pwc-archive/papers-with-abstracts huggingface dataset:
Zero-shot Text-to-SQL Learning with Auxiliary Task
Authors: Shuaichen Chang, PengFei Liu, Yun Tang, Jing Huang, Xiaodong He, Bo-Wen Zhou
Date: 2019-08-29 00:00:00
Tasks: Text to SQL, Text-To-SQL

Recent years have seen great success in the use of neural seq2seq models on the text-to-SQL task. However, little work has paid attention to how these models generalize to realistic unseen data, which naturally raises a question: does this impressive performance signify a perfect generalization model, or are there still some limitations? In this paper, we first diagnose the bottleneck of text-to-SQL task by providing a new testbed, in which we observe that existing models present poor generalization ability on rarely-seen data. The above analysis encourages us to design a simple but effective auxiliary task, which serves as a supportive model as well as a regularization term to the generation task to increase the models generalization. Experimentally, We evaluate our models on a large text-to-SQL dataset WikiSQL. Compared to a strong baseline coarse-to-fine model, our models improve over the baseline by more than 3% absolute in accuracy on the whole dataset. More interestingly, on a zero-shot subset test of WikiSQL, our models achieve 5% absolute accuracy gain over the baseline, clearly demonstrating its superior generalizability.
Data-based wind disaster climate identification algorithm and extreme wind speed prediction
Authors: Wei Cui, Teng Ma, Lin Zhao, Yaojun Ge
Date: 2019-08-29 00:00:00

An extreme wind speed estimation method that considers wind hazard climate types is critical for design wind load calculation for building structures affected by mixed climates. However, it is very difficult to obtain wind hazard climate types from meteorological data records, because they restrict the application of extreme wind speed estimation in mixed climates. This paper first proposes a wind hazard type identification algorithm based on a numerical pattern recognition method that utilizes feature extraction and generalization. Next, it compares six commonly used machine learning models using K-fold cross-validation. Finally, it takes meteorological data from three locations near the southeast coast of China as examples to examine the algorithm performance. Based on classification results, the extreme wind speeds calculated based on mixed wind hazard types is compared with those obtained from conventional methods, and the effects on structural design for different return periods are discussed.
Multilingual and Multi-Aspect Hate Speech Analysis
Authors: Nedjma Ousidhoum, Zizheng Lin, Hongming Zhang, Yangqiu Song, Dit-yan Yeung
Date: 2019-08-29 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Classification, General Classification, Hate Speech Detection

Current research on hate speech analysis is typically oriented towards monolingual and single classification tasks. In this paper, we present a new multilingual multi-aspect hate speech analysis dataset and use it to test the current state-of-the-art multilingual multitask learning approaches. We evaluate our dataset in various classification settings, then we discuss how to leverage our annotations in order to improve hate speech detection and classification in general.
Shallow Syntax in Deep Water
Authors: Swabha Swayamdipta, Matthew Peters, Brendan Roof, Chris Dyer, Noah A. Smith
Date: 2019-08-29 00:00:00

Shallow syntax provides an approximation of phrase-syntactic structure of sentences; it can be produced with high accuracy, and is computationally cheap to obtain. We investigate the role of shallow syntax-aware representations for NLP tasks using two techniques. First, we enhance the ELMo architecture to allow pretraining on predicted shallow syntactic parses, instead of just raw text, so that contextual embeddings make use of shallow syntactic context. Our second method involves shallow syntactic features obtained automatically on downstream task data. Neither approach leads to a significant gain on any of the four downstream tasks we considered relative to ELMo-only baselines. Further analysis using black-box probes confirms that our shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo's embeddings. We take these findings as evidence that ELMo-style pretraining discovers representations which make additional awareness of shallow syntax redundant.
Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER
Authors: Peng-Hsuan Li, Tsu-Jui Fu, Wei-Yun Ma
Date: 2019-08-29 00:00:00
Tasks: Language Modeling, Named Entity Recognition (NER), NER

