pwc-archive/papers-with-abstracts huggingface dataset:
ActiveHNE: Active Heterogeneous Network Embedding
Authors: Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, Xiangliang Zhang
Date: 2019-05-14 00:00:00
Tasks: Network Embedding

Heterogeneous network embedding (HNE) is a challenging task due to the diverse node types and/or diverse relationships between nodes. Existing HNE methods are typically unsupervised. To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network Embedding (ActiveHNE) framework, which includes two components: Discriminative Heterogeneous Network Embedding (DHNE) and Active Query in Heterogeneous Networks (AQHN). In DHNE, we introduce a novel semi-supervised heterogeneous network embedding method based on graph convolutional neural network. In AQHN, we first introduce three active selection strategies based on uncertainty and representativeness, and then derive a batch selection method that assembles these strategies using a multi-armed bandit mechanism. ActiveHNE aims at improving the performance of HNE by feeding the most valuable supervision obtained by AQHN into DHNE. Experiments on public datasets demonstrate the effectiveness of ActiveHNE and its advantage on reducing the query cost.
"Tom" pet robot applied to urban autism
Authors: Xingqian Li, Chenwei Lou, Jian Zhao, HuaPeng Wei, Hongwei Zhao
Date: 2019-05-14 00:00:00

With the fast development of network information technology, more and more people are immersed in the virtual community environment brought by the network, ignoring the social interaction in real life. The consequent urban autism problem has become more and more serious. Promoting offline communication between people " and "eliminating loneliness through emotional communication between pet robots and breeders" to solve this problem, and has developed a design called "Tom". "Tom" is a smart pet robot with a pet robot-based social mechanism Called "Tom-Talker". The main contribution of this paper is to propose a social mechanism called "Tom-Talker" that encourages users to socialize offline. And "Tom-Talker" also has a corresponding reward mechanism and a friend recommendation algorithm. It also proposes a pet robot named "Tom" with an emotional interaction algorithm to recognize users' emotions, simulate animal emotions and communicate emotionally with use s. This paper designs experiments and analyzes the results. The results show that our pet robots have a good effect on solving urban autism problems.
Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems
Authors: Fei Mi, Minlie Huang, Jiyong Zhang, Boi Faltings
Date: 2019-05-14 00:00:00
Tasks: Meta-Learning, Task-Oriented Dialogue Systems, Text Generation

Natural language generation (NLG) is an essential component of task-oriented dialogue systems. Despite the recent success of neural approaches for NLG, they are typically developed for particular domains with rich annotated training examples. In this paper, we study NLG in a low-resource setting to generate sentences in new scenarios with handful training examples. We formulate the problem from a meta-learning perspective, and propose a generalized optimization-based approach (Meta-NLG) based on the well-recognized model-agnostic meta-learning (MAML) algorithm. Meta-NLG defines a set of meta tasks, and directly incorporates the objective of adapting to new low-resource NLG tasks into the meta-learning optimization process. Extensive experiments are conducted on a large multi-domain dataset (MultiWoz) with diverse linguistic variations. We show that Meta-NLG significantly outperforms other training procedures in various low-resource configurations. We analyze the results, and demonstrate that Meta-NLG adapts extremely fast and well to low-resource situations.
Sample Efficient Toeplitz Covariance Estimation
Authors: Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco
Date: 2019-05-14 00:00:00

We study the sample complexity of estimating the covariance matrix $T$ of a distribution $\mathcal{D}$ over $d$-dimensional vectors, under the assumption that $T$ is Toeplitz. This assumption arises in many signal processing problems, where the covariance between any two measurements only depends on the time or distance between those measurements. We are interested in estimation strategies that may choose to view only a subset of entries in each vector sample $x \sim \mathcal{D}$, which often equates to reducing hardware and communication requirements in applications ranging from wireless signal processing to advanced imaging. Our goal is to minimize both 1) the number of vector samples drawn from $\mathcal{D}$ and 2) the number of entries accessed in each sample. We provide some of the first non-asymptotic bounds on these sample complexity measures that exploit $T$'s Toeplitz structure, and by doing so, significantly improve on results for generic covariance matrices. Our bounds follow from a novel analysis of classical and widely used estimation algorithms (along with some new variants), including methods based on selecting entries from each vector sample according to a so-called sparse ruler. In many cases, we pair our upper bounds with matching or nearly matching lower bounds. In addition to results that hold for any Toeplitz $T$, we further study the important setting when $T$ is close to low-rank, which is often the case in practice. We show that methods based on sparse rulers perform even better in this setting, with sample complexity scaling sublinearly in $d$. Motivated by this finding, we develop a new covariance estimation strategy that further improves on all existing methods in the low-rank case: when $T$ is rank-$k$ or nearly rank-$k$, it achieves sample complexity depending polynomially on $k$ and only logarithmically on $d$.
Monocular 3D Object Detection via Geometric Reasoning on Keypoints
Authors: Ivan Barabanau, Alexey Artemov, Evgeny Burnaev, Vyacheslav Murashkin
Date: 2019-05-14 00:00:00
Tasks: 3D Object Detection, Keypoint Detection, Monocular 3D Object Detection, Object, object-detection, Object Detection

Monocular 3D object detection is well-known to be a challenging vision task due to the loss of depth information; attempts to recover depth using separate image-only approaches lead to unstable and noisy depth estimates, harming 3D detections. In this paper, we propose a novel keypoint-based approach for 3D object detection and localization from a single RGB image. We build our multi-branch model around 2D keypoint detection in images and complement it with a conceptually simple geometric reasoning method. Our network performs in an end-to-end manner, simultaneously and interdependently estimating 2D characteristics, such as 2D bounding boxes, keypoints, and orientation, along with full 3D pose in the scene. We fuse the outputs of distinct branches, applying a reprojection consistency loss during training. The experimental evaluation on the challenging KITTI dataset benchmark demonstrates that our network achieves state-of-the-art results among other monocular 3D detectors.
How to Fine-Tune BERT for Text Classification?
Authors: Chi Sun, Xipeng Qiu, Yige Xu, Xuanjing Huang
Date: 2019-05-14 00:00:00
Tasks: General Classification, Language Modeling, Language Modelling, Sentiment Analysis, Text Classification

Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in many language understanding tasks. In this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning. Finally, the proposed solution obtains new state-of-the-art results on eight widely-studied text classification datasets.
Imputing Missing Events in Continuous-Time Event Streams
Authors: Hongyuan Mei, Guanghui Qin, Jason Eisner
Date: 2019-05-14 00:00:00

Events in the world may be caused by other, unobserved events. We consider sequences of events in continuous time. Given a probability model of complete sequences, we propose particle smoothing---a form of sequential importance sampling---to impute the missing events in an incomplete sequence. We develop a trainable family of proposal distributions based on a type of bidirectional continuous-time LSTM: Bidirectionality lets the proposals condition on future observations, not just on the past as in particle filtering. Our method can sample an ensemble of possible complete sequences (particles), from which we form a single consensus prediction that has low Bayes risk under our chosen loss metric. We experiment in multiple synthetic and real domains, using different missingness mechanisms, and modeling the complete sequences in each domain with a neural Hawkes process (Mei & Eisner 2017). On held-out incomplete sequences, our method is effective at inferring the ground-truth unobserved events, with particle smoothing consistently improving upon particle filtering.
TauRieL: Targeting Traveling Salesman Problem with a deep reinforcement learning inspired architecture
Authors: Gorker Alp Malazgirt, Osman S. Unsal, Adrian Cristal Kestelman
Date: 2019-05-14 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Traveling Salesman Problem

In this paper, we propose TauRieL and target Traveling Salesman Problem (TSP) since it has broad applicability in theoretical and applied sciences. TauRieL utilizes an actor-critic inspired architecture that adopts ordinary feedforward nets to obtain a policy update vector $v$. Then, we use $v$ to improve the state transition matrix from which we generate the policy. Also, the state transition matrix allows the solver to initialize from precomputed solutions such as nearest neighbors. In an online learning setting, TauRieL unifies the training and the search where it can generate near-optimal results in seconds. The input to the neural nets in the actor-critic architecture are raw 2-D inputs, and the design idea behind this decision is to keep neural nets relatively smaller than the architectures with wide embeddings with the tradeoff of omitting any distributed representations of the embeddings. Consequently, TauRieL generates TSP solutions two orders of magnitude faster per TSP instance as compared to state-of-the-art offline techniques with a performance impact of 6.1\% in the worst case.
Efficient Computation of Hessian Matrices in TensorFlow
Authors: Geir K. Nilsen, Antonella Z. Munthe-Kaas, Hans J. Skaug, Morten Brun
Date: 2019-05-14 00:00:00
Tasks: Deep Learning

The Hessian matrix has a number of important applications in a variety of different fields, such as optimzation, image processing and statistics. In this paper we focus on the practical aspects of efficiently computing Hessian matrices in the context of deep learning using the Python scripting language and the TensorFlow library. We define a general feed-forward neural network model and show how to efficiently compute two quantities: the cost function's exact Hessian matrix, and the cost function's approximate Hessian matrix, known as the Outer Product of Gradients (OPG) matrix. Furthermore, as the number of parameters (weights and biases) in deep learning usually is very large, we show how to reduce the quadratic space complexity by an efficient implementation based on approximate eigendecompositions.
A Dynamic Evolutionary Framework for Timeline Generation based on Distributed Representations
Authors: Dongyun Liang, Guo-Hua Wang, Jing Nie
Date: 2019-05-14 00:00:00
Tasks: Timeline Summarization

