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

Probing gravitational non-minimal coupling with dark energy surveys

We investigate observational constraints on a specific one-parameter extension to the minimal quintessence model, where the quintessence field acquires a quadratic coupling to the scalar curvature through a coupling constant $\xi$. The value of $\xi$ is highly suppressed in typical tracker models if the late-time cosmic acceleration is driven at some field values near the Planck scale. We test $\xi$ in a second class of models in which the field value today becomes a free model parameter. We use the combined data from type-Ia supernovae, cosmic microwave background, baryon acoustic oscillations and matter power spectrum, to weak lensing measurements and find a best-fit value $\xi > 0.289$ where $\xi = 0$ is excluded outside the 95 per cent confidence region. The effective gravitational constant $G_{\rm eff}$ subject to the hint of a non-zero $\xi$ is constrained to $-0.003 < 1- G_{\rm eff}/G < 0.033$ at the same confidence level on cosmological scales, and can be narrowed down to $1- G_{\rm eff}/G < 2.2 \times 10^{-5}$ when combining with Solar System tests.Comment: Context extended, figures and references added, title changed to match with accepted version for publicatio

Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data

It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not difficult to collect a large set of audio data for each user, it is difficult to transcribe it. However, it is now possible to automatically discover acoustic tokens from unlabeled personal data in an unsupervised way. We therefore propose a multi-task deep learning framework called a phoneme-token deep neural network (PTDNN), jointly trained from unsupervised acoustic tokens discovered from unlabeled data and very limited transcribed data for personalized acoustic modeling. We term this scenario "weakly supervised". The underlying intuition is that the high degree of similarity between the HMM states of acoustic token models and phoneme models may help them learn from each other in this multi-task learning framework. Initial experiments performed over a personalized audio data set recorded from Facebook posts demonstrated that very good improvements can be achieved in both frame accuracy and word accuracy over popularly-considered baselines such as fDLR, speaker code and lightly supervised adaptation. This approach complements existing speaker adaptation approaches and can be used jointly with such techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201

"Teleparallel" Dark Energy

Using the "teleparallel" equivalent of General Relativity as the gravitational sector, which is based on torsion instead of curvature, we add a canonical scalar field, allowing for a nonminimal coupling with gravity. Although the minimal case is completely equivalent to standard quintessence, the nonminimal scenario has a richer structure, exhibiting quintessence-like or phantom-like behavior, or experiencing the phantom-divide crossing. The richer structure is manifested in the absence of a conformal transformation to an equivalent minimally-coupled model.Comment: 5 pages, 1 figure, Version published in PLB704 (2011) 384-38

Acute generalized exanthematous pustulosis: A retrospective study of 51 cases in Taiwan

AbstractBackground/ObjectiveAcute generalized exanthematous pustulosis (AGEP) is a severe cutaneous adverse drug reaction characterized by fever and numerous sterile non-follicular pustules. It is mainly attributed to drugs, although other factors have been implicated. The objective of this study was to evaluate the clinical and histological features of AGEP in a Taiwanese population.MethodsIn this retrospective study, we reviewed patients diagnosed with AGEP with a EuroSCAR (RegiSCAR) validation score more than 4 (>4, probable to definite cases), between 1992 and 2012 at the Chang Gung Memorial Hospital in Taiwan. Demographic, clinical and laboratory data, pathologic findings, and disease causality were analyzed.ResultsA total of 51 patients were included in this study, with 34 (66.7%) patients being diagnosed with AGEP with drug causality, and 17 (33.3%) patients being diagnosed with AGEP without drug causality. Cases of AGEP with drug causality showed an older average age, and a significantly higher rate of previous drug hypersensitivity history compared to cases of AGEP without drug causality (p = 0.0018). None of the patients had a history of psoriasis or had developed psoriasis at the 1-year follow-up. A total of 12 cases (23.5%) had systemic involvement, including liver and kidneys. Penicillin or aminopenicillin (17.6%) and cephalosporins (17.6%) were the most common causative drug groups related to AGEP. In AGEP patients without drug causality, three cases of pathogen infections were identified (1 case of mycoplasma, Coxsackie virus, and Epstein-Barr virus, respectively).ConclusionWe found that beta-lactam antibiotics were the major drug class responsible for inducing AGEP in a Taiwanese population, but that some infectious pathogens may also contribute to AGEP development