Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for natural language processing tasks. Recently, some works geared towards pre-training specially-crafted models for particular domains, such as scientific papers, medical documents, user-generated texts, among others. These domain-specific models have been shown to improve performance significantly in most tasks; however, for languages other than English, such models are not widely available. In this work, we present RoBERTuito, a pre-trained language model for user-generated text in Spanish, trained on over 500 million tweets. Experiments on a benchmark of tasks involving user-generated text showed that RoBERTuito outperformed other pre-trained language models in Spanish. In addition to this, our model has some cross-lingual abilities, achieving top results for English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and also competitive performance against monolingual models in English Twitter tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.

Visual Dialog is assumed to require the dialog history to generate correct responses during a dialog.However, it is not clear from previous work how dialog history is needed for visual dialog.In this paper we define what it means for visual questions to require dialog history and we propose a methodology for identifying them.We release a subset of the Guesswhat?! questions for which their dialog history completely changes their responses.We propose a novel interpretable representation that visually grounds dialog history: the Region under Discussion.It constrains the image's spatial features according to a semantic representation of the history inspired by the information structure notion of Question under Discussion.We evaluate the architecture on task-specific multimodal models and the visual transformer model LXMERT and show that there is still room for improvement.

We derive a spectral method for unsupervised learning of Weighted Context Free Grammars.We frame WCFG induction as finding a Hankel matrix that has low rank and is linearly constrained to represent a function computed by inside-outside recursions.The proposed algorithm picks the grammar that agrees with a sample and is the simplest with respect to the nuclear norm of the Hankel matrix.