scholarly journals Interactive Text Ranking with Bayesian Optimization: A Case Study on Community QA and Summarization

2020 ◽  
Vol 8 ◽  
pp. 759-775
Author(s):  
Edwin Simpson ◽  
Yang Gao ◽  
Iryna Gurevych
Keyword(s):  
Question Answering ◽  
Training Data ◽  
Bayesian Optimization ◽  
Small Data ◽  
Large Space ◽  
Reward Function ◽  
Community Question Answering ◽  
Interactive Text ◽  
Text Ranking

For many NLP applications, such as question answering and summarization, the goal is to select the best solution from a large space of candidates to meet a particular user’s needs. To address the lack of user or task-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method uses Bayesian optimization to focus the user’s labeling effort on high quality candidates and integrate prior knowledge to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive multidocument summarization, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarization.

scholarly journals COALA: A Neural Coverage-Based Approach for Long Answer Selection with Small Data

2019 ◽  
Vol 33 ◽  
pp. 6932-6939 ◽  
Author(s):  
Andreas Rücklé ◽  
Nafise Sadat Moosavi ◽  
Iryna Gurevych
Keyword(s):  
Question Answering ◽  
Training Data ◽  
Small Data ◽  
Huge Number ◽  
Passage Retrieval ◽  
Large Margin ◽  
Community Question Answering ◽  
Syntactic Information ◽  
Selection Approach ◽  
Training Examples

Current neural network based community question answering (cQA) systems fall short of (1) properly handling long answers which are common in cQA; (2) performing under small data conditions, where a large amount of training data is unavailable—i.e., for some domains in English and even more so for a huge number of datasets in other languages; and (3) benefiting from syntactic information in the model—e.g., to differentiate between identical lexemes with different syntactic roles. In this paper, we propose COALA, an answer selection approach that (a) selects appropriate long answers due to an effective comparison of all question-answer aspects, (b) has the ability to generalize from a small number of training examples, and (c) makes use of the information about syntactic roles of words. We show that our approach outperforms existing answer selection models by a large margin on six cQA datasets from different domains. Furthermore, we report the best results on the passage retrieval benchmark WikiPassageQA.

ARQMath

2020 ◽  
Vol 54 (2) ◽  
pp. 1-9
Author(s):  
Richard Zanibbi ◽  
Behrooz Mansouri ◽  
Anurag Agarwal ◽  
Douglas W. Oard
Keyword(s):  
Undergraduate Students ◽  
Question Answering ◽  
Training Data ◽  
Barriers To Entry ◽  
Challenging Problem ◽  
Shared Task ◽  
Problem Domain ◽  
Community Question Answering ◽  
Relevance Judgments ◽  
First Time

The Answer Retrieval for Questions on Math (ARQMath) evaluation was run for the first time at CLEF 2020. ARQMath is the first Community Question Answering (CQA) shared task for math, retrieving existing answers from Math Stack Exchange (MSE) that can help to answer previously unseen math questions. ARQMath also introduces a new protocol for math formula search, where formulas are evaluated in context using a query formula's associated question post, and posts associated with each retrieved formula. Over 70 topics were annotated for each task by eight undergraduate students supervised by a professor of mathematics. A formula index is provided in three formats: LATEX, Presentation MathML, and Content MathML, avoiding the need for participants to extract these themselves. In addition to detailed relevance judgments, tools are provided to parse MSE data, generate question threads in HTML, and evaluate retrieval results. To make comparisons with participating systems fairer, nDCG' (i.e., nDCG for assessed hits only) is used to compare systems for each task. ARQMath will continue in CLEF 2021, with training data from 2020 and baseline systems for both tasks to reduce barriers to entry for this challenging problem domain.

scholarly journals Boris Johnson in hospital: a Chinese gaze at Western democracies in the COVID-19 pandemic

2020 ◽  
Vol 177 (1) ◽  
pp. 76-91 ◽  
Author(s):  
Altman Yuzhu Peng ◽  
Ivy Shixin Zhang ◽  
James Cummings ◽  
Xiaoxiao Zhang
Keyword(s):  
Question Answering ◽  
Domestic Politics ◽  
Original Data ◽  
British Politics ◽  
Community Question Answering ◽  
Internet Users ◽  
Research Findings ◽  
Democratic Systems ◽  
The Uk

In this article, we examine Chinese assessments of Western democratic systems in the context of the COVID-19 pandemic. This research is based on an up-to-date case study of how Chinese Internet users discussed the UK Prime Minister – Boris Johnson’s infection with COVID-19 in late March and early April 2020. The research collected original data from the Chinese community question-answering (CQA) site – Zhihu. Using a mixed-method approach, consisting of content analysis (CA) and thematic analysis (TA), we show how Zhihu users evaluate the incident (1) as a way to express their sentiments towards Boris Johnson, (2) as a case to assess British politics and (3) as a vehicle for rationalizing their views on Western democratic systems in relation to China’s domestic politics. The research findings shed new light on a Chinese gaze at Western democratic systems in the COVID-19 pandemic crisis.

