9 citations · 9 across the 3 of their papers we have counts for
7 papers · 1 filter
Am I Blue or Is My Hobby Counting Teardrops? Expression Leakage in Large Language Models as a Symptom of Irrelevancy Disruption
Berkay Köprü, Mehrzad Mashal, Yigit Gurses +6
Large language models (LLMs) have advanced natural language processing (NLP) skills such as through next-token prediction and self-attention, but their ability to integrate broad c…
Improving Lemmatization of Non-Standard Languages with Joint Learning
Enrique Manjavacas, Ákos Kádár, Mike Kestemont
Lemmatization of standard languages is concerned with (i) abstracting over morphological differences and (ii) resolving token-lemma ambiguities of inflected words in order to map t…
Lessons learned in multilingual grounded language learning
Ákos Kádár, Desmond Elliott, Marc-Alexandre Côté +2
Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language. Here, we investigate in detail which conditions aff…
Revisiting the Hierarchical Multiscale LSTM
Ákos Kádár, Marc-Alexandre Côté, Grzegorz Chrupała +1
Hierarchical Multiscale LSTM (Chung et al., 2016a) is a state-of-the-art language model that learns interpretable structure from character-level input. Such models can provide fert…
NeuralREG: An end-to-end approach to referring expression generation
Thiago Castro Ferreira, Diego Moussallem, Ákos Kádár +2
Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on featu…
On the difficulty of a distributional semantics of spoken language
Grzegorz Chrupała, Lieke Gelderloos, Ákos Kádár +1
In the domain of unsupervised learning most work on speech has focused on discovering low-level constructs such as phoneme inventories or word-like units. In contrast, for written…