pwc-archive/papers-with-abstracts huggingface dataset:
Predict Anchor Links across Social Networks via an Embedding Approach
Authors: Tong Man, Hua-Wei Shen, Shenghua Liu, Xiaolong Jin, and Xueqi Cheng
Date: 2016-06-25 00:00:00
Proceeding: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) 2016 6
Tasks: Anchor link prediction, Link Prediction, Network Embedding

Predicting anchor links across social networks has
important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation. One challenging problem is: whether and to what extent
we can address the anchor link prediction problem, if only structural information of networks is
available. Most existing methods, unsupervised or
supervised, directly work on networks themselves
rather than on their intrinsic structural regularities,
and thus their effectiveness is sensitive to the high
dimension and sparsity of networks. To offer a robust method, we propose a novel supervised model,
called PALE, which employs network embedding
with awareness of observed anchor links as supervised information to capture the major and specific structural regularities and further learns a stable cross-network mapping for predicting anchor
links. Through extensive experiments on two realistic datasets, we demonstrate that PALE significantly outperforms the state-of-the-art methods
A Skip Connection Architecture for Localization of Image Manipulations
Authors: Ghazal Mazaheri, Niluthpol Chowdhury Mithun, Jawadul H. Bappy, Amit K. Roy-Chowdhury
Date: 2019-06-01 00:00:00
Proceeding: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019 6
Tasks: Decoder, Image Manipulation, Image Manipulation Detection

Detection and localization of image manipulations are becoming of increasing interest to researchers in recent years due to the significant rise of malicious content-changing image tampering on the web. One of the major challenges for an image manipulation detection method is to discriminate between the tampered regions and other regions in an image. We observe that most of the manipulated images leave some traces near boundaries of manipulated regions including blurred edges. In order to exploit these traces in localizing the tampered regions, we propose an encoder-decoder based network where we fuse representations from early layers in the encoder (which are richer in low-level spatial cues, like edges) by skip pooling with representations of the last layer of the decoder and use for manipulation detection. In addition, we utilize resampling features extracted from patches of images by feeding them to LSTM cells to capture the transition between manipulated and non manipulated blocks in the frequency domain and combine the output of the LSTM with our encoder. The overall framework is capable of detecting different types of image manipulations simultaneously including copy-move, removal, and splicing. Experimental results on two standard benchmark datasets (CASIA 1.0 and NIST’16) demonstrate that the proposed method can achieve a significantly better performance than the state-of-the-art methods and baselines.
Improved Heterogeneous Distance Functions
Authors: D. R. Wilson, T. R. Martinez
Date: 1997-01-01 00:00:00
Tasks: Attribute

Instance-based learning techniques typically handle continuous and linear input values well, but often do not handle nominal input attributes appropriately. The Value Difference Metric (VDM) was designed to find reasonable distance values between nominal attribute values, but it largely ignores continuous attributes, requiring discretization to map continuous values into nominal values. This paper proposes three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference Metric (IVDM), and the Windowed Value Difference Metric (WVDM). These new distance functions are designed to handle applications with nominal attributes, continuous attributes, or both. In experiments on 48 applications the new distance metrics achieve higher classification accuracy on average than three previous distance functions on those datasets that have both nominal and continuous attributes.
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Authors: Yaniv Taigman, Ming Yang, Marc’ Aurelio Ranzato, Lior Wolf
Date: 2014-06-24 00:00:00
Proceeding: Conference on Computer Vision and Pattern Recognition (CVPR) 2014 6
Tasks: 3D Face Modelling, Face Recognition, Face Verification

In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities.

