What Syntactic Structures block Dependencies in RNN Language Models?

Recurrent Neural Networks (RNNs) trained on a language modeling task have been shown to acquire a number of non-local grammatical dependencies with some success. Here, we provide new evidence that RNN language models are sensitive to hierarchical syntactic structure by investigating the filler--gap dependency and constraints on it, known as syntactic islands. Previous work is inconclusive about whether RNNs learn to attenuate their expectations for gaps in island constructions in particular or in any sufficiently complex syntactic environment. This paper gives new evidence for the former by providing control studies that have been lacking so far. We demonstrate that two state-of-the-art RNN models are are able to maintain the filler--gap dependency through unbounded sentential embeddings and are also sensitive to the hierarchical relationship between the filler and the gap. Next, we demonstrate that the models are able to maintain possessive pronoun gender expectations through island constructions---this control case rules out the possibility that island constructions block all information flow in these networks. We also evaluate three untested islands constraints: coordination islands, left branch islands, and sentential subject islands. Models are able to learn left branch islands and learn coordination islands gradiently, but fail to learn sentential subject islands. Through these controls and new tests, we provide evidence that model behavior is due to finer-grained expectations than gross syntactic complexity, but also that the models are conspicuously un-humanlike in some of their performance characteristics.Comment: To Appear at the 41st Annual Meeting of the Cognitive Science Society, Montreal, Canada, July 201

Manufacture of DPFC-DMS polymer in the SKG range

BPFC-DMS block copolymers were synthesized on a pre-pilot scale (i.e., to 5 Kg lots) and subsequently fabricated into clear, colorless films. Details of the synthesis procedures, property determinations, and film casting techniques are presented. Solubility, viscosity and molecular weight characteristics of the resulting product are reported

Seedling Emergence from Seed Banks in Ludwigia hexapetala-Invaded Wetlands: Implications for Restoration

Soil seed banks play a critical role in the maintenance of wetland plant communities and contribute to revegetation following disturbances. Analysis of the seed bank can therefore inform restoration planning and management. Emergence from seed banks may vary in response to hydrologic conditions and sediment disturbances. To assess the community-level impact of exotic Ludwigia hexapetala on soil seed banks, we compared differences in species composition of standing vegetation among invaded and non-invaded wetlands and the degree of similarity between vegetation and soil seed banks in northern California. To determine potential seed bank recruitment of L. hexapetala and associated plant species, we conducted a seedling emergence assay in response to inundation regime (drawdown vs. flooded) and sediment depth (surface vs. buried). Plant species richness, evenness, and Shannon’s H’ diversity were substantially lower in standing vegetation at L. hexapetala invaded sites as compared to non-invaded sites. Over 12 months, 69 plant taxa germinated from the seed banks, including L. hexapetala and several other exotic taxa. Seedling density varied among sites, being the highest (10,500 seedlings m−2) in surface sediments from non-invaded sites subjected to drawdown treatments. These results signal the need for invasive plant management strategies to deplete undesirable seed banks for restoration success