Bing Bai is a former Researcher with the Machine Learning group at NEC Laboratories America, Inc.


Generating Followup Questions for Interpretable Multi hop Question Answering

We propose a framework for answering open domain multi hop questions in which partial information is read and used to generate followup questions, to finally be answered by a pretrained single hop answer extractor. This framework makes each hop interpretable, and makes the retrieval associated with later hops as flexible and specific as for the first hop. As a first instantiation of this framework, we train a pointer generator network to predict followup questions based on the question and partial information. This provides a novel application of a neural question generation network, which is applied to give weak ground truth single hop followup questions based on the final answers and their supporting facts. Learning to generate followup questions that select the relevant answer spans against downstream supporting facts, while avoiding distracting premises, poses an exciting semantic challenge for text generation. We present an evaluation using the two hop bridge questions of HotpotQA

On Novel Object Recognition: A Unified Framework for Discriminability and Adaptability

The rich and accessible labeled data fueled the revolutionary successes of deep learning in object recognition. However, recognizing objects of novel classes with limited supervision information provided, i.e., Novel Object Recognition (NOR), remains a challenging task. We identify in this paper two key factors for the success of NOR that previous approaches fail to simultaneously guarantee. The first is producing discriminative feature representations for images of novel classes, and the second is generating a flexible classifier readily adapted to novel classes provided with limited supervision signals. To secure both key factors, we propose a framework which decouples a deep classification model into a feature extraction module and a classification module. We learn the former to ensure feature discriminability with a standard multi-class classification task by fully utilizing the competing information among all classes within a training set, and learn the latter to secure adaptability by training a meta-learner network which generates classifier weights whenever provided with minimal supervision information of target classes. Extensive experiments on common benchmark datasets in the settings of both zero-shot and few-shot learning demonstrate our method achieves state-of-the-art performance.

Conditional GAN with Discriminative Filter Generation for Text-to-Video Synthesis

Developing conditional generative models for text-to-video synthesis is an extremely challenging yet an important topic of research in machine learning. In this work, we address this problem by introducing Text-Filter conditioning Generative Adversarial Network (TFGAN), a conditional GAN model with a novel multi-scale text-conditioning scheme that improves text-video associations. By combining the proposed conditioning scheme with a deep GAN architecture, TFGAN generates high quality videos from text on challenging real-world video datasets. In addition, we construct a synthetic dataset of text-conditioned moving shapes to systematically evaluate our conditioning scheme. Extensive experiments demonstrate that TFGAN significantly outperforms existing approaches, and can also generate videos of novel categories not seen during training.

Leveraging Knowledge Bases for Future Prediction with Memory Comparison Networks

Making predictions about what might happen in the future is important for reacting adequately in many situations. For example, observing that “Man kidnaps girl” may have the consequence that “Man kills girl”. While this is part of common sense reasoning for humans, it is not obvious how machines can acquire and generalize over such knowledge. In this article, we propose a new type of memory network that can predict the next future event also for observations that are not in the knowledge base. We evaluate our proposed method on two knowledge bases: Reuters KB (events from news articles) and Regneri KB (events from scripts). For both knowledge bases, our proposed method shows similar or better prediction accuracy on unseen events (or scripts) than recently proposed deep neural networks and rankSVM. We also demonstrate that the attention mechanism of our proposed method can be helpful for error analysis and manual expansion of the knowledge base.