Zero-Shot Relation Extraction via Reading Comprehension

We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot. This reduction has several advantages: we can (1) learn relation-extraction models by extending recent neural reading-comprehension techniques, (2) build very large training sets for those models by combining relation-specific crowd-sourced questions with distant supervision, and even (3) do zero-shot learning by extracting new relation types that are only specified at test-time, for which we have no labeled training examples. Experiments on a Wikipedia slot-filling task demonstrate that the approach can generalize to new questions for known relation types with high accuracy, and that zero-shot generalization to unseen relation types is possible, at lower accuracy levels, setting the bar for future work on this task.Comment: CoNLL 201

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a feature-based classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study. Data and code available at -- http://nlp.cs.washington.edu/triviaqa/Comment: Added references, fixed typos, minor baseline updat

QuAC : Question Answering in Context

We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at http://quac.ai.Comment: EMNLP Camera Read

Load Deformation Test of Metal Brackets : A Comparative Study

The purpose of this study was to determine the effect material and design (slot torque degree and wing type) had on the force and stress to permanently deform metal brackets. Fourteen different types of metal brackets were tested and categorized into three categories. The three categories were: raw material composition, slot torque degree, and wing type. There were 5 types of raw materials (310SS, 316L, 303SE, 3038, and 17-4PH), 3 types of slot torque degree (0 degree, 7 degree, and 12 degree), and 4 types of wing design (mini twin, single, regular twin, and modified twin). All brackets were tested using arch wire torque test developed by Flores. An analysis of variance (ANOVA) and Student\u27s t-test showed that raw material, wing type, and slot torque degree had a significant effect on the force and stress to permanently deform metal brackets. Of the three variables, raw material had the greatest effect on the force to permanently deform metal brackets. Results showed that 17-4PH and 303S had higher yield strengths and regular twin had higher resistance to deformation. Also, as slot torque degree increased, brackets deformed with less force. A positive correlation between the micro hardness and the stress to deform metal brackets confirmed that brackets with the greatest stress to permanently deform were made of steels with the greatest hardness

B-Physics Phenomenology with Emphasis on the Light-Cone

Theoretical overviews on the B-physics are presented with an emphasis on the light-cone degrees of freedom. Our new treatment of the embedded states seems to give an encouraging result.Comment: 10 pages including 2 figures, elsart.sty; invited talk presented at the Tenth International Light-Cone Meeting on Non-Perturbative QCD and Hadron Phenomenology, Heidelberg, June 12-17, 200

Electronic modulation of infrared emissivity in graphene plasmonic resonators

Electronic control of blackbody emission from graphene plasmonic resonators on a silicon nitride substrate is demonstrated at temperatures up to 250 C. It is shown that the graphene resonators produce antenna-coupled blackbody radiation, manifest as narrow spectral emission peaks in the mid-IR. By continuously varying the nanoresonators carrier density, the frequency and intensity of these spectral features can be modulated via an electrostatic gate. We describe these phenomena as plasmonically enhanced radiative emission originating both from loss channels associated with plasmon decay in the graphene sheet and from vibrational modes in the SiNx.Comment: 17 pages, 6 figure