22 citations · 63 across the 22 of their papers we have counts for
32 papers
Multitask Multimodal Prompted Training for Interactive Embodied Task Completion
Georgios Pantazopoulos, Malvina Nikandrou, Amit Parekh +6
Interactive and embodied tasks pose at least two fundamental challenges to existing Vision & Language (VL) models, including 1) grounding language in trajectories of actions and ob…
No that's not what I meant: Handling Third Position Repair in Conversational Question Answering
Vevake Balaraman, Arash Eshghi, Ioannis Konstas +1
The ability to handle miscommunication is crucial to robust and faithful conversational AI. People usually deal with miscommunication immediately as they detect it, using highly sy…
Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark
Jason Hoelscher-Obermaier, Julia Persson, Esben Kran +2
Recent model editing techniques promise to mitigate the problem of memorizing false or outdated associations during LLM training. However, we show that these techniques can introdu…
Neuron to Graph: Interpreting Language Model Neurons at Scale
Alex Foote, Neel Nanda, Esben Kran +3
Advances in Large Language Models (LLMs) have led to remarkable capabilities, yet their inner mechanisms remain largely unknown. To understand these models, we need to unravel the…
The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering
Sabrina Chiesurin, Dimitris Dimakopoulos, Marco Antonio Sobrevilla Cabezudo +4
Large language models are known to produce output which sounds fluent and convincing, but is also often wrong, e.g. "unfaithful" with respect to a rationale as retrieved from a kno…
The Larger They Are, the Harder They Fail: Language Models do not Recognize Identifier Swaps in Python
Antonio Valerio Miceli-Barone, Fazl Barez, Ioannis Konstas +1
Large Language Models (LLMs) have successfully been applied to code generation tasks, raising the question of how well these models understand programming. Typical programming lang…
iLab at SemEval-2023 Task 11 Le-Wi-Di: Modelling Disagreement or Modelling Perspectives?
Nikolas Vitsakis, Amit Parekh, Tanvi Dinkar +3
There are two competing approaches for modelling annotator disagreement: distributional soft-labelling approaches (which aim to capture the level of disagreement) or modelling pers…
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…
Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models
Zdeněk Kasner, Ioannis Konstas, Ondřej Dušek
Pretrained language models (PLMs) for data-to-text (D2T) generation can use human-readable data labels such as column headings, keys, or relation names to generalize to out-of-doma…
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…
SPaR.txt, a cheap Shallow Parsing approach for Regulatory texts
Ruben Kruiper, Ioannis Konstas, Alasdair Gray +3
Automated Compliance Checking (ACC) systems aim to semantically parse building regulations to a set of rules. However, semantic parsing is known to be hard and requires large amoun…
MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization
Xinnuo Xu, Ondřej Dušek, Shashi Narayan +2
One of the most challenging aspects of current single-document news summarization is that the summary often contains 'extrinsic hallucinations', i.e., facts that are not present in…
AGGGEN: Ordering and Aggregating while Generating
Xinnuo Xu, Ondřej Dušek, Verena Rieser +1
We present AGGGEN (pronounced 'again'), a data-to-text model which re-introduces two explicit sentence planning stages into neural data-to-text systems: input ordering and input ag…
OTTers: One-turn Topic Transitions for Open-Domain Dialogue
Karin Sevegnani, David M. Howcroft, Ioannis Konstas +1
Mixed initiative in open-domain dialogue requires a system to pro-actively introduce new topics. The one-turn topic transition task explores how a system connects two topics in a c…
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games
Alessandro Suglia, Yonatan Bisk, Ioannis Konstas +4
Guessing games are a prototypical instance of the "learning by interacting" paradigm. This work investigates how well an artificial agent can benefit from playing guessing games wh…
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games
Alessandro Suglia, Antonio Vergari, Ioannis Konstas +4
In visual guessing games, a Guesser has to identify a target object in a scene by asking questions to an Oracle. An effective strategy for the players is to learn conceptual repres…
CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning
Alessandro Suglia, Ioannis Konstas, Andrea Vanzo +4
Approaches to Grounded Language Learning typically focus on a single task-based final performance measure that may not depend on desirable properties of the learned hidden represen…
A Scientific Information Extraction Dataset for Nature Inspired Engineering
Ruben Kruiper, Julian F. V. Vincent, Jessica Chen-Burger +2
Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. Howe…
In Layman's Terms: Semi-Open Relation Extraction from Scientific Texts
Ruben Kruiper, Julian F. V. Vincent, Jessica Chen-Burger +2
Information Extraction (IE) from scientific texts can be used to guide readers to the central information in scientific documents. But narrow IE systems extract only a fraction of…
History for Visual Dialog: Do we really need it?
Shubham Agarwal, Trung Bui, Joon-Young Lee +2
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…
Findings of the Third Workshop on Neural Generation and Translation
Hiroaki Hayashi, Yusuke Oda, Alexandra Birch +5
This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Lang…
Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)
Ondřej Dušek, Karin Sevegnani, Ioannis Konstas +1
We present a recurrent neural network based system for automatic quality estimation of natural language generation (NLG) outputs, which jointly learns to assign numerical ratings t…
Current Challenges in Spoken Dialogue Systems and Why They Are Critical for Those Living with Dementia
Angus Addlesee, Arash Eshghi, Ioannis Konstas
Dialogue technologies such as Amazon's Alexa have the potential to transform the healthcare industry. However, current systems are not yet naturally interactive: they are often tur…
SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression
Christos Baziotis, Ion Androutsopoulos, Ioannis Konstas +1
Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-se…