Structural Embedding of Syntactic Trees for Machine Comprehension

Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods

CharManteau: Character Embedding Models For Portmanteau Creation

Portmanteaus are a word formation phenomenon where two words are combined to form a new word. We propose character-level neural sequence-to-sequence (S2S) methods for the task of portmanteau generation that are end-to-end-trainable, language independent, and do not explicitly use additional phonetic information. We propose a noisy-channel-style model, which allows for the incorporation of unsupervised word lists, improving performance over a standard source-to-target model. This model is made possible by an exhaustive candidate generation strategy specifically enabled by the features of the portmanteau task. Experiments find our approach superior to a state-of-the-art FST-based baseline with respect to ground truth accuracy and human evaluation.Comment: Accepted for publication in EMNLP 201

Using Implicit Feedback to Improve Question Generation

Question Generation (QG) is a task of Natural Language Processing (NLP) that aims at automatically generating questions from text. Many applications can benefit from automatically generated questions, but often it is necessary to curate those questions, either by selecting or editing them. This task is informative on its own, but it is typically done post-generation, and, thus, the effort is wasted. In addition, most existing systems cannot incorporate this feedback back into them easily. In this work, we present a system, GEN, that learns from such (implicit) feedback. Following a pattern-based approach, it takes as input a small set of sentence/question pairs and creates patterns which are then applied to new unseen sentences. Each generated question, after being corrected by the user, is used as a new seed in the next iteration, so more patterns are created each time. We also take advantage of the corrections made by the user to score the patterns and therefore rank the generated questions. Results show that GEN is able to improve by learning from both levels of implicit feedback when compared to the version with no learning, considering the top 5, 10, and 20 questions. Improvements go up from 10%, depending on the metric and strategy used.Comment: 27 pages, 8 figure

American Diagnostic Radiology Moves Offshore: Surfing the Internet Wave to Worldwide Access and Quality Perspectives: American Diagnostic Radiology Moves Offshore: Where Is the Internet Wave Taking This Field

International reading of medical imaging studies, or offshore teleradiology, has been a successful, though limited, practice benefiting patients and physicians for over a decade. Domestic and international market forces will continue to expand the demand for teleradiology as an important complement to United States based diagnostic radiology, though a full exodus of diagnostic reading to offshore sites is unlikely and inappropriate. Considerable obstacles remain to taking the teleradiology market to scale; however, barriers related to licensure, liability, quality assurance, and reimbursement will likely yield to market forces to be resolved in recognition of the significant benefits teleradiology offers to consumers and providers. As in other aspects of the economy, the world of medicine is becoming flat as the necessity of physical proximity is becoming less essential in the doctor-patient relationship. Telemedicine, which is the use of electronic information and communication technologies to diagnose and manage medical care from a distance, is realistic, successful, and even preferred in several instances. Telemedicine has existed for decades with telephone and fax, but with the Internet and the ability to view large amounts of audio and visual data at increasingly faster and cheaper rates, the practices of telemedicine is rapidly expanding