scholarly journals SAR Target Recognition via Meta-Learning and Amortized Variational Inference

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5966
Author(s):  
Ke Wang ◽  
Gong Zhang
Keyword(s):  
Target Recognition ◽  
Probability Distributions ◽  
Automatic Target Recognition ◽  
Variational Inference ◽  
Training Data ◽  
Superior Performance ◽  
Small Data ◽  
Meta Learning ◽  
Radar Automatic Target Recognition ◽  
Global Parameters

The challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that incorporates meta-learning and amortized variational inference (AVI). Specifically, the model consists of global parameters and task-specific parameters. The global parameters, trained by meta-learning, construct a common feature extractor shared between all recognition tasks. The task-specific parameters, modeled by probability distributions, can adapt to new tasks with a small amount of training data. To reduce the computation and storage cost, the task-specific parameters are inferred by AVI implemented with set-to-set functions. Extensive experiments were conducted on a real SAR dataset to evaluate the effectiveness of the model. The results of the proposed approach compared with those of the latest SAR-ATR methods show the superior performance of our model, especially on recognition tasks with limited data.

scholarly journals 2D–3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryoya Shiode ◽  
Mototaka Kabashima ◽  
Yuta Hiasa ◽  
Kunihiro Oka ◽  
Tsuyoshi Murase ◽  
...  
Keyword(s):  
Wrist Joint ◽  
High Accuracy ◽  
Training Data ◽  
Small Data ◽  
Data Set ◽  
Accuracy Estimation ◽  
X Ray ◽  
Learning Network ◽  
Forearm Bone ◽  
Deep Learning Network

AbstractThe purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.

scholarly journals Investigating the Potential of Network Optimization for a Constrained Object Detection Problem

2021 ◽  
Vol 7 (4) ◽  
pp. 64
Author(s):  
Tanguy Ophoff ◽  
Cédric Gullentops ◽  
Kristof Van Beeck ◽  
Toon Goedemé
Keyword(s):  
Computational Complexity ◽  
Object Detection ◽  
Network Optimization ◽  
Real Life ◽  
Optimization Techniques ◽  
Training Data ◽  
Single Shot ◽  
Standard Object ◽  
Number Of Classes

Object detection models are usually trained and evaluated on highly complicated, challenging academic datasets, which results in deep networks requiring lots of computations. However, a lot of operational use-cases consist of more constrained situations: they have a limited number of classes to be detected, less intra-class variance, less lighting and background variance, constrained or even fixed camera viewpoints, etc. In these cases, we hypothesize that smaller networks could be used without deteriorating the accuracy. However, there are multiple reasons why this does not happen in practice. Firstly, overparameterized networks tend to learn better, and secondly, transfer learning is usually used to reduce the necessary amount of training data. In this paper, we investigate how much we can reduce the computational complexity of a standard object detection network in such constrained object detection problems. As a case study, we focus on a well-known single-shot object detector, YoloV2, and combine three different techniques to reduce the computational complexity of the model without reducing its accuracy on our target dataset. To investigate the influence of the problem complexity, we compare two datasets: a prototypical academic (Pascal VOC) and a real-life operational (LWIR person detection) dataset. The three optimization steps we exploited are: swapping all the convolutions for depth-wise separable convolutions, perform pruning and use weight quantization. The results of our case study indeed substantiate our hypothesis that the more constrained a problem is, the more the network can be optimized. On the constrained operational dataset, combining these optimization techniques allowed us to reduce the computational complexity with a factor of 349, as compared to only a factor 9.8 on the academic dataset. When running a benchmark on an Nvidia Jetson AGX Xavier, our fastest model runs more than 15 times faster than the original YoloV2 model, whilst increasing the accuracy by 5% Average Precision (AP).

Question Quality in Community Question Answering Forums

2015 ◽  
Vol 17 (1) ◽  
pp. 8-13 ◽  
Author(s):  
Antoaneta Baltadzhieva ◽  
Grzegorz Chrupała
Keyword(s):  
Question Answering ◽  
Community Question Answering

scholarly journals Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Wei Zhao ◽  
William Yamada ◽  
Tianxin Li ◽  
Matthew Digman ◽  
Troy Runge
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Precision Agriculture ◽  
Crop Production ◽  
Domain Adaptation ◽  
Training Data ◽  
Detection Accuracy ◽  
Detection Model ◽  
Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

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