131 citations · 372 across the 10 of their papers we have counts for
12 papers
MemWM: Memory-Augmented Text-Based World Model
Yujun Wang, Tao Zhang, Jinhe Bi +9
World models are increasingly used to support planning in agents by predicting how environment states evolve in response to agent actions. Yet fluent next-state predictions can sti…
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…
TextWorld: A Learning Environment for Text-based Games
Marc-Alexandre Côté, Ákos Kádár, Xingdi Yuan +10
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play…
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…
FigureQA: An Annotated Figure Dataset for Visual Reasoning
Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson +3
We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images. The images are synthetic, scientific-style figures from…
Imagination improves Multimodal Translation
Desmond Elliott, Ákos Kádár
We decompose multimodal translation into two sub-tasks: learning to translate and learning visually grounded representations. In a multitask learning framework, translations are le…
Representation of linguistic form and function in recurrent neural networks
Ákos Kádár, Grzegorz Chrupała, Afra Alishahi
We present novel methods for analyzing the activation patterns of RNNs from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we…
Learning language through pictures
Grzegorz Chrupała, Ákos Kádár, Afra Alishahi
We propose Imaginet, a model of learning visually grounded representations of language from coupled textual and visual input. The model consists of two Gated Recurrent Unit network…