, Dimitra Gkatzia, Helena Moniz, Irene Russo, Fabio Kepler, Iacer Calixto, Marcin Paprzycki, François Portet, Isabelle Augenstein, Mirela Alhasani
This paper presents the Multitask, Multilingual, Multimodal Language Generation COST Action – Multi3Generation (CA18231), an interdisciplinary network of research groups working on different aspects of language generation.
no code implementations • 22 Jun 2022 • Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
no code implementations • 2 May 2023 • Anya Belz, Craig Thomson, Ehud Reiter, Gavin Abercrombie, Jose M. Alonso-Moral, Mohammad Arvan, Anouck Braggaar, Mark Cieliebak, Elizabeth Clark, Kees Van Deemter, Tanvi Dinkar, Ondřej Dušek, Steffen Eger, Qixiang Fang, Mingqi Gao, Albert Gatt, Dimitra Gkatzia, Javier González-Corbelle, Dirk Hovy, Manuela Hürlimann, Takumi Ito, John D. Kelleher, Filip Klubicka, Emiel Krahmer, Huiyuan Lai, Chris van der Lee, Yiru Li, Saad Mahamood, Margot Mieskes, Emiel van Miltenburg, Pablo Mosteiro, Malvina Nissim, Natalie Parde, Ondřej Plátek, Verena Rieser, Jie Ruan, Joel Tetreault, Antonio Toral, Xiaojun Wan, Leo Wanner, Lewis Watson, Diyi Yang
We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/less reproducible.