pwc-archive/papers-with-abstracts huggingface dataset:
Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations
Authors: Peng Qi, Yuhao Zhang, Christopher D. Manning
Date: 2020-04-30 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Informativeness, Question Generation, Question-Generation, Reinforcement Learning, Specificity

We investigate the problem of generating informative questions in information-asymmetric conversations. Unlike previous work on question generation which largely assumes knowledge of what the answer might be, we are interested in the scenario where the questioner is not given the context from which answers are drawn, but must reason pragmatically about how to acquire new information, given the shared conversation history. We identify two core challenges: (1) formally defining the informativeness of potential questions, and (2) exploring the prohibitively large space of potential questions to find the good candidates. To generate pragmatic questions, we use reinforcement learning to optimize an informativeness metric we propose, combined with a reward function designed to promote more specific questions. We demonstrate that the resulting pragmatic questioner substantially improves the informativeness and specificity of questions generated over a baseline model, as evaluated by our metrics as well as humans.
Bias-corrected estimator for intrinsic dimension and differential entropy--a visual multiscale approach
Authors: Jugurta Montalvão, Jânio Canuto, Luiz Miranda
Date: 2020-04-30 00:00:00

Intrinsic dimension and differential entropy estimators are studied in this paper, including their systematic bias. A pragmatic approach for joint estimation and bias correction of these two fundamental measures is proposed. Shared steps on both estimators are highlighted, along with their useful consequences to data analysis. It is shown that both estimators can be complementary parts of a single approach, and that the simultaneous estimation of differential entropy and intrinsic dimension give meaning to each other, where estimates at different observation scales convey different perspectives of underlying manifolds. Experiments with synthetic and real datasets are presented to illustrate how to extract meaning from visual inspections, and how to compensate for biases.
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Authors: Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen
Date: 2020-04-30 00:00:00
Proceeding: CVPR 2021 1
Tasks: CPU, GPU, Neural Architecture Search, Object, object-detection, Object Detection

Inverted bottleneck layers, which are built upon depthwise convolutions, have been the predominant building blocks in state-of-the-art object detection models on mobile devices. In this work, we investigate the optimality of this design pattern over a broad range of mobile accelerators by revisiting the usefulness of regular convolutions. We discover that regular convolutions are a potent component to boost the latency-accuracy trade-off for object detection on accelerators, provided that they are placed strategically in the network via neural architecture search. By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, we obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators. On the COCO object detection task, MobileDets outperform MobileNetV3+SSDLite by 1.7 mAP at comparable mobile CPU inference latencies. MobileDets also outperform MobileNetV2+SSDLite by 1.9 mAP on mobile CPUs, 3.7 mAP on Google EdgeTPU, 3.4 mAP on Qualcomm Hexagon DSP and 2.7 mAP on Nvidia Jetson GPU without increasing latency. Moreover, MobileDets are comparable with the state-of-the-art MnasFPN on mobile CPUs even without using the feature pyramid, and achieve better mAP scores on both EdgeTPUs and DSPs with up to 2x speedup. Code and models are available in the TensorFlow Object Detection API: https://github.com/tensorflow/models/tree/master/research/object_detection.
Simulated Multiple Reference Training Improves Low-Resource Machine Translation
Authors: Huda Khayrallah, Brian Thompson, Matt Post, Philipp Koehn
Date: 2020-04-30 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Machine Translation, Sentence, Translation, valid

Many valid translations exist for a given sentence, yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings. We introduce Simulated Multiple Reference Training (SMRT), a novel MT training method that approximates the full space of possible translations by sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser's distribution over possible tokens. We demonstrate the effectiveness of SMRT in low-resource settings when translating to English, with improvements of 1.2 to 7.0 BLEU. We also find SMRT is complementary to back-translation.
Exploiting Sentence Order in Document Alignment
Authors: Brian Thompson, Philipp Koehn
Date: 2020-04-30 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Sentence

We present a simple document alignment method that incorporates sentence order information in both candidate generation and candidate re-scoring. Our method results in 61% relative reduction in error compared to the best previously published result on the WMT16 document alignment shared task. Our method improves downstream MT performance on web-scraped Sinhala--English documents from ParaCrawl, outperforming the document alignment method used in the most recent ParaCrawl release. It also outperforms a comparable corpora method which uses the same multilingual embeddings, demonstrating that exploiting sentence order is beneficial even if the end goal is sentence-level bitext.
An Empirical Study of Pre-trained Transformers for Arabic Information Extraction
Authors: Wuwei Lan, Yang Chen, Wei Xu, Alan Ritter
Date: 2020-04-30 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Cross-Lingual Transfer, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Inference, Part-Of-Speech Tagging, Question Answering, Relation Extraction, Sentiment Analysis, Transfer Learning, Zero-Shot Learning

Multilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable the effective cross-lingual zero-shot transfer. However, their performance on Arabic information extraction (IE) tasks is not very well studied. In this paper, we pre-train a customized bilingual BERT, dubbed GigaBERT, that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning. We study GigaBERT's effectiveness on zero-short transfer across four IE tasks: named entity recognition, part-of-speech tagging, argument role labeling, and relation extraction. Our best model significantly outperforms mBERT, XLM-RoBERTa, and AraBERT (Antoun et al., 2020) in both the supervised and zero-shot transfer settings. We have made our pre-trained models publicly available at https://github.com/lanwuwei/GigaBERT.
Bilingual Text Extraction as Reading Comprehension
Authors: Katsuki Chousa, Masaaki Nagata, Masaaki Nishino
Date: 2020-04-29 00:00:00
Tasks: Articles, Reading Comprehension, Sentence, Translation

In this paper, we propose a method to extract bilingual texts automatically from noisy parallel corpora by framing the problem as a token-level span prediction, such as SQuAD-style Reading Comprehension. To extract a span of the target document that is a translation of a given source sentence (span), we use either QANet or multilingual BERT. QANet can be trained for a specific parallel corpus from scratch, while multilingual BERT can utilize pre-trained multilingual representations. For the span prediction method using QANet, we introduce a total optimization method using integer linear programming to achieve consistency in the predicted parallel spans. We conduct a parallel sentence extraction experiment using simulated noisy parallel corpora with two language pairs (En-Fr and En-Ja) and find that the proposed method using QANet achieves significantly better accuracy than a baseline method using two bi-directional RNN encoders, particularly for distant language pairs (En-Ja). We also conduct a sentence alignment experiment using En-Ja newspaper articles and find that the proposed method using multilingual BERT achieves significantly better accuracy than a baseline method using a bilingual dictionary and dynamic programming.
A Supervised Word Alignment Method based on Cross-Language Span Prediction using Multilingual BERT
Authors: Masaaki Nagata, Chousa Katsuki, Masaaki Nishino
Date: 2020-04-29 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Question Answering, Sentence, Word Alignment

We present a novel supervised word alignment method based on cross-language span prediction. We first formalize a word alignment problem as a collection of independent predictions from a token in the source sentence to a span in the target sentence. As this is equivalent to a SQuAD v2.0 style question answering task, we then solve this problem by using multilingual BERT, which is fine-tuned on a manually created gold word alignment data. We greatly improved the word alignment accuracy by adding the context of the token to the question. In the experiments using five word alignment datasets among Chinese, Japanese, German, Romanian, French, and English, we show that the proposed method significantly outperformed previous supervised and unsupervised word alignment methods without using any bitexts for pretraining. For example, we achieved an F1 score of 86.7 for the Chinese-English data, which is 13.3 points higher than the previous state-of-the-art supervised methods.
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
Authors: Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Ryuto Konno, Kentaro Inui
Date: 2020-04-29 00:00:00
Proceeding: ACL 2020 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Structured Prediction

Interpretable rationales for model predictions play a critical role in practical applications. In this study, we develop models possessing interpretable inference process for structured prediction. Specifically, we present a method of instance-based learning that learns similarities between spans. At inference time, each span is assigned a class label based on its similar spans in the training set, where it is easy to understand how much each training instance contributes to the predictions. Through empirical analysis on named entity recognition, we demonstrate that our method enables to build models that have high interpretability without sacrificing performance.
Asking without Telling: Exploring Latent Ontologies in Contextual Representations
Authors: Julian Michael, Jan A. Botha, Ian Tenney
Date: 2020-04-29 00:00:00
Proceeding: EMNLP 2020 11

The success of pretrained contextual encoders, such as ELMo and BERT, has brought a great deal of interest in what these models learn: do they, without explicit supervision, learn to encode meaningful notions of linguistic structure? If so, how is this structure encoded? To investigate this, we introduce latent subclass learning (LSL): a modification to existing classifier-based probing methods that induces a latent categorization (or ontology) of the probe's inputs. Without access to fine-grained gold labels, LSL extracts emergent structure from input representations in an interpretable and quantifiable form. In experiments, we find strong evidence of familiar categories, such as a notion of personhood in ELMo, as well as novel ontological distinctions, such as a preference for fine-grained semantic roles on core arguments. Our results provide unique new evidence of emergent structure in pretrained encoders, including departures from existing annotations which are inaccessible to earlier methods.
Counterfactual Off-Policy Training for Neural Response Generation
Authors: Qingfu Zhu, Wei-Nan Zhang, Ting Liu, William Yang Wang
Date: 2020-04-29 00:00:00
Tasks: counterfactual, Counterfactual Reasoning, Dialogue Generation, Response Generation

Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses. In this paper, we propose to explore potential responses by counterfactual reasoning. Given an observed response, the counterfactual reasoning model automatically infers the outcome of an alternative policy that could have been taken. The resulting counterfactual response synthesized in hindsight is of higher quality than the response synthesized from scratch. Training on the counterfactual responses under the adversarial learning framework helps to explore the high-reward area of the potential response space. An empirical study on the DailyDialog dataset shows that our approach significantly outperforms the HRED model as well as the conventional adversarial learning approaches.
Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation
Authors: Ji Ma, Ivan Korotkov, Yinfei Yang, Keith Hall, Ryan Mcdonald
Date: 2020-04-29 00:00:00
Proceeding: EACL 2021 2
Tasks: Passage Retrieval, Question Generation, Question-Generation, Retrieval, Zero-Shot Learning

A major obstacle to the wide-spread adoption of neural retrieval models is that they require large supervised training sets to surpass traditional term-based techniques, which are constructed from raw corpora. In this paper, we propose an approach to zero-shot learning for passage retrieval that uses synthetic question generation to close this gap. The question generation system is trained on general domain data, but is applied to documents in the targeted domain. This allows us to create arbitrarily large, yet noisy, question-passage relevance pairs that are domain specific. Furthermore, when this is coupled with a simple hybrid term-neural model, first-stage retrieval performance can be improved further. Empirically, we show that this is an effective strategy for building neural passage retrieval models in the absence of large training corpora. Depending on the domain, this technique can even approach the accuracy of supervised models.
Reduced-Dimensional Reinforcement Learning Control using Singular Perturbation Approximations
Authors: Sayak Mukherjee, He Bai, Aranya Chakrabortty
Date: 2020-04-29 00:00:00
Tasks: Clustering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a set of model-free, reduced-dimensional reinforcement learning (RL) based optimal control designs for linear time-invariant singularly perturbed (SP) systems. We first present a state-feedback and output-feedback based RL control design for a generic SP system with unknown state and input matrices. We take advantage of the underlying time-scale separation property of the plant to learn a linear quadratic regulator (LQR) for only its slow dynamics, thereby saving a significant amount of learning time compared to the conventional full-dimensional RL controller. We analyze the sub-optimality of the design using SP approximation theorems and provide sufficient conditions for closed-loop stability. Thereafter, we extend both designs to clustered multi-agent consensus networks, where the SP property reflects through clustering. We develop both centralized and cluster-wise block-decentralized RL controllers for such networks, in reduced dimensions. We demonstrate the details of the implementation of these controllers using simulations of relevant numerical examples and compare them with conventional RL designs to show the computational benefits of our approach.
Posterior Calibrated Training on Sentence Classification Tasks
Authors: Taehee Jung, Dongyeop Kang, Hua Cheng, Lucas Mentch, Thomas Schaaf
Date: 2020-04-29 00:00:00
Proceeding: ACL 2020 6
Tasks: Classification, General Classification, Sentence, Sentence Classification

Most classification models work by first predicting a posterior probability distribution over all classes and then selecting that class with the largest estimated probability. In many settings however, the quality of posterior probability itself (e.g., 65% chance having diabetes), gives more reliable information than the final predicted class alone. When these methods are shown to be poorly calibrated, most fixes to date have relied on posterior calibration, which rescales the predicted probabilities but often has little impact on final classifications. Here we propose an end-to-end training procedure called posterior calibrated (PosCal) training that directly optimizes the objective while minimizing the difference between the predicted and empirical posterior probabilities.We show that PosCal not only helps reduce the calibration error but also improve task performance by penalizing drops in performance of both objectives. Our PosCal achieves about 2.5% of task performance gain and 16.1% of calibration error reduction on GLUE (Wang et al., 2018) compared to the baseline. We achieved the comparable task performance with 13.2% calibration error reduction on xSLUE (Kang and Hovy, 2019), but not outperforming the two-stage calibration baseline. PosCal training can be easily extendable to any types of classification tasks as a form of regularization term. Also, PosCal has the advantage that it incrementally tracks needed statistics for the calibration objective during the training process, making efficient use of large training sets.
Rethinking Class-Discrimination Based CNN Channel Pruning
Authors: Yuchen Liu, David Wentzlaff, S. Y. Kung
Date: 2020-04-29 00:00:00

Channel pruning has received ever-increasing focus on network compression. In particular, class-discrimination based channel pruning has made major headway, as it fits seamlessly with the classification objective of CNNs and provides good explainability. Prior works singly propose and evaluate their discriminant functions, while further study on the effectiveness of the adopted metrics is absent. To this end, we initiate the first study on the effectiveness of a broad range of discriminant functions on channel pruning. Conventional single-variate binary-class statistics like Student's T-Test are also included in our study via an intuitive generalization. The winning metric of our study has a greater ability to select informative channels over other state-of-the-art methods, which is substantiated by our qualitative and quantitative analysis. Moreover, we develop a FLOP-normalized sensitivity analysis scheme to automate the structural pruning procedure. On CIFAR-10, CIFAR-100, and ILSVRC-2012 datasets, our pruned models achieve higher accuracy with less inference cost compared to state-of-the-art results. For example, on ILSVRC-2012, our 44.3% FLOPs-pruned ResNet-50 has only a 0.3% top-1 accuracy drop, which significantly outperforms the state of the art.
Detecting Deep-Fake Videos from Appearance and Behavior
Authors: Shruti Agarwal, Tarek El-Gaaly, Hany Farid, Ser-Nam Lim
Date: 2020-04-29 00:00:00
Tasks: Metric Learning

Synthetically-generated audios and videos -- so-called deep fakes -- continue to capture the imagination of the computer-graphics and computer-vision communities. At the same time, the democratization of access to technology that can create sophisticated manipulated video of anybody saying anything continues to be of concern because of its power to disrupt democratic elections, commit small to large-scale fraud, fuel dis-information campaigns, and create non-consensual pornography. We describe a biometric-based forensic technique for detecting face-swap deep fakes. This technique combines a static biometric based on facial recognition with a temporal, behavioral biometric based on facial expressions and head movements, where the behavioral embedding is learned using a CNN with a metric-learning objective function. We show the efficacy of this approach across several large-scale video datasets, as well as in-the-wild deep fakes.
Interactive Video Stylization Using Few-Shot Patch-Based Training
Authors: Ondřej Texler, David Futschik, Michal Kučera, Ondřej Jamriška, Šárka Sochorová, Menglei Chai, Sergey Tulyakov, Daniel Sýkora
Date: 2020-04-29 00:00:00
Tasks: Style Transfer, Translation, Video Propagation

In this paper, we present a learning-based method to the keyframe-based video stylization that allows an artist to propagate the style from a few selected keyframes to the rest of the sequence. Its key advantage is that the resulting stylization is semantically meaningful, i.e., specific parts of moving objects are stylized according to the artist's intention. In contrast to previous style transfer techniques, our approach does not require any lengthy pre-training process nor a large training dataset. We demonstrate how to train an appearance translation network from scratch using only a few stylized exemplars while implicitly preserving temporal consistency. This leads to a video stylization framework that supports real-time inference, parallel processing, and random access to an arbitrary output frame. It can also merge the content from multiple keyframes without the need to perform an explicit blending operation. We demonstrate its practical utility in various interactive scenarios, where the user paints over a selected keyframe and sees her style transferred to an existing recorded sequence or a live video stream.
Teaching Cameras to Feel: Estimating Tactile Physical Properties of Surfaces From Images
Authors: Matthew Purri, Kristin Dana
Date: 2020-04-29 00:00:00
Proceeding: ECCV 2020 8
Tasks: Friction, Neural Architecture Search

The connection between visual input and tactile sensing is critical for object manipulation tasks such as grasping and pushing. In this work, we introduce the challenging task of estimating a set of tactile physical properties from visual information. We aim to build a model that learns the complex mapping between visual information and tactile physical properties. We construct a first of its kind image-tactile dataset with over 400 multiview image sequences and the corresponding tactile properties. A total of fifteen tactile physical properties across categories including friction, compliance, adhesion, texture, and thermal conductance are measured and then estimated by our models. We develop a cross-modal framework comprised of an adversarial objective and a novel visuo-tactile joint classification loss. Additionally, we develop a neural architecture search framework capable of selecting optimal combinations of viewing angles for estimating a given physical property.
Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet Data
Authors: Eric L. Goodman, Chase Zimmerman, Corey Hudson
Date: 2020-04-29 00:00:00
Tasks: General Classification, image-classification, Image Classification, Intrusion Detection

One of deep learning's attractive benefits is the ability to automatically extract relevant features for a target problem from largely raw data, instead of utilizing human engineered and error prone handcrafted features. While deep learning has shown success in fields such as image classification and natural language processing, its application for feature extraction on raw network packet data for intrusion detection is largely unexplored. In this paper we modify a Word2Vec approach, used for text processing, and apply it to packet data for automatic feature extraction. We call this approach Packet2Vec. For the classification task of benign versus malicious traffic on a 2009 DARPA network data set, we obtain an area under the curve (AUC) of the receiver operating characteristic (ROC) between 0.988-0.996 and an AUC of the Precision/Recall curve between 0.604-0.667.