Successful machine learning systems currently rely on massive amounts of data, which are very effective in hiding some of the shallowness of the learned models. To help train models with more complex and compositional skills, we need challenging data, on which a system is successful only if it detects structure and regularities, that will allow it to generalize. In this paper, we describe a French dataset (BLM-AgrF) for learning the underlying rules of subject-verb agreement in sentences, developed in the BLM framework, a new task inspired by visual IQ tests known as Raven’s Progressive Matrices. In this task, an instance consists of sequences of sentences with specific attributes. To predict the correct answer as the next element of the sequence, a model must correctly detect the generative model used to produce the dataset. We provide details and share a dataset built following this methodology. Two exploratory baselines based on commonly used architectures show that despite the simplicity of the phenomenon, it is a complex problem for deep learning systems.

What do people know when they know the meaning of words? Word associations have been widely used to tap into lexical repre- sentations and their structure, as a way of probing semantic knowledge in humans. We investigate whether current word embedding spaces (contextualized and uncontextualized) can be considered good models of human lexi- cal knowledge by studying whether they have comparable characteristics to human associa- tion spaces. We study the three properties of association rank, asymmetry of similarity and triangle inequality. We find that word embeddings are good mod- els of some word associations properties. They replicate well human associations between words, and, like humans, their context-aware variants show violations of the triangle in- equality. While they do show asymmetry of similarities, their asymmetries do not map those of human association norms.