Supervised and Unsupervised Transfer Learning for Question Answering

Although transfer learning has been shown to be successful for tasks like object and speech recognition, its applicability to question answering (QA) has yet to be well-studied. In this paper, we conduct extensive experiments to investigate the transferability of knowledge learned from a source QA dataset to a target dataset using two QA models. The performance of both models on a TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson et al., 2013) is significantly improved via a simple transfer learning technique from MovieQA (Tapaswi et al., 2016). In particular, one of the models achieves the state-of-the-art on all target datasets; for the TOEFL listening comprehension test, it outperforms the previous best model by 7%. Finally, we show that transfer learning is helpful even in unsupervised scenarios when correct answers for target QA dataset examples are not available.Comment: To appear in NAACL HLT 2018 (long paper

An Unsupervised Autoregressive Model for Speech Representation Learning

This paper proposes a novel unsupervised autoregressive neural model for learning generic speech representations. In contrast to other speech representation learning methods that aim to remove noise or speaker variabilities, ours is designed to preserve information for a wide range of downstream tasks. In addition, the proposed model does not require any phonetic or word boundary labels, allowing the model to benefit from large quantities of unlabeled data. Speech representations learned by our model significantly improve performance on both phone classification and speaker verification over the surface features and other supervised and unsupervised approaches. Further analysis shows that different levels of speech information are captured by our model at different layers. In particular, the lower layers tend to be more discriminative for speakers, while the upper layers provide more phonetic content.Comment: Accepted to Interspeech 2019. Code available at: https://github.com/iamyuanchung/Autoregressive-Predictive-Codin

Respect: Refrainment From Impression Management Behavior Despite High Impression Motivation

We sometimes unintentionally distance ourselves from the people we respect. In a relationship between two individuals of perceived unequal status, what are the behaviors or factors of the person of lower status that distance him or her from the person he or she respects? Impression management is the process by which people control their impressions others form of them (Leary & Kowalski, 1990), and can be a useful theory in explaining this phenomenon; however, it does not explain the entire story. This is one possible instance in which we have high impression motivation but refrain from impression management behavior. This project aims to shed light on the nature and causes of refrainment from impression management behavior despite high impression motivation by exploring the factors that cause people to distance themselves from the people that they respect and perceive to have higher status and power. This research focuses on the distancing factors surrounding the person of the lower status. Data has been collected through one-on-one interviews with people from different backgrounds at different stages of career in diverse organizational contexts

Between Suitcases And Skywriting

How can the documentation of performance-based practice be destabilised to establish a more generative relationship with the very performance that it documents? Utilising the moving image as a cinematic extension for the practice of performance-based art, I will be investigating how historic and contemporary interventions with the camera have developed practical approaches to interrogate the relationship between performance art and its documentation