Deep Tree Transductions - A Short Survey

The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An empirical analysis is performed on real-world benchmarks, highlighting how there is no single model adequate to effectively approach all transduction problems.Comment: To appear in the Proceedings of the 2019 INNS Big Data and Deep Learning (INNSBDDL 2019). arXiv admin note: text overlap with arXiv:1809.0909

Intrinsic vs. extrinsic evaluation measures for referring expression generation

In this paper we present research in which we apply (i) the kind of intrinsic evaluation metrics that are characteristic of current comparative HLT evaluation, and (ii) extrinsic, human task-performance evaluations more in keeping with NLG traditions, to 15 systems implementing a language generation task. We analyse the evaluation results and find that there are no significant correlations between intrinsic and extrinsic evaluation measures for this task.peer-reviewe

Improving Grammaticality in Statistical Sentence Generation: Introducing a Dependency Spanning Tree Algorithm with an Argument Satisfaction Model

Abstract-like text summarisation requires a means of producing novel summary sentences. In order to improve the grammaticality of the generated sentence, we model a global (sentence) level syntactic structure. We couch statistical sentence generation as a spanning tree problem in order to search for the best dependency tree spanning a set of chosen words. We also introduce a new search algorithm for this task that models argument satisfaction to improve the linguistic validity of the generated tree. We treat the allocation of modifiers to heads as a weighted bipartite graph matching (or assignment) problem, a well studied problem in graph theory. Using BLEU to measure performance on a string regeneration task, we found an improvement, illustrating the benefit of the spanning tree approach armed with an argument satisfaction model.

Predicting the Fluency of Text with Shallow Structural Features: Case Studies of Machine Tanslation and Human-Written Text

Sentence fluency is an important component of overall text readability but few studies in natural language processing have sought to understand the factors that define it. We report the results of an initial study into the predictive power of surface syntactic statistics for the task; we use fluency assessments done for the purpose of evaluating machine translation. We find that these features are weakly but significantly correlated with fluency. Machine and human translations can be distinguished with accuracy over 80%. The performance of pairwise comparison of fluency is also very high—over 90% for a multi-layer perceptron classifier. We also test the hypothesis that the learned models capture general fluency properties applicable to human-written text. The results do not support this hypothesis: prediction accuracy on the new data is only 57%. This finding suggests that developing a dedicated, task-independent corpus of fluency judgments will be beneficial for further investigations of the problem

StarNet: Style-Aware 3D Point Cloud Generation

This paper investigates an open research task of reconstructing and generating 3D point clouds. Most existing works of 3D generative models directly take the Gaussian prior as input for the decoder to generate 3D point clouds, which fail to learn disentangled latent codes, leading noisy interpolated results. Most of the GAN-based models fail to discriminate the local geometries, resulting in the point clouds generated not evenly distributed at the object surface, hence degrading the point cloud generation quality. Moreover, prevailing methods adopt computation-intensive frameworks, such as flow-based models and Markov chains, which take plenty of time and resources in the training phase. To resolve these limitations, this paper proposes a unified style-aware network architecture combining both point-wise distance loss and adversarial loss, StarNet which is able to reconstruct and generate high-fidelity and even 3D point clouds using a mapping network that can effectively disentangle the Gaussian prior from input's high-level attributes in the mapped latent space to generate realistic interpolated objects. Experimental results demonstrate that our framework achieves comparable state-of-the-art performance on various metrics in the point cloud reconstruction and generation tasks, but is more lightweight in model size, requires much fewer parameters and less time for model training