Recursive Neural Networks Can Learn Logical Semantics

Tree-structured recursive neural networks (TreeRNNs) for sentence meaning have been successful for many applications, but it remains an open question whether the fixed-length representations that they learn can support tasks as demanding as logical deduction. We pursue this question by evaluating whether two such models---plain TreeRNNs and tree-structured neural tensor networks (TreeRNTNs)---can correctly learn to identify logical relationships such as entailment and contradiction using these representations. In our first set of experiments, we generate artificial data from a logical grammar and use it to evaluate the models' ability to learn to handle basic relational reasoning, recursive structures, and quantification. We then evaluate the models on the more natural SICK challenge data. Both models perform competitively on the SICK data and generalize well in all three experiments on simulated data, suggesting that they can learn suitable representations for logical inference in natural language

A large annotated corpus for learning natural language inference

Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research in this area has been dramatically limited by the lack of large-scale resources. To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning. At 570K pairs, it is two orders of magnitude larger than all other resources of its type. This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models, and it allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time.Comment: To appear at EMNLP 2015. The data will be posted shortly before the conference (the week of 14 Sep) at http://nlp.stanford.edu/projects/snli

Trace Elements in Soils of the South Texas Uranium District: Concentrations, Origin, and Environmental Significance

UT Librarie

Bipolar molecular outflows driven by hydromagnetic protostellar winds

We demonstrate that magnetically-collimated protostellar winds will sweep ambient material into thin, radiative, momentum-conserving shells whose features reproduce those commonly observed in bipolar molecular outflows. We find the typical position-velocity and mass-velocity relations to occur in outflows in a wide variety of ambient density distributions, regardless of the time histories of their driving winds.Comment: 4 pages, 1 figure, submitted to ApJ

Long range order in the classical kagome antiferromagnet: effective Hamiltonian approach

Following Huse and Rutenberg [Phys. Rev. B 45, 7536 (1992)], I argue the classical Heisenberg antiferromagnet on the kagom\'e lattice has long-range spin order of the $\sqrt{3}\times\sqrt{3}$ type (modulo gradual orientation fluctuations of the spins' plane). I start from the effective quartic Hamiltonian for the soft (out of plane) spin fluctuation modes, and treat as a perturbation those terms which depend on the discrete coplanar state. Soft mode correlations, which become the coefficients of a discrete effective Hamiltonian, are estimated analytically.Comment: 4pp, no figures. Converted to PRB format, extensive revisions/some reorderings to improve clarity; some cut

N-heterocyclic germylenes: structural characterisation of some heavy analogues of the ubiquitous N-heterocyclic carbenes

The X-ray crystal structures of three N-heterocyclic germylenes (NHGes) have been elucidated including the previously unknown 1,3-bis(2,6-dimethylphenyl)diazagermol-2-ylidene (1). In addition, the X-ray crystal structures of the previously synthesised 1,3-bis(2,4,6-trimethylphenyl)diazagermol-2-ylidene (2) and 1,3-bis(2,6-diisopropylphenyl)diazagermol-2-ylidene (3) are also reported. The discrete molecular structures of compounds 1 to 3 are comparable, with Ge-N bond lengths in the range 1.835-1.875 Å, while the N-Ge-N bond angles range between 83.6 and 85.2°. Compound 2 was compared to the analogous N-heterocyclic carbene species, 1,3-bis(2,4,6-trimethylphenyl)imidazol-2-ylidene (IMes). The major geometrical difference observed, as expected, was the bond angle around the divalent group 14 atom. The N-Ge-N bond angle was 83.6° for compound 2 versus the N-C-N bond angle of 101.4° for IMes. The Sn equivalent of (1), 1,3-bis(2,6-dimethylphenyl)diazastannol-2-ylidene (4), has also been synthesised and its crystal structure is reported here. In order to test their suitability as ligands, compounds 1 to 3 were reacted with a wide range of transition metal complexes. No NHGes containing metal complexes were observed. In all cases the NHGe either degraded or gave no reaction