BiLSTM has been prevalently used as a core module for NER in a sequence-labeling setup. State-of-the-art approaches use BiLSTM with additional resources such as gazetteers, language-modeling, or multi-task supervision to further improve NER. This paper instead takes a step back and focuses on analyzing problems of BiLSTM itself and how exactly self-attention can bring improvements. We formally show the limitation of (CRF-)BiLSTM in modeling cross-context patterns for each word -- the XOR limitation. Then, we show that two types of simple cross-structures -- self-attention and Cross-BiLSTM -- can effectively remedy the problem. We test the practical impacts of the deficiency on real-world NER datasets, OntoNotes 5.0 and WNUT 2017, with clear and consistent improvements over the baseline, up to 8.7% on some of the multi-token entity mentions. We give in-depth analyses of the improvements across several aspects of NER, especially the identification of multi-token mentions. This study should lay a sound foundation for future improvements on sequence-labeling NER. (Source codes: https://github.com/jacobvsdanniel/cross-ner)
Discrete Laplace Operator Estimation for Dynamic 3D Reconstruction
Authors: Xiangyu Xu, Enrique Dunn
Date: 2019-08-29 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D geometry, 3D Reconstruction, Event Segmentation

We present a general paradigm for dynamic 3D reconstruction from multiple independent and uncontrolled image sources having arbitrary temporal sampling density and distribution. Our graph-theoretic formulation models the Spatio-temporal relationships among our observations in terms of the joint estimation of their 3D geometry and its discrete Laplace operator. Towards this end, we define a tri-convex optimization framework that leverages the geometric properties and dependencies found among a Euclideanshape-space and the discrete Laplace operator describing its local and global topology. We present a reconstructability analysis, experiments on motion capture data and multi-view image datasets, as well as explore applications to geometry-based event segmentation and data association.
3D Face Pose and Animation Tracking via Eigen-Decomposition based Bayesian Approach
Authors: Ngoc-Trung Tran, Fakhr-Eddine Ababsa, Maurice Charbit, Jacques Feldmar, Dijana Petrovska-Delacrétaz, Gérard Chollet
Date: 2019-08-29 00:00:00
Tasks: Face Model, Pose Estimation

This paper presents a new method to track both the face pose and the face animation with a monocular camera. The approach is based on the 3D face model CANDIDE and on the SIFT (Scale Invariant Feature Transform) descriptors, extracted around a few given landmarks (26 selected vertices of CANDIDE model) with a Bayesian approach. The training phase is performed on a synthetic database generated from the first video frame. At each current frame, the face pose and animation parameters are estimated via a Bayesian approach, with a Gaussian prior and a Gaussian likelihood function whose the mean and the covariance matrix eigenvalues are updated from the previous frame using eigen decomposition. Numerical results on pose estimation and landmark locations are reported using the Boston University Face Tracking (BUFT) database and Talking Face video. They show that our approach, compared to six other published algorithms, provides a very good compromise and presents a promising perspective due to the good results in terms of landmark localization.
DWnet: Deep-Wide Network for 3D Action Recognition
Authors: Yonghao Dang, Fuxing Yang, Jianqin Yin
Date: 2019-08-29 00:00:00
Tasks: 3D Action Recognition, Action Recognition

We propose in this paper a deep-wide network (DWnet) which combines the deep structure with the broad learning system (BLS) to recognize actions. Compared with the deep structure, the novel model saves lots of testing time and almost achieves real-time testing. Furthermore, the DWnet can capture better features than broad learning system can. In terms of methodology, we use pruned hierarchical co-occurrence network (PruHCN) to learn local and global spatial-temporal features. To obtain sufficient global information, BLS is used to expand features extracted by PruHCN. Experiments on two common skeletal datasets demonstrate the advantage of the proposed model on testing time and the effectiveness of the novel model to recognize the action.
An Auto-ML Framework Based on GBDT for Lifelong Learning
Authors: Jinlong Chai, Jiangeng Chang, Yakun Zhao, Honggang Liu
Date: 2019-08-29 00:00:00
Tasks: BIG-bench Machine Learning, Incremental Learning, Lifelong learning

