Multilingual language models exhibit better performance for some languages than for others (Singh et al., 2019), and many languages do not seem to benefit from multilingual sharing at all, presumably as a result of poor multilingual segmentation (Pyysal o et al., 2020). This work explores the idea of learning multilingual language models based on clustering of monolingual segments. We show significant improvements over standard multilingual segmentation and training across nine languages on a question answering task, both in a small model regime and for a model of the size of BERT-base.
Standard image caption generation systems produce generic descriptions of images and do not utilize any contextual information or world knowledge. In particular, they are unable to generate captions that contain references to the geographic context of an image, for example, the location where a photograph is taken or relevant geographic objects around an image location. In this paper, we develop a geo-aware image caption generation system, which incorporates geographic contextual information into a standard image captioning pipeline. We propose a way to build an image-specific representation of the geographic context and adapt the caption generation network to produce appropriate geographic names in the image descriptions. We evaluate our system on a novel captioning dataset that contains contextualized captions and geographic metadata and achieve substantial improvements in BLEU, ROUGE, METEOR and CIDEr scores. We also introduce a new metric to assess generated geographic references directly and empirically demonstrate our system’s ability to produce captions with relevant and factually accurate geographic referencing.
We propose a novel application of self-attention networks towards grammar induction. We present an attention-based supertagger for a refined type-logical grammar, trained on constructing types inductively. In addition to achieving a high overall type accuracy, our model is able to learn the syntax of the grammar’s type system along with its denotational semantics. This lifts the closed world assumption commonly made by lexicalized grammar supertaggers, greatly enhancing its generalization potential. This is evidenced both by its adequate accuracy over sparse word types and its ability to correctly construct complex types never seen during training, which, to the best of our knowledge, was as of yet unaccomplished.
We describe the induction of lexical resources from unannotated corpora that are aligned with treebank grammars, providing a systematic correspondence between features in the lexical resource and a treebank syntactic resource. We first describe a methodology based on parsing technology for augmenting a treebank database with linguistic features. A PCFG containing these features is created from the augmented treebank. We then use a procedure based on the inside-outside algorithm to learn lexical resources aligned with the treebank PCFG from large unannotated corpora. The method has been applied in creating a feature-annotated English treebank based on the Penn Treebank. The unsupervised estimation procedure gives a substantial error reduction (up to 31.6%) on the task of learning the subcategorization preference of novel verbs that are not present in the annotated training sample.