The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.
Addictive Auctions: using lucky-draw and gambling addiction to increase participation during auctioning
Authors: Ravin Kumar
Date: 2019-06-07 00:00:00
Tasks: Fairness

Auction theories are believed to provide a better selling opportunity for the resources to be allocated. Various organizations have taken measures to increase trust among participants towards their auction system, but trust alone cannot ensure a high level of participation. We propose a new type of auction system which takes advantage of lucky draw and gambling addictions to increase the engagement level of candidates in an auction. Our system makes use of security features present in existing auction systems for ensuring fairness and maintaining trust among participants.
Complexity of Representation and Inference in Compositional Models with Part Sharing
Authors: Alan L. Yuille, Roozbeh Mottaghi
Date: 2013-01-16 00:00:00

This paper describes serial and parallel compositional models of multiple objects with part sharing. Objects are built by part-subpart compositions and expressed in terms of a hierarchical dictionary of object parts. These parts are represented on lattices of decreasing sizes which yield an executive summary description. We describe inference and learning algorithms for these models. We analyze the complexity of this model in terms of computation time (for serial computers) and numbers of nodes (e.g., "neurons") for parallel computers. In particular, we compute the complexity gains by part sharing and its dependence on how the dictionary scales with the level of the hierarchy. We explore three regimes of scaling behavior where the dictionary size (i) increases exponentially with the level, (ii) is determined by an unsupervised compositional learning algorithm applied to real data, (iii) decreases exponentially with scale. This analysis shows that in some regimes the use of shared parts enables algorithms which can perform inference in time linear in the number of levels for an exponential number of objects. In other regimes part sharing has little advantage for serial computers but can give linear processing on parallel computers.
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and Enhancement
Authors: Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, Ming-Hsuan Yang
Date: 2018-10-20 00:00:00
Proceeding: arXiv 2018 10
Tasks: Computational Efficiency, Denoising, Motion Compensation, Motion Estimation, Optical Flow Estimation, Super-Resolution, Video Enhancement, Video Frame Interpolation

Motion estimation (ME) and motion compensation (MC) have dominated classical video frame interpolation systems over the past decades. Recently, the convolutional neural networks set up a new data-driven paradigm for frame interpolation. However, existing learning based methods typically fall into estimating only one of the ME and MC building blocks, resulting in a limited performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and motion compensation driven neural network for video frame interpolation. A novel adaptive warping layer is proposed to integrate both optical flow and interpolation kernels to synthesize target frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. Our method benefits from the ME and MC model-driven architecture while avoiding the conventional hand-crafted design by training on a large amount of video data. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Moreover, our MEMC architecture is a general framework, which can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video frame interpolation and enhancement algorithms on a wide range of datasets.
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues
Authors: Chongyang Tao, Wei Wu, Can Xu, Wenpeng Hu, Dongyan Zhao, Rui Yan
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Conversational Response Selection, Retrieval

Currently, researchers have paid great attention to retrieval-based dialogues in open-domain. In particular, people study the problem by investigating context-response matching for multi-turn response selection based on publicly recognized benchmark data sets. State-of-the-art methods require a response to interact with each utterance in a context from the beginning, but the interaction is performed in a shallow way. In this work, we let utterance-response interaction go deep by proposing an interaction-over-interaction network (IoI). The model performs matching by stacking multiple interaction blocks in which residual information from one time of interaction initiates the interaction process again. Thus, matching information within an utterance-response pair is extracted from the interaction of the pair in an iterative fashion, and the information flows along the chain of the blocks via representations. Evaluation results on three benchmark data sets indicate that IoI can significantly outperform state-of-the-art methods in terms of various matching metrics. Through further analysis, we also unveil how the depth of interaction affects the performance of IoI.
Boosting Dialog Response Generation
Authors: Wenchao Du, Alan W. black
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Diversity, Response Generation

Neural models have become one of the most important approaches to dialog response generation. However, they still tend to generate the most common and generic responses in the corpus all the time. To address this problem, we designed an iterative training process and ensemble method based on boosting. We combined our method with different training and decoding paradigms as the base model, including mutual-information-based decoding and reward-augmented maximum likelihood learning. Empirical results show that our approach can significantly improve the diversity and relevance of the responses generated by all base models, backed by objective measurements and human evaluation.
Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection
Authors: Junyu Lu, Chenbin Zhang, Zeying Xie, Guang Ling, Tom Chao Zhou, Zenglin Xu
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Text Matching

Response selection plays an important role in fully automated dialogue systems. Given the dialogue context, the goal of response selection is to identify the best-matched next utterance (i.e., response) from multiple candidates. Despite the efforts of many previous useful models, this task remains challenging due to the huge semantic gap and also the large size of candidate set. To address these issues, we propose a Spatio-Temporal Matching network (STM) for response selection. In detail, soft alignment is first used to obtain the local relevance between the context and the response. And then, we construct spatio-temporal features by aggregating attention images in time dimension and make use of 3D convolution and pooling operations to extract matching information. Evaluation on two large-scale multi-turn response selection tasks has demonstrated that our proposed model significantly outperforms the state-of-the-art model. Particularly, visualization analysis shows that the spatio-temporal features enables matching information in segment pairs and time sequences, and have good interpretability for multi-turn text matching.
Reliability-aware Dynamic Feature Composition for Name Tagging
Authors: Ying Lin, Liyuan Liu, Heng Ji, Dong Yu, Jiawei Han
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Named Entity Recognition (NER), Word Embeddings

Word embeddings are widely used on a variety of tasks and can substantially improve the performance. However, their quality is not consistent throughout the vocabulary due to the long-tail distribution of word frequency. Without sufficient contexts, rare word embeddings are usually less reliable than those of common words. However, current models typically trust all word embeddings equally regardless of their reliability and thus may introduce noise and hurt the performance. Since names often contain rare and uncommon words, this problem is particularly critical for name tagging. In this paper, we propose a novel reliability-aware name tagging model to tackle this issue. We design a set of word frequency-based reliability signals to indicate the quality of each word embedding. Guided by the reliability signals, the model is able to dynamically select and compose features such as word embedding and character-level representation using gating mechanisms. For example, if an input word is rare, the model relies less on its word embedding and assigns higher weights to its character and contextual features. Experiments on OntoNotes 5.0 show that our model outperforms the baseline model by 2.7{\%} absolute gain in F-score. In cross-genre experiments on five genres in OntoNotes, our model improves the performance for most genre pairs and obtains up to 5{\%} absolute F-score gain.
Effective Adversarial Regularization for Neural Machine Translation
Authors: Motoki Sato, Jun Suzuki, Shun Kiyono
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Machine Translation, NMT, text-classification, Text Classification, Translation

A regularization technique based on adversarial perturbation, which was initially developed in the field of image processing, has been successfully applied to text classification tasks and has yielded attractive improvements. We aim to further leverage this promising methodology into more sophisticated and critical neural models in the natural language processing field, i.e., neural machine translation (NMT) models. However, it is not trivial to apply this methodology to such models. Thus, this paper investigates the effectiveness of several possible configurations of applying the adversarial perturbation and reveals that the adversarial regularization technique can significantly and consistently improve the performance of widely used NMT models, such as LSTM-based and Transformer-based models.
Neural Relation Extraction for Knowledge Base Enrichment
Authors: Bayu Distiawan Trisedya, Gerhard Weikum, Jianzhong Qi, Rui Zhang
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Decoder, Entity Disambiguation, Entity Embeddings, Relation, Relation Extraction, Sentence

We study relation extraction for knowledge base (KB) enrichment. Specifically, we aim to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end-to-end manner. Previous studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into the KB space. This way, NED errors may cause extraction errors that affect the overall precision and recall.To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model. We collect high-quality training data by distant supervision with co-reference resolution and paraphrase detection. We propose an n-gram based attention model that captures multi-word entity names in a sentence. Our model employs jointly learned word and entity embeddings to support named entity disambiguation. Finally, our model uses a modified beam search and a triple classifier to help generate high-quality triples. Our model outperforms state-of-the-art baselines by 15.51{\%} and 8.38{\%} in terms of F1 score on two real-world datasets.
Spatial Aggregation Facilitates Discovery of Spatial Topics
Authors: Aniruddha Maiti, Slobodan Vucetic
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

Spatial aggregation refers to merging of documents created at the same spatial location. We show that by spatial aggregation of a large collection of documents and applying a traditional topic discovery algorithm on the aggregated data we can efficiently discover spatially distinct topics. By looking at topic discovery through matrix factorization lenses we show that spatial aggregation allows low rank approximation of the original document-word matrix, in which spatially distinct topics are preserved and non-spatial topics are aggregated into a single topic. Our experiments on synthetic data confirm this observation. Our experiments on 4.7 million tweets collected during the Sandy Hurricane in 2012 show that spatial and temporal aggregation allows rapid discovery of relevant spatial and temporal topics during that period. Our work indicates that different forms of document aggregation might be effective in rapid discovery of various types of distinct topics from large collections of documents.
You Only Need Attention to Traverse Trees
Authors: Mahtab Ahmed, Muhammad Rifayat Samee, Robert E. Mercer
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Sentence

In recent NLP research, a topic of interest is universal sentence encoding, sentence representations that can be used in any supervised task. At the word sequence level, fully attention-based models suffer from two problems: a quadratic increase in memory consumption with respect to the sentence length and an inability to capture and use syntactic information. Recursive neural nets can extract very good syntactic information by traversing a tree structure. To this end, we propose Tree Transformer, a model that captures phrase level syntax for constituency trees as well as word-level dependencies for dependency trees by doing recursive traversal only with attention. Evaluation of this model on four tasks gets noteworthy results compared to the standard transformer and LSTM-based models as well as tree-structured LSTMs. Ablation studies to find whether positional information is inherently encoded in the trees and which type of attention is suitable for doing the recursive traversal are provided.
Neural News Recommendation with Long- and Short-term User Representations
Authors: Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: News Recommendation

Personalized news recommendation is important to help users find their interested news and improve reading experience. A key problem in news recommendation is learning accurate user representations to capture their interests. Users usually have both long-term preferences and short-term interests. However, existing news recommendation methods usually learn single representations of users, which may be insufficient. In this paper, we propose a neural news recommendation approach which can learn both long- and short-term user representations. The core of our approach is a news encoder and a user encoder. In the news encoder, we learn representations of news from their titles and topic categories, and use attention network to select important words. In the user encoder, we propose to learn long-term user representations from the embeddings of their IDs.In addition, we propose to learn short-term user representations from their recently browsed news via GRU network. Besides, we propose two methods to combine long-term and short-term user representations. The first one is using the long-term user representation to initialize the hidden state of the GRU network in short-term user representation. The second one is concatenating both long- and short-term user representations as a unified user vector. Extensive experiments on a real-world dataset show our approach can effectively improve the performance of neural news recommendation.
Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings
Authors: Zied Haj-Yahia, Adrien Sieg, L{\'e}a A. Deleris
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Classification, General Classification, Text Categorization, text-classification, Text Classification, Unsupervised Text Classification, Word Embeddings

Text classification aims at mapping documents into a set of predefined categories. Supervised machine learning models have shown great success in this area but they require a large number of labeled documents to reach adequate accuracy. This is particularly true when the number of target categories is in the tens or the hundreds. In this work, we explore an unsupervised approach to classify documents into categories simply described by a label. The proposed method is inspired by the way a human proceeds in this situation: It draws on textual similarity between the most relevant words in each document and a dictionary of keywords for each category reflecting its semantics and lexical field. The novelty of our method hinges on the enrichment of the category labels through a combination of human expertise and language models, both generic and domain specific. Our experiments on 5 standard corpora show that the proposed method increases F1-score over relying solely on human expertise and can also be on par with simple supervised approaches. It thus provides a practical alternative to situations where low cost text categorization is needed, as we illustrate with our application to operational risk incidents classification.
Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table
Authors: Matthew Shardlow, Raheel Nawaz
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Text Simplification

Clinical letters are infamously impenetrable for the lay patient. This work uses neural text simplification methods to automatically improve the understandability of clinical letters for patients. We take existing neural text simplification software and augment it with a new phrase table that links complex medical terminology to simpler vocabulary by mining SNOMED-CT. In an evaluation task using crowdsourcing, we show that the results of our new system are ranked easier to understand (average rank 1.93) than using the original system (2.34) without our phrase table. We also show improvement against baselines including the original text (2.79) and using the phrase table without the neural text simplification software (2.94). Our methods can easily be transferred outside of the clinical domain by using domain-appropriate resources to provide effective neural text simplification for any domain without the need for costly annotation.