Automatic Machine Learning (Auto-ML) has attracted more and more attention in recent years, our work is to solve the problem of data drift, which means that the distribution of data will gradually change with the acquisition process, resulting in a worse performance of the auto-ML model. We construct our model based on GBDT, Incremental learning and full learning are used to handle with drift problem. Experiments show that our method performs well on the five data sets. Which shows that our method can effectively solve the problem of data drift and has robust performance.
Towards Ethical Content-Based Detection of Online Influence Campaigns
Authors: Evan Crothers, Nathalie Japkowicz, Herna Viktor
Date: 2019-08-29 00:00:00
Tasks: Language Identification, Native Language Identification, Sentence

The detection of clandestine efforts to influence users in online communities is a challenging problem with significant active development. We demonstrate that features derived from the text of user comments are useful for identifying suspect activity, but lead to increased erroneous identifications when keywords over-represented in past influence campaigns are present. Drawing on research in native language identification (NLI), we use "named entity masking" (NEM) to create sentence features robust to this shortcoming, while maintaining comparable classification accuracy. We demonstrate that while NEM consistently reduces false positives when key named entities are mentioned, both masked and unmasked models exhibit increased false positive rates on English sentences by Russian native speakers, raising ethical considerations that should be addressed in future research.
Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsules
Authors: Xin Wen, Zhizhong Han, Xinhai Liu, Yu-Shen Liu
Date: 2019-08-29 00:00:00

Learning discriminative shape representation directly on point clouds is still challenging in 3D shape analysis and understanding. Recent studies usually involve three steps: first splitting a point cloud into some local regions, then extracting corresponding feature of each local region, and finally aggregating all individual local region features into a global feature as shape representation using simple max pooling. However, such pooling-based feature aggregation methods do not adequately take the spatial relationships between local regions into account, which greatly limits the ability to learn discriminative shape representation. To address this issue, we propose a novel deep learning network, named Point2SpatialCapsule, for aggregating features and spatial relationships of local regions on point clouds, which aims to learn more discriminative shape representation. Compared with traditional max-pooling based feature aggregation networks, Point2SpatialCapsule can explicitly learn not only geometric features of local regions but also spatial relationships among them. It consists of two modules. To resolve the disorder problem of local regions, the first module, named geometric feature aggregation, is designed to aggregate the local region features into the learnable cluster centers, which explicitly encodes the spatial locations from the original 3D space. The second module, named spatial relationship aggregation, is proposed for further aggregating clustered features and the spatial relationships among them in the feature space using the spatial-aware capsules developed in this paper. Compared to the previous capsule network based methods, the feature routing on the spatial-aware capsules can learn more discriminative spatial relationships among local regions for point clouds, which establishes a direct mapping between log priors and the spatial locations through feature clusters.
Metric-based Regularization and Temporal Ensemble for Multi-task Learning using Heterogeneous Unsupervised Tasks
Authors: Dae Ha Kim, Seung Hyun Lee, Byung Cheol Song
Date: 2019-08-29 00:00:00
Tasks: Clustering, Multi-Task Learning, object-detection, Object Detection

One of the ways to improve the performance of a target task is to learn the transfer of abundant knowledge of a pre-trained network. However, learning of the pre-trained network requires high computation capability and large-scale labeled dataset. To mitigate the burden of large-scale labeling, learning in un/self-supervised manner can be a solution. In addition, using unsupervised multi-task learning, a generalized feature representation can be learned. However, unsupervised multi-task learning can be biased to a specific task. To overcome this problem, we propose the metric-based regularization term and temporal task ensemble (TTE) for multi-task learning. Since these two techniques prevent the entire network from learning in a state deviated to a specific task, it is possible to learn a generalized feature representation that appropriately reflects the characteristics of each task without biasing. Experimental results for three target tasks such as classification, object detection and embedding clustering prove that the TTE-based multi-task framework is more effective than the state-of-the-art (SOTA) method in improving the performance of a target task.
Regularized Context Gates on Transformer for Machine Translation
Authors: Xintong Li, Lemao Liu, Rui Wang, Guoping Huang, Max Meng
Date: 2019-08-29 00:00:00
Proceeding: ACL 2020 6
Tasks: Machine Translation, NMT, Translation

