Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation

Natural language generation (NLG) is a critical component in spoken dialogue system, which can be divided into two phases: (1) sentence planning: deciding the overall sentence structure, (2) surface realization: determining specific word forms and flattening the sentence structure into a string. With the rise of deep learning, most modern NLG models are based on a sequence-to-sequence (seq2seq) model, which basically contains an encoder-decoder structure; these NLG models generate sentences from scratch by jointly optimizing sentence planning and surface realization. However, such simple encoder-decoder architecture usually fail to generate complex and long sentences, because the decoder has difficulty learning all grammar and diction knowledge well. This paper introduces an NLG model with a hierarchical attentional decoder, where the hierarchy focuses on leveraging linguistic knowledge in a specific order. The experiments show that the proposed method significantly outperforms the traditional seq2seq model with a smaller model size, and the design of the hierarchical attentional decoder can be applied to various NLG systems. Furthermore, different generation strategies based on linguistic patterns are investigated and analyzed in order to guide future NLG research work.Comment: accepted by the 7th IEEE Workshop on Spoken Language Technology (SLT 2018). arXiv admin note: text overlap with arXiv:1808.0274

The Development of Vietnam’s Sea-Denial Strategy

In the past two decades, Vietnam’s military investment has manifested a strategic shift of national interest from land to the maritime sphere, especially since 2000. This evolution reflects the country’s altered external environment and its economic transformation. During the Cold War, Hanoi focused on land warfare. Despite the existence of a small navy since the 1960s, land warfare represented the main security issue for Vietnamese decision makers, whether it concerned the Vietnam War against the United States and its allies, military intervention in Cambodia, or border defense against China

Interpreting The 750 GeV Diphoton Excess Within Topflavor Seesaw Model

We propose to interpret the 750 GeV diphoton excess in a typical topflavor seesaw model. The new resonance X can be identified as a CP-even scalar emerging from a certain bi-doublet Higgs field. Such a scalar can couple to charged scalars, fermions as well as heavy gauge bosons predicted by the model, and consequently all of the particles contribute to the diphoton decay mode of the X. Numerical analysis indicates that the model can predict the central value of the diphoton excess without contradicting any constraints from 8 TeV LHC, and among the constraints, the tightest one comes from the Z \gamma channel, \sigma_{8 {\rm TeV}}^{Z \gamma} \lesssim 3.6 {\rm fb}, which requires \sigma_{13 {\rm TeV}}^{\gamma \gamma} \lesssim 6 {\rm fb} in most of the favored parameter space.Comment: Major changes, 17 pages, 4 figure, typos corrected, calculation details adde