pwc-archive/papers-with-abstracts huggingface dataset:
Opinion aspect extraction in Dutch childrens diary entries
Authors: Hella Haanstra, Maaike H. T. de Boer
Date: 2019-10-21 00:00:00
Tasks: Aspect Extraction

Aspect extraction can be used in dialogue systems to understand the topic of opinionated text. Expressing an empathetic reaction to an opinion can strengthen the bond between a human and, for example, a robot. The aim of this study is three-fold: 1. create a new annotated dataset for both aspect extraction and opinion words for Dutch childrens language, 2. acquire aspect extraction results for this task and 3. improve current results for aspect extraction in Dutch reviews. This was done by training a deep learning Gated Recurrent Unit (GRU) model, originally developed for an English review dataset, on Dutch restaurant review data to classify both opinion words and their respective aspects. We obtained state-of-the-art performance on the Dutch restaurant review dataset. Additionally, we acquired aspect extraction results for the Dutch childrens dataset. Since the model was trained on standardised language, these results are quite promising.
XL-Editor: Post-editing Sentences with XLNet
Authors: Yong-Siang Shih, Wei-Cheng Chang, Yiming Yang
Date: 2019-10-19 00:00:00
Tasks: Sentence, Style Transfer, Text Style Transfer

While neural sequence generation models achieve initial success for many NLP applications, the canonical decoding procedure with left-to-right generation order (i.e., autoregressive) in one-pass can not reflect the true nature of human revising a sentence to obtain a refined result. In this work, we propose XL-Editor, a novel training framework that enables state-of-the-art generalized autoregressive pretraining methods, XLNet specifically, to revise a given sentence by the variable-length insertion probability. Concretely, XL-Editor can (1) estimate the probability of inserting a variable-length sequence into a specific position of a given sentence; (2) execute post-editing operations such as insertion, deletion, and replacement based on the estimated variable-length insertion probability; (3) complement existing sequence-to-sequence models to refine the generated sequences. Empirically, we first demonstrate better post-editing capabilities of XL-Editor over XLNet on the text insertion and deletion tasks, which validates the effectiveness of our proposed framework. Furthermore, we extend XL-Editor to the unpaired text style transfer task, where transferring the target style onto a given sentence can be naturally viewed as post-editing the sentence into the target style. XL-Editor achieves significant improvement in style transfer accuracy and also maintains coherent semantic of the original sentence, showing the broad applicability of our method.
Estimator Vectors: OOV Word Embeddings based on Subword and Context Clue Estimates
Authors: Raj Patel, Carlotta Domeniconi
Date: 2019-10-18 00:00:00
Tasks: Word Embeddings

Semantic representations of words have been successfully extracted from unlabeled corpuses using neural network models like word2vec. These representations are generally high quality and are computationally inexpensive to train, making them popular. However, these approaches generally fail to approximate out of vocabulary (OOV) words, a task humans can do quite easily, using word roots and context clues. This paper proposes a neural network model that learns high quality word representations, subword representations, and context clue representations jointly. Learning all three types of representations together enhances the learning of each, leading to enriched word vectors, along with strong estimates for OOV words, via the combination of the corresponding context clue and subword embeddings. Our model, called Estimator Vectors (EV), learns strong word embeddings and is competitive with state of the art methods for OOV estimation.
Question Classification with Deep Contextualized Transformer
Authors: Haozheng Luo, Ningwei Liu, Charles Feng
Date: 2019-10-17 00:00:00
Tasks: Classification, General Classification

The latest work for Question and Answer problems is to use the Stanford Parse Tree. We build on prior work and develop a new method to handle the Question and Answer problem with the Deep Contextualized Transformer to manage some aberrant expressions. We also conduct extensive evaluations of the SQuAD and SwDA dataset and show significant improvement over QA problem classification of industry needs. We also investigate the impact of different models for the accuracy and efficiency of the problem answers. It shows that our new method is more effective for solving QA problems with higher accuracy
Fully Quantized Transformer for Machine Translation
Authors: Gabriele Prato, Ella Charlaix, