565 citations · 818 across the 51 of their papers we have counts for
5 papers · 2 filters
The Fast and the Flexible: training neural networks to learn to follow instructions from small data
Rezka Leonandya, Elia Bruni, Dieuwke Hupkes +1
Learning to follow human instructions is a long-pursued goal in artificial intelligence. The task becomes particularly challenging if no prior knowledge of the employed language is…
Do Language Models Understand Anything? On the Ability of LSTMs to Understand Negative Polarity Items
Jaap Jumelet, Dieuwke Hupkes
In this paper, we attempt to link the inner workings of a neural language model to linguistic theory, focusing on a complex phenomenon well discussed in formal linguis- tics: (nega…
Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue
Dieuwke Hupkes, Sanne Bouwmeester, Raquel Fernández
We investigate how encoder-decoder models trained on a synthetic dataset of task-oriented dialogues process disfluencies, such as hesitations and self-corrections. We find that, co…
Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information
Mario Giulianelli, Jacqueline Harding, Florian Mohnert +2
How do neural language models keep track of number agreement between subject and verb? We show that `diagnostic classifiers', trained to predict number from the internal states of…
Learning compositionally through attentive guidance
Dieuwke Hupkes, Anand Singh, Kris Korrel +2
While neural network models have been successfully applied to domains that require substantial generalisation skills, recent studies have implied that they struggle when solving th…