Context gates are effective to control the contributions from the source and target contexts in the recurrent neural network (RNN) based neural machine translation (NMT). However, it is challenging to extend them into the advanced Transformer architecture, which is more complicated than RNN. This paper first provides a method to identify source and target contexts and then introduce a gate mechanism to control the source and target contributions in Transformer. In addition, to further reduce the bias problem in the gate mechanism, this paper proposes a regularization method to guide the learning of the gates with supervision automatically generated using pointwise mutual information. Extensive experiments on 4 translation datasets demonstrate that the proposed model obtains an averaged gain of 1.0 BLEU score over a strong Transformer baseline.
A Joint Model for Aspect-Category Sentiment Analysis with Shared Sentiment Prediction Layer
Authors: Yuncong Li, Zhe Yang, Cunxiang Yin, Xu Pan, Lunan Cui, Qiang Huang, Ting Wei
Date: 2019-08-29 00:00:00
Proceeding: CCL 2020 10
Tasks: Aspect Category Sentiment Analysis, Sentiment Analysis

Aspect-category sentiment analysis (ACSA) aims to predict the aspect categories mentioned in texts and their corresponding sentiment polarities. Some joint models have been proposed to address this task. Given a text, these joint models detect all the aspect categories mentioned in the text and predict the sentiment polarities toward them at once. Although these joint models obtain promising performances, they train separate parameters for each aspect category and therefore suffer from data deficiency of some aspect categories. To solve this problem, we propose a novel joint model which contains a shared sentiment prediction layer. The shared sentiment prediction layer transfers sentiment knowledge between aspect categories and alleviates the problem caused by data deficiency. Experiments conducted on SemEval-2016 Datasets demonstrate the effectiveness of our model.
Neural Snowball for Few-Shot Relation Learning
Authors: Tianyu Gao, Xu Han, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun
Date: 2019-08-29 00:00:00
Tasks: Knowledge Graphs, Relation, Relation Extraction

Knowledge graphs typically undergo open-ended growth of new relations. This cannot be well handled by relation extraction that focuses on pre-defined relations with sufficient training data. To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations. More specifically, we use Relational Siamese Networks (RSN) to learn the metric of relational similarities between instances based on existing relations and their labeled data. Afterwards, given a new relation and its few-shot instances, we use RSN to accumulate reliable instances from unlabeled corpora; these instances are used to train a relation classifier, which can further identify new facts of the new relation. The process is conducted iteratively like a snowball. Experiments show that our model can gather high-quality instances for better few-shot relation learning and achieves significant improvement compared to baselines. Codes and datasets are released on https://github.com/thunlp/Neural-Snowball.
Spectral Regularization for Combating Mode Collapse in GANs
Authors: Kanglin Liu, Wenming Tang, Fei Zhou, Guoping Qiu
Date: 2019-08-29 00:00:00
Proceeding: ICCV 2019 10

Despite excellent progress in recent years, mode collapse remains a major unsolved problem in generative adversarial networks (GANs).In this paper, we present spectral regularization for GANs (SR-GANs), a new and robust method for combating the mode collapse problem in GANs. Theoretical analysis shows that the optimal solution to the discriminator has a strong relationship to the spectral distributions of the weight matrix.Therefore, we monitor the spectral distribution in the discriminator of spectral normalized GANs (SN-GANs), and discover a phenomenon which we refer to as spectral collapse, where a large number of singular values of the weight matrices drop dramatically when mode collapse occurs. We show that there are strong evidence linking mode collapse to spectral collapse; and based on this link, we set out to tackle spectral collapse as a surrogate of mode collapse. We have developed a spectral regularization method where we compensate the spectral distributions of the weight matrices to prevent them from collapsing, which in turn successfully prevents mode collapse in GANs. We provide theoretical explanations for why SR-GANs are more stable and can provide better performances than SN-GANs. We also present extensive experimental results and analysis to show that SR-GANs not only always outperform SN-GANs but also always succeed in combating mode collapse where SN-GANs fail. The code is available at https://github.com/max-liu-112/SRGANs-Spectral-Regularization-GANs-.
Focus-Enhanced Scene Text Recognition with Deformable Convolutions
Authors: Linjie Deng, Yanxiang Gong, Xinchen Lu, Xin Yi, Zheng Ma, Mei Xie
Date: 2019-08-29 00:00:00
Tasks: Scene Text Recognition