Given the collection of timestamped web documents related to the evolving topic, timeline summarization (TS) highlights its most important events in the form of relevant summaries to represent the development of a topic over time. Most of the previous work focuses on fully-observable ranking models and depends on hand-designed features or complex mechanisms that may not generalize well. We present a novel dynamic framework for evolutionary timeline generation leveraging distributed representations, which dynamically finds the most likely sequence of evolutionary summaries in the timeline, called the Viterbi timeline, and reduces the impact of events that irrelevant or repeated to the topic. The assumptions of the coherence and the global view run through our model. We explore adjacent relevance to constrain timeline coherence and make sure the events evolve on the same topic with a global view. Experimental results demonstrate that our framework is feasible to extract summaries for timeline generation, outperforms various competitive baselines, and achieves the state-of-the-art performance as an unsupervised approach.
Multilingual Factor Analysis
Authors: Francisco Vargas, Kamen Brestnichki, Alex Papadopoulos-Korfiatis, Nils Hammerla
Date: 2019-05-14 00:00:00
Proceeding: ACL 2019 7

In this work we approach the task of learning multilingual word representations in an offline manner by fitting a generative latent variable model to a multilingual dictionary. We model equivalent words in different languages as different views of the same word generated by a common latent variable representing their latent lexical meaning. We explore the task of alignment by querying the fitted model for multilingual embeddings achieving competitive results across a variety of tasks. The proposed model is robust to noise in the embedding space making it a suitable method for distributed representations learned from noisy corpora.
The Language of Legal and Illegal Activity on the Darknet
Authors: Leshem Choshen, Dan Eldad, Daniel Hershcovich, Elior Sulem, Omri Abend
Date: 2019-05-14 00:00:00
Proceeding: ACL 2019 7
Tasks: POS

The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity. Given the magnitude of these networks, scalably monitoring their activity necessarily relies on automated tools, and notably on NLP tools. However, little is known about what characteristics texts communicated through the Darknet have, and how well off-the-shelf NLP tools do on this domain. This paper tackles this gap and performs an in-depth investigation of the characteristics of legal and illegal text in the Darknet, comparing it to a clear net website with similar content as a control condition. Taking drug-related websites as a test case, we find that texts for selling legal and illegal drugs have several linguistic characteristics that distinguish them from one another, as well as from the control condition, among them the distribution of POS tags, and the coverage of their named entities in Wikipedia.
A self-organising eigenspace map for time series clustering
Authors: Donya Rahmani, Damien Fay, Jacek Brodzki
Date: 2019-05-14 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis, Time Series Clustering, Time Series Forecasting, valid

This paper presents a novel time series clustering method, the self-organising eigenspace map (SOEM), based on a generalisation of the well-known self-organising feature map (SOFM). The SOEM operates on the eigenspaces of the embedded covariance structures of time series which are related directly to modes in those time series. Approximate joint diagonalisation acts as a pseudo-metric across these spaces allowing us to generalise the SOFM to a neural network with matrix input. The technique is empirically validated against three sets of experiments; univariate and multivariate time series clustering, and application to (clustered) multi-variate time series forecasting. Results indicate that the technique performs a valid topologically ordered clustering of the time series. The clustering is superior in comparison to standard benchmarks when the data is non-aligned, gives the best clustering stage for when used in forecasting, and can be used with partial/non-overlapping time series, multivariate clustering and produces a topological representation of the time series objects.
Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting
Authors: Maria Becker, Michael Staniek, Vivi Nastase, Anette Frank
Date: 2019-05-14 00:00:00
Proceeding: WS 2019 5
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Relation, Relation Classification

Commonsense knowledge relations are crucial for advanced NLU tasks. We examine the learnability of such relations as represented in CONCEPTNET, taking into account their specific properties, which can make relation classification difficult: a given concept pair can be linked by multiple relation types, and relations can have multi-word arguments of diverse semantic types. We explore a neural open world multi-label classification approach that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the specific properties of the CONCEPTNET resource, we investigate the impact of different relation representations and model variations. Our analysis reveals that the complexity of argument types and relation ambiguity are the most important challenges to address. We design a customized evaluation method to address the incompleteness of the resource that can be expanded in future work.
Atom Responding Machine for Dialog Generation
Authors: Ganbin Zhou, Ping Luo, Jingwu Chen, Fen Lin, Leyu Lin, Qing He
Date: 2019-05-14 00:00:00
Tasks: Decoder, Diversity

Recently, improving the relevance and diversity of dialogue system has attracted wide attention. For a post x, the corresponding response y is usually diverse in the real-world corpus, while the conventional encoder-decoder model tends to output the high-frequency (safe but trivial) responses and thus is difficult to handle the large number of responding styles. To address these issues, we propose the Atom Responding Machine (ARM), which is based on a proposed encoder-composer-decoder network trained by a teacher-student framework. To enrich the generated responses, ARM introduces a large number of molecule-mechanisms as various responding styles, which are conducted by taking different combinations from a few atom-mechanisms. In other words, even a little of atom-mechanisms can make a mickle of molecule-mechanisms. The experiments demonstrate diversity and quality of the responses generated by ARM. We also present generating process to show underlying interpretability for the result.
Entity-Relation Extraction as Multi-Turn Question Answering
Authors: Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, Jiwei Li
Date: 2019-05-14 00:00:00
Proceeding: ACL 2019 7
Tasks: Machine Reading Comprehension, Question Answering, Reading Comprehension, Relation, Relation Extraction, Triplet

In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and relations is transformed to the task of identifying answer spans from the context. This multi-turn QA formalization comes with several key advantages: firstly, the question query encodes important information for the entity/relation class we want to identify; secondly, QA provides a natural way of jointly modeling entity and relation; and thirdly, it allows us to exploit the well developed machine reading comprehension (MRC) models. Experiments on the ACE and the CoNLL04 corpora demonstrate that the proposed paradigm significantly outperforms previous best models. We are able to obtain the state-of-the-art results on all of the ACE04, ACE05 and CoNLL04 datasets, increasing the SOTA results on the three datasets to 49.4 (+1.0), 60.2 (+0.6) and 68.9 (+2.1), respectively. Additionally, we construct a newly developed dataset RESUME in Chinese, which requires multi-step reasoning to construct entity dependencies, as opposed to the single-step dependency extraction in the triplet exaction in previous datasets. The proposed multi-turn QA model also achieves the best performance on the RESUME dataset.
Is Word Segmentation Necessary for Deep Learning of Chinese Representations?
Authors: Xiaoya Li, Yuxian Meng, Xiaofei Sun, Qinghong Han, Arianna Yuan, Jiwei Li
Date: 2019-05-14 00:00:00
Proceeding: ACL 2019 7
Tasks: Chinese Word Segmentation, Deep Learning, Language Modeling, Language Modelling, Machine Translation, Segmentation, Sentence, text-classification, Text Classification, Translation

Segmenting a chunk of text into words is usually the first step of processing Chinese text, but its necessity has rarely been explored. In this paper, we ask the fundamental question of whether Chinese word segmentation (CWS) is necessary for deep learning-based Chinese Natural Language Processing. We benchmark neural word-based models which rely on word segmentation against neural char-based models which do not involve word segmentation in four end-to-end NLP benchmark tasks: language modeling, machine translation, sentence matching/paraphrase and text classification. Through direct comparisons between these two types of models, we find that char-based models consistently outperform word-based models. Based on these observations, we conduct comprehensive experiments to study why word-based models underperform char-based models in these deep learning-based NLP tasks. We show that it is because word-based models are more vulnerable to data sparsity and the presence of out-of-vocabulary (OOV) words, and thus more prone to overfitting. We hope this paper could encourage researchers in the community to rethink the necessity of word segmentation in deep learning-based Chinese Natural Language Processing. \footnote{Yuxian Meng and Xiaoya Li contributed equally to this paper.}
Deep Residual Output Layers for Neural Language Generation
Authors: Nikolaos Pappas, James Henderson
Date: 2019-05-14 00:00:00
Tasks: Language Modelling, Machine Translation, Text Generation

Many tasks, including language generation, benefit from learning the structure of the output space, particularly when the space of output labels is large and the data is sparse. State-of-the-art neural language models indirectly capture the output space structure in their classifier weights since they lack parameter sharing across output labels. Learning shared output label mappings helps, but existing methods have limited expressivity and are prone to overfitting. In this paper, we investigate the usefulness of more powerful shared mappings for output labels, and propose a deep residual output mapping with dropout between layers to better capture the structure of the output space and avoid overfitting. Evaluations on three language generation tasks show that our output label mapping can match or improve state-of-the-art recurrent and self-attention architectures, and suggest that the classifier does not necessarily need to be high-rank to better model natural language if it is better at capturing the structure of the output space.