pwc-archive/papers-with-abstracts huggingface dataset:
Stylized Dialogue Response Generation Using Stylized Unpaired Texts
Authors: Yinhe Zheng, Zikai Chen, Rongsheng Zhang, Shilei Huang, Xiaoxi Mao, Minlie Huang
Date: 2020-09-27 00:00:00
Tasks: Decoder, Dialogue Generation, Response Generation

Generating stylized responses is essential to build intelligent and engaging dialogue systems. However, this task is far from well-explored due to the difficulties of rendering a particular style in coherent responses, especially when the target style is embedded only in unpaired texts that cannot be directly used to train the dialogue model. This paper proposes a stylized dialogue generation method that can capture stylistic features embedded in unpaired texts. Specifically, our method can produce dialogue responses that are both coherent to the given context and conform to the target style. In this study, an inverse dialogue model is first introduced to predict possible posts for the input responses, and then this inverse model is used to generate stylized pseudo dialogue pairs based on these stylized unpaired texts. Further, these pseudo pairs are employed to train the stylized dialogue model with a joint training process, and a style routing approach is proposed to intensify stylistic features in the decoder. Automatic and manual evaluations on two datasets demonstrate that our method outperforms competitive baselines in producing coherent and style-intensive dialogue responses.
A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution
Authors: Hongming Zhang, Xinran Zhao, Yangqiu Song
Date: 2020-09-27 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Natural Language Understanding, Relation Prediction, Survey

Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to. Compared with the general coreference resolution task, the main challenge of PCR is the coreference relation prediction rather than the mention detection. As one important natural language understanding (NLU) component, pronoun resolution is crucial for many downstream tasks and still challenging for existing models, which motivates us to survey existing approaches and think about how to do better. In this survey, we first introduce representative datasets and models for the ordinary pronoun coreference resolution task. Then we focus on recent progress on hard pronoun coreference resolution problems (e.g., Winograd Schema Challenge) to analyze how well current models can understand commonsense. We conduct extensive experiments to show that even though current models are achieving good performance on the standard evaluation set, they are still not ready to be used in real applications (e.g., all SOTA models struggle on correctly resolving pronouns to infrequent objects). All experiment codes are available at https://github.com/HKUST-KnowComp/PCR.
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Authors: Wenhan Xiong, Xiang Lorraine Li, Srini Iyer, Jingfei Du, Patrick Lewis, William Yang Wang, Yashar Mehdad, Wen-tau Yih, Sebastian Riedel, Douwe Kiela, Barlas Oğuz
Date: 2020-09-27 00:00:00
Proceeding: ICLR 2021 1
Tasks: Question Answering, Retrieval

We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER. Contrary to previous work, our method does not require access to any corpus-specific information, such as inter-document hyperlinks or human-annotated entity markers, and can be applied to any unstructured text corpus. Our system also yields a much better efficiency-accuracy trade-off, matching the best published accuracy on HotpotQA while being 10 times faster at inference time.
TernaryBERT: Distillation-aware Ultra-low Bit BERT
Authors: Wei Zhang, Lu Hou, Yichun Yin, Lifeng Shang, Xiao Chen, Xin Jiang, Qun Liu
Date: 2020-09-27 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Knowledge Distillation, Quantization

Transformer-based pre-training models like BERT have achieved remarkable performance in many natural language processing tasks.However, these models are both computation and memory expensive, hindering their deployment to resource-constrained devices. In this work, we propose TernaryBERT, which ternarizes the weights in a fine-tuned BERT model. Specifically, we use both approximation-based and loss-aware ternarization methods and empirically investigate the ternarization granularity of different parts of BERT. Moreover, to reduce the accuracy degradation caused by the lower capacity of low bits, we leverage the knowledge distillation technique in the training process. Experiments on the GLUE benchmark and SQuAD show that our proposed TernaryBERT outperforms the other BERT quantization methods, and even achieves comparable performance as the full-precision model while being 14.9x smaller.
What does it mean to be language-agnostic? Probing multilingual sentence encoders for typological properties
Authors: Rochelle Choenni, Ekaterina Shutova
Date: 2020-09-27 00:00:00
Tasks: Sentence, XLM-R