Recently, scene text recognition methods based on deep learning have sprung up in computer vision area. The existing methods achieved great performances, but the recognition of irregular text is still challenging due to the various shapes and distorted patterns. Consider that at the time of reading words in the real world, normally we will not rectify it in our mind but adjust our focus and visual fields. Similarly, through utilizing deformable convolutional layers whose geometric structures are adjustable, we present an enhanced recognition network without the steps of rectification to deal with irregular text in this work. A number of experiments have been applied, where the results on public benchmarks demonstrate the effectiveness of our proposed components and shows that our method has reached satisfactory performances. The code will be publicly available at https://github.com/Alpaca07/dtr soon.
Real-time interactive magnetic resonance (MR) temperature imaging in both aqueous and adipose tissues using cascaded deep neural networks for MR-guided focused ultrasound surgery (MRgFUS)
Authors: Jong-Min Kim, You-Jin Jeong, Han-Jae Chung, Chulhyun Lee, Chang-Hyun Oh
Date: 2019-08-29 00:00:00
Tasks: Image Reconstruction

Purpose: To acquire the real-time interactive temperature map for aqueous and adipose tissue, the problems of long acquisition and processing time must be addressed. To overcome these major challenges, this paper proposes a cascaded convolutional neural network (CNN) framework and multi-echo gradient echo (meGRE) with a single reference variable flip angle (srVFA). Methods: To optimize the echo times for each method, MR images are acquired using a meGRE sequence; meGRE images with two flip angles (FAs) and meGRE images with a single FA are acquired during the pretreatment and treatment stages, respectively. These images are then processed and reconstructed by a cascaded CNN, which consists of two CNNs. The first CNN (called DeepACCnet) performs HR complex MR image reconstruction from the LR MR image acquired during the treatment stage, which is improved by the HR magnitude MR image acquired during the pretreatment stage. The second CNN (called DeepPROCnet) copes with T1 mapping. Results: Measurements of temperature and T1 changes obtained by meGRE combined with srVFA and cascaded CNNs were achieved in an agarose gel phantom, ex vivo porcine muscle, and ex vivo porcine muscle with fat layers (heating tests), and in vivo human prostate and brain (non-heating tests). In the heating test, the maximum differences between fiber-optic sensor and samples are less than 1 degree Celcius. In all cases, temperature mapping using the cascaded CNN achieved the best results in all cases. The acquisition and processing times for the proposed method are 0.8 s and 32 ms, respectively. Conclusions: Real-time interactive HR MR temperature mapping for simultaneously measuring aqueous and adipose tissue is feasible by combining a cascaded CNN with meGRE and srVFA.
Two-Pass End-to-End Speech Recognition
Authors: Tara N. Sainath, Ruoming Pang, David Rybach, Yanzhang He, Rohit Prabhavalkar, Wei Li, Mirkó Visontai, Qiao Liang, Trevor Strohman, Yonghui Wu, Ian McGraw, Chung-Cheng Chiu
Date: 2019-08-29 00:00:00
Tasks: speech-recognition, Speech Recognition, Vocal Bursts Valence Prediction

The requirements for many applications of state-of-the-art speech recognition systems include not only low word error rate (WER) but also low latency. Specifically, for many use-cases, the system must be able to decode utterances in a streaming fashion and faster than real-time. Recently, a streaming recurrent neural network transducer (RNN-T) end-to-end (E2E) model has shown to be a good candidate for on-device speech recognition, with improved WER and latency metrics compared to conventional on-device models [1]. However, this model still lags behind a large state-of-the-art conventional model in quality [2]. On the other hand, a non-streaming E2E Listen, Attend and Spell (LAS) model has shown comparable quality to large conventional models [3]. This work aims to bring the quality of an E2E streaming model closer to that of a conventional system by incorporating a LAS network as a second-pass component, while still abiding by latency constraints. Our proposed two-pass model achieves a 17%-22% relative reduction in WER compared to RNN-T alone and increases latency by a small fraction over RNN-T.