Portrait of Shawn Tan

Shawn Tan

Alumni
Website
Google Scholar

Publications

Generating Contradictory, Neutral, and Entailing Sentences
Yikang Shen
Shawn Tan
Chin-wei Huang
Aaron Courville
Learning distributed sentence representations remains an interesting problem in the field of Natural Language Processing (NLP). We want to l… (see more)earn a model that approximates the conditional latent space over the representations of a logical antecedent of the given statement. In our paper, we propose an approach to generating sentences, conditioned on an input sentence and a logical inference label. We do this by modeling the different possibilities for the output sentence as a distribution over the latent representation, which we train using an adversarial objective. We evaluate the model using two state-of-the-art models for the Recognizing Textual Entailment (RTE) task, and measure the BLEU scores against the actual sentences as a probe for the diversity of sentences produced by our model. The experiment results show that, given our framework, we have clear ways to improve the quality and diversity of generated sentences.
2018-03-07
ArXiv (preprint)
arxiv.org
arxiv.org
Inferring Identity Factors for Grouped Examples
Shawn Tan
Chris Pal
Aaron Courville
We propose a method for modelling groups of face images from the same identity. The model is trained to infer a distribution over the latent… (see more) space for identity given a small set of “training data”. One can then sample images using that latent representation to produce images of the same identity. We demonstrate that the model extracts disentangled factors for identity factors and image-specific vectors. We also perform generative classification over identities to assess its feasibility for few-shot face recognition.
2018-02-12
(published)
openreview.net
openreview.net
Improving Explorability in Variational Inference with Annealed Variational Objectives
Chin-wei Huang
Shawn Tan
Alexandre Lacoste
Aaron Courville
Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process can s… (see more)till limit the density that is ultimately learned. We demonstrate the drawbacks of biasing the true posterior to be unimodal, and introduce Annealed Variational Objectives (AVO) into the training of hierarchical variational methods. Inspired by Annealed Importance Sampling, the proposed method facilitates learning by incorporating energy tempering into the optimization objective. In our experiments, we demonstrate our method's robustness to deterministic warm up, and the benefits of encouraging exploration in the latent space.
arxiv.org
Self-organized Hierarchical Softmax
Yikang Shen
Shawn Tan
Chris Pal
Aaron Courville
We propose a new self-organizing hierarchical softmax formulation for neural-network-based language models over large vocabularies. Instead … (see more)of using a predefined hierarchical structure, our approach is capable of learning word clusters with clear syntactical and semantic meaning during the language model training process. We provide experiments on standard benchmarks for language modeling and sentence compression tasks. We find that this approach is as fast as other efficient softmax approximations, while achieving comparable or even better performance relative to similar full softmax models.
2017-07-26
ArXiv (preprint)
arxiv.org
arxiv.org