Multilingual sentence encoders have seen much success in cross-lingual model transfer for downstream NLP tasks. Yet, we know relatively little about the properties of individual languages or the general patterns of linguistic variation that they encode. We propose methods for probing sentence representations from state-of-the-art multilingual encoders (LASER, M-BERT, XLM and XLM-R) with respect to a range of typological properties pertaining to lexical, morphological and syntactic structure. In addition, we investigate how this information is distributed across all layers of the models. Our results show interesting differences in encoding linguistic variation associated with different pretraining strategies.
Semantic-based Distance Approaches in Multi-objective Genetic Programming
Authors: Edgar Galván, Fergal Stapleton
Date: 2020-09-25 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Semantics in the context of Genetic Program (GP) can be understood as the behaviour of a program given a set of inputs and has been well documented in improving performance of GP for a range of diverse problems. There have been a wide variety of different methods which have incorporated semantics into single-objective GP. The study of semantics in Multi-objective (MO) GP, however, has been limited and this paper aims at tackling this issue. More specifically, we conduct a comparison of three different forms of semantics in MOGP. One semantic-based method, (i) Semantic Similarity-based Crossover (SSC), is borrowed from single-objective GP, where the method has consistently being reported beneficial in evolutionary search. We also study two other methods, dubbed (ii) Semantic-based Distance as an additional criteriOn (SDO) and (iii) Pivot Similarity SDO. We empirically and consistently show how by naturally handling semantic distance as an additional criterion to be optimised in MOGP leads to better performance when compared to canonical methods and SSC. Both semantic distance based approaches made use of a pivot, which is a reference point from the sparsest region of the search space and it was found that individuals which were both semantically similar and dissimilar to this pivot were beneficial in promoting diversity. Moreover, we also show how the semantics successfully promoted in single-objective optimisation does not necessary lead to a better performance when adopted in MOGP.
Analyzing Adaptive Parameter Landscapes in Parameter Adaptation Methods for Differential Evolution
Authors: Ryoji Tanabe
Date: 2020-09-26 00:00:00

Since the scale factor and the crossover rate significantly influence the performance of differential evolution (DE), parameter adaptation methods (PAMs) for the two parameters have been well studied in the DE community. Although PAMs can sufficiently improve the effectiveness of DE, PAMs are poorly understood (e.g., the working principle of PAMs). One of the difficulties in understanding PAMs comes from the unclarity of the parameter space that consists of the scale factor and the crossover rate. This paper addresses this issue by analyzing adaptive parameter landscapes in PAMs for DE. First, we propose a concept of an adaptive parameter landscape, which captures a moment in a parameter adaptation process. For each iteration, each individual in the population has its adaptive parameter landscape. Second, we propose a method of analyzing adaptive parameter landscapes using a 1-step-lookahead greedy improvement metric. Third, we examine adaptive parameter landscapes in PAMs by using the proposed method. Results provide insightful information about PAMs in DE.
Reliability-Performance Trade-offs in Neuromorphic Computing
Authors: Twisha Titirsha, Anup Das
Date: 2020-09-26 00:00:00
Tasks: BIG-bench Machine Learning

Neuromorphic architectures built with Non-Volatile Memory (NVM) can significantly improve the energy efficiency of machine learning tasks designed with Spiking Neural Networks (SNNs). A major source of voltage drop in a crossbar of these architectures are the parasitic components on the crossbar's bitlines and wordlines, which are deliberately made longer to achieve lower cost-per-bit. We observe that the parasitic voltage drops create a significant asymmetry in programming speed and reliability of NVM cells in a crossbar. Specifically, NVM cells that are on shorter current paths are faster to program but have lower endurance than those on longer current paths, and vice versa. This asymmetry in neuromorphic architectures create reliability-performance trade-offs, which can be exploited efficiently using SNN mapping techniques. In this work, we demonstrate such trade-offs using a previously-proposed SNN mapping technique with 10 workloads from contemporary machine learning tasks for a state-of-the art neuromoorphic hardware.
An Analysis of Quality Indicators Using Approximated Optimal Distributions in a Three-dimensional Objective Space
Authors: Ryoji Tanabe, Hisao Ishibuchi
Date: 2020-09-27 00:00:00
Tasks: Benchmarking

Although quality indicators play a crucial role in benchmarking evolutionary multi-objective optimization algorithms, their properties are still unclear. One promising approach for understanding quality indicators is the use of the optimal distribution of objective vectors that optimizes each quality indicator. However, it is difficult to obtain the optimal distribution for each quality indicator, especially when its theoretical property is unknown. Thus, optimal distributions for most quality indicators have not been well investigated. To address these issues, first, we propose a problem formulation of finding the optimal distribution for each quality indicator on an arbitrary Pareto front. Then, we approximate the optimal distributions for nine quality indicators using the proposed problem formulation. We analyze the nine quality indicators using their approximated optimal distributions on eight types of Pareto fronts of three-objective problems. Our analysis demonstrates that uniformly-distributed objective vectors over the entire Pareto front are not optimal in many cases. Each quality indicator has its own optimal distribution for each Pareto front. We also examine the consistency among the nine quality indicators.
An Easy-to-use Real-world Multi-objective Optimization Problem Suite
Authors: Ryoji Tanabe, Hisao Ishibuchi
Date: 2020-09-27 00:00:00

