6 papers
Performance-Efficiency Trade-Offs in Adapting Language Models to Text Classification Tasks
Laura Aina, Nikos Voskarides, Roi Blanco
Pre-trained language models (LMs) obtain state-of-the-art performance when adapted to text classification tasks. However, when using such models in real-world applications, efficie…
Does referent predictability affect the choice of referential form? A computational approach using masked coreference resolution
Laura Aina, Xixian Liao, Gemma Boleda +1
It is often posited that more predictable parts of a speaker's meaning tend to be made less explicit, for instance using shorter, less informative words. Studying these dynamics in…
The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation
Laura Aina, Tal Linzen
Temporary syntactic ambiguities arise when the beginning of a sentence is compatible with multiple syntactic analyses. We inspect to which extent neural language models (LMs) exhib…
Putting words in context: LSTM language models and lexical ambiguity
Laura Aina, Kristina Gulordava, Gemma Boleda
In neural network models of language, words are commonly represented using context-invariant representations (word embeddings) which are then put in context in the hidden layers. S…
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue
Laura Aina, Carina Silberer, Matthijs Westera +2
Humans use language to refer to entities in the external world. Motivated by this, in recent years several models that incorporate a bias towards learning entity representations ha…
AMORE-UPF at SemEval-2018 Task 4: BiLSTM with Entity Library
Laura Aina, Carina Silberer, Ionut-Teodor Sorodoc +2
This paper describes our winning contribution to SemEval 2018 Task 4: Character Identification on Multiparty Dialogues. It is a simple, standard model with one key innovation, an e…