24 citations · 36 across the 11 of their papers we have counts for
8 papers · 1 filter
SparsePO: Controlling Preference Alignment of LLMs via Sparse Token Masks
Fenia Christopoulou, Ronald Cardenas, Gerasimos Lampouras +2
Direct alignment algorithms have proven an effective step for aligning language models to human-desired behaviors. Current variants of the Direct Preference Optimization objective…
Text-to-Code Generation with Modality-relative Pre-training
Fenia Christopoulou, Guchun Zhang, Gerasimos Lampouras
Large pre-trained language models have recently been expanded and applied to programming language tasks with great success, often through further pre-training of a strictly-natural…
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…
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…
Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors
Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Kno…
Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs
Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
Document-level relation extraction is a complex human process that requires logical inference to extract relationships between named entities in text. Existing approaches use graph…
Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network
Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa +1
Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies. Existing…
A Walk-based Model on Entity Graphs for Relation Extraction
Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
We present a novel graph-based neural network model for relation extraction. Our model treats multiple pairs in a sentence simultaneously and considers interactions among them. All…