Although synthetic test problems are widely used for the performance assessment of evolutionary multi-objective optimization algorithms, they are likely to include unrealistic properties which may lead to overestimation/underestimation. To address this issue, we present a multi-objective optimization problem suite consisting of 16 bound-constrained real-world problems. The problem suite includes various problems in terms of the number of objectives, the shape of the Pareto front, and the type of design variables. 4 out of the 16 problems are multi-objective mixed-integer optimization problems. We provide Java, C, and Matlab source codes of the 16 problems so that they are available in an off-the-shelf manner. We examine an approximated Pareto front of each test problem. We also analyze the performance of six representative evolutionary multi-objective optimization algorithms on the 16 problems. In addition to the 16 problems, we present 8 constrained multi-objective real-world problems.
Investigating Misinformation in Online Marketplaces: An Audit Study on Amazon
Authors: Eslam Hussein, Hoda Eldardiry
Date: 2020-09-25 00:00:00
Tasks: Misinformation, Recommendation Systems

Search and recommendation systems are ubiquitous and irreplaceable tools in our daily lives. Despite their critical role in selecting and ranking the most relevant information, they typically do not consider the veracity of information presented to the user. In this paper, we introduce an audit methodology to investigate the extent of misinformation presented in search results and recommendations on online marketplaces. We investigate the factors and personalization attributes that influence the amount of misinformation in searches and recommendations. Recently, several media reports criticized Amazon for hosting and recommending items that promote misinformation on topics such as vaccines. Motivated by those reports, we apply our algorithmic auditing methodology on Amazon to verify those claims. Our audit study investigates (a) factors that might influence the search algorithms of Amazon and (b) personalization attributes that contribute to amplifying the amount of misinformation recommended to users in their search results and recommendations. Our audit study collected ~526k search results and ~182k homepage recommendations, with ~8.5k unique items. Each item is annotated for its stance on vaccines' misinformation (pro, neutral, or anti). Our study reveals that (1) the selection and ranking by the default Featured search algorithm of search results that have misinformation stances are positively correlated with the stance of search queries and customers' evaluation of items (ratings and reviews), (2) misinformation stances of search results are neither affected by users' activities nor by interacting (browsing, wish-listing, shopping) with items that have a misinformation stance, and (3) a filter bubble built-in users' homepages have a misinformation stance positively correlated with the misinformation stance of items that a user interacts with.
How do people describe locations during a natural disaster: an analysis of tweets from Hurricane Harvey
Authors: Yingjie Hu, Jimin Wang
Date: 2020-09-27 00:00:00

Social media platforms, such as Twitter, have been increasingly used by people during natural disasters to share information and request for help. Hurricane Harvey was a category 4 hurricane that devastated Houston, Texas, USA in August 2017 and caused catastrophic flooding in the Houston metropolitan area. Hurricane Harvey also witnessed the widespread use of social media by the general public in response to this major disaster, and geographic locations are key information pieces described in many of the social media messages. A geoparsing system, or a geoparser, can be utilized to automatically extract and locate the described locations, which can help first responders reach the people in need. While a number of geoparsers have already been developed, it is unclear how effective they are in recognizing and geo-locating the locations described by people during natural disasters. To fill this gap, this work seeks to understand how people describe locations during a natural disaster by analyzing a sample of tweets posted during Hurricane Harvey. We then identify the limitations of existing geoparsers in processing these tweets, and discuss possible approaches to overcoming these limitations.
An Analysis of the Impact of SEO on University Website Ranking
Authors: Mohammad Javad Shayegan, Maasoumeh Kouhzadi
Date: 2020-09-25 00:00:00

Today, ranking systems in universities have been considered by the academic community, and there is a tight competition between world universities to achieve higher ranks. In the meantime, the ranking of university websites is also in the spotlight, and the Webometric research center announces the ranks of university websites twice a year. Examining university rankings indicators and the Webometric ranks of the university indicates that some of these indicators, directly and indirectly, affect each other. On the other hand, a preliminary study of Webometric indicators shows that some Search Engine Optimization (SEO) indicators can affect Webometric ranks. The purpose of this research is to show how far the SEO metrics can affect the website rank of the university. To do this, after extracting 38 points of the significant SEO metrics of the extracted universities using various tools, data analysis was conducted along with applying association rules on the data. The results of the research show that some of the SEO metrics, such as the number of backlinks, Alexa Rank, and Page Rank have a direct and significant impact on the website rank of universities, and in this regard, interesting rules have been extracted.
Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction
Authors: Wei Li, Ruihan Bao, Keiko Harimoto, Deli Chen, Jingjing Xu and Qi Su
Date: 2020-06-26 00:00:00
Tasks: Air Pollution Prediction, Relation

Stock movement prediction is a hot topic in the Fintech area. Previous works usually predict the price movement in a daily basis, although the market impact of news can be absorbed much shorter, and the exact time is hard to estimate. In this work, we propose a more practical objective to predict the overnight stock movement between the previous close price and the open price. As no trading operation occurs after market close, the market impact of overnight news will be reflected by the overnight movement. One big obstacle for such task is the lacking of data, in this work we collect and publish the overnight stock price movement dataset of Reuters Financial News. Another challenge is that the stocks in the market are not independent, which is omitted by previous works. To make use of the connection among stocks, we propose a LSTM Relational Graph Convolutional Network (LSTM-RGCN) model, which models the connection among stocks with their correlation matrix. Extensive experiment results show that our model outperforms the baseline models. Further analysis shows that the introduction of the graph enables our model to predict the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.
Decontextualized learning for interpretable hierarchical representations of visual patterns
Authors: R. Ian Etheredge, Manfred Schartl, Alex Jordan
Date: 2020-08-31 00:00:00
Tasks: Image Generation, object-detection, Representation Learning

Apart from discriminative models for classification and object detection tasks, the application of deep convolutional neural networks to basic research utilizing natural imaging data has been somewhat limited; particularly in cases where a set of interpretable features for downstream analysis is needed, a key requirement for many scientific investigations. We present an algorithm and training paradigm designed specifically to address this: decontextualized hierarchical representation learning (DHRL). By combining a generative model chaining procedure with a ladder network architecture and latent space regularization for inference, DHRL address the limitations of small datasets and encourages a disentangled set of hierarchically organized features. In addition to providing a tractable path for analyzing complex hierarchal patterns using variation inference, this approach is generative and can be directly combined with empirical and theoretical approaches. To highlight the extensibility and usefulness of DHRL, we demonstrate this method in application to a question from evolutionary biology.
Visual Pivoting for (Unsupervised) Entity Alignment
Authors: Fangyu Liu, Muhao Chen, Dan Roth, Nigel Collier
Date: 2020-09-28 00:00:00
Tasks: Entity Alignment, Knowledge Graphs, Multi-modal Entity Alignment

This work studies the use of visual semantic representations to align entities in heterogeneous knowledge graphs (KGs). Images are natural components of many existing KGs. By combining visual knowledge with other auxiliary information, we show that the proposed new approach, EVA, creates a holistic entity representation that provides strong signals for cross-graph entity alignment. Besides, previous entity alignment methods require human labelled seed alignment, restricting availability. EVA provides a completely unsupervised solution by leveraging the visual similarity of entities to create an initial seed dictionary (visual pivots). Experiments on benchmark data sets DBP15k and DWY15k show that EVA offers state-of-the-art performance on both monolingual and cross-lingual entity alignment tasks. Furthermore, we discover that images are particularly useful to align long-tail KG entities, which inherently lack the structural contexts necessary for capturing the correspondences.
Utterance-level Dialogue Understanding: An Empirical Study
Authors: Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria
Date: 2020-09-29 00:00:00
Tasks: Dialogue Understanding, Goal-Oriented Dialogue Systems, Text Generation

The recent abundance of conversational data on the Web and elsewhere calls for effective NLP systems for dialog understanding. Complete utterance-level understanding often requires context understanding, defined by nearby utterances. In recent years, a number of approaches have been proposed for various utterance-level dialogue understanding tasks. Most of these approaches account for the context for effective understanding. In this paper, we explore and quantify the role of context for different aspects of a dialogue, namely emotion, intent, and dialogue act identification, using state-of-the-art dialog understanding methods as baselines. Specifically, we employ various perturbations to distort the context of a given utterance and study its impact on the different tasks and baselines. This provides us with insights into the fundamental contextual controlling factors of different aspects of a dialogue. Such insights can inspire more effective dialogue understanding models, and provide support for future text generation approaches. The implementation pertaining to this work is available at https://github.com/declare-lab/dialogue-understanding.
Stream-51: Streaming Classification and Novelty Detection from Videos
Authors: Ryne Roady, Tyler L. Hayes, Hitesh Vaidya, Christopher Kanan
Date: 2020-06-14 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Novelty Detection, object-detection, Object Detection

Deep neural networks are popular for visual perception tasks such as image classification and object detection. Once trained and deployed in a real-time environment, these models struggle to identify novel inputs not initially represented in the training distribution. Further, they cannot be easily updated on new information or they will catastrophically forget previously learned knowledge. While there has been much interest in developing models capable of overcoming forgetting, most research has focused on incrementally learning from common image classification datasets broken up into large batches. Online streaming learning is a more realistic paradigm where a model must learn one sample at a time from temporally correlated data streams. Although there are a few datasets designed specifically for this protocol, most have limitations such as few classes or poor image quality. In this work, we introduce Stream-51, a new dataset for streaming classification consisting of temporally correlated images from 51 distinct object categories and additional evaluation classes outside of the training distribution to test novelty recognition. We establish unique evaluation protocols, experimental metrics, and baselines for our dataset in the streaming paradigm.