24 citations · 48 across the 21 of their papers we have counts for
23 papers
Code-Optimise: Self-Generated Preference Data for Correctness and Efficiency
Leonidas Gee, Milan Gritta, Gerasimos Lampouras +1
Code Language Models have been trained to generate accurate solutions, typically with no regard for runtime. On the other hand, previous works that explored execution optimisation…
HumanRankEval: Automatic Evaluation of LMs as Conversational Assistants
Milan Gritta, Gerasimos Lampouras, Ignacio Iacobacci
Language models (LMs) as conversational assistants recently became popular tools that help people accomplish a variety of tasks. These typically result from adapting LMs pretrained…
MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation
Petru-Daniel Tudosiu, Yongxin Yang, Shifeng Zhang +5
Text-to-image generation has achieved astonishing results, yet precise spatial controllability and prompt fidelity remain highly challenging. This limitation is typically addressed…
Findings of the First Workshop on Simulating Conversational Intelligence in Chat
Yvette Graham, Mohammed Rameez Qureshi, Haider Khalid +3
The aim of the workshop was to bring together experts working on open-domain dialogue research. In this speedily advancing research area many challenges still exist, such as learni…
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis
Philip John Gorinski, Matthieu Zimmer, Gerasimos Lampouras +2
The advent of large pre-trained language models in the domain of Code Synthesis has shown remarkable performance on various benchmarks, treating the problem of Code Generation in a…
A Systematic Study of Performance Disparities in Multilingual Task-Oriented Dialogue Systems
Songbo Hu, Han Zhou, Moy Yuan +5
Achieving robust language technologies that can perform well across the world's many languages is a central goal of multilingual NLP. In this work, we take stock of and empirically…
Correct and Optimal: the Regular Expression Inference Challenge
Mojtaba Valizadeh, Philip John Gorinski, Ignacio Iacobacci +1
We propose regular expression inference (REI) as a challenge for code/language modelling, and the wider machine learning community. REI is a supervised machine learning (ML) and pr…
Multi3WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems
Songbo Hu, Han Zhou, Mete Hergul +5
Creating high-quality annotated data for task-oriented dialog (ToD) is known to be notoriously difficult, and the challenges are amplified when the goal is to create equitable, cul…
Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access
Yue Feng, Gerasimos Lampouras, Ignacio Iacobacci
To alleviate the problem of structured databases' limited coverage, recent task-oriented dialogue systems incorporate external unstructured knowledge to guide the generation of sys…
Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU
Fenia Christopoulou, Gerasimos Lampouras, Ignacio Iacobacci
Curriculum Learning (CL) is a technique of training models via ranking examples in a typically increasing difficulty trend with the aim of accelerating convergence and improving ge…
Relational Graph Convolutional Neural Networks for Multihop Reasoning: A Comparative Study
Ieva Staliūnaitė, Philip John Gorinski, Ignacio Iacobacci
Multihop Question Answering is a complex Natural Language Processing task that requires multiple steps of reasoning to find the correct answer to a given question. Previous researc…
EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching
Chenxi Whitehouse, Fenia Christopoulou, Ignacio Iacobacci
Accurate alignment between languages is fundamental for improving cross-lingual pre-trained language models (XLMs). Motivated by the natural phenomenon of code-switching (CS) in mu…
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta +19
We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i.e. the synthesis of programming language solut…
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking
Han Zhou, Ignacio Iacobacci, Pasquale Minervini
Dialogue State Tracking (DST), a crucial component of task-oriented dialogue (ToD) systems, keeps track of all important information pertaining to dialogue history: filling slots w…
CrossAligner & Co: Zero-Shot Transfer Methods for Task-Oriented Cross-lingual Natural Language Understanding
Milan Gritta, Ruoyu Hu, Ignacio Iacobacci
Task-oriented personal assistants enable people to interact with a host of devices and services using natural language. One of the challenges of making neural dialogue systems avai…
Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuning
Benjamin Minixhofer, Milan Gritta, Ignacio Iacobacci
Transfer learning has become the dominant paradigm for many natural language processing tasks. In addition to models being pretrained on large datasets, they can be further trained…
XeroAlign: Zero-Shot Cross-lingual Transformer Alignment
Milan Gritta, Ignacio Iacobacci
The introduction of pretrained cross-lingual language models brought decisive improvements to multilingual NLP tasks. However, the lack of labelled task data necessitates a variety…
Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation
Ieva Staliūnaitė, Philip John Gorinski, Ignacio Iacobacci
Determining the plausibility of causal relations between clauses is a commonsense reasoning task that requires complex inference ability. The general approach to this task is to tr…
Conversation Graph: Data Augmentation, Training and Evaluation for Non-Deterministic Dialogue Management
Milan Gritta, Gerasimos Lampouras, Ignacio Iacobacci
Task-oriented dialogue systems typically rely on large amounts of high-quality training data or require complex handcrafted rules. However, existing datasets are often limited in s…
Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA
Ieva Staliūnaitė, Ignacio Iacobacci
Many NLP tasks have benefited from transferring knowledge from contextualized word embeddings, however the picture of what type of knowledge is transferred is incomplete. This pape…
Show Us the Way: Learning to Manage Dialog from Demonstrations
Gabriel Gordon-Hall, Philip John Gorinski, Gerasimos Lampouras +1
We present our submission to the End-to-End Multi-Domain Dialog Challenge Track of the Eighth Dialog System Technology Challenge. Our proposed dialog system adopts a pipeline archi…
Embedding Words and Senses Together via Joint Knowledge-Enhanced Training
Massimiliano Mancini, Jose Camacho-Collados, Ignacio Iacobacci +1
Word embeddings are widely used in Natural Language Processing, mainly due to their success in capturing semantic information from massive corpora. However, their creation process…
Semantic Representations of Word Senses and Concepts
José Camacho-Collados, Ignacio Iacobacci, Roberto Navigli +1
Representing the semantics of linguistic items in a machine-interpretable form has been a major goal of Natural Language Processing since its earliest days. Among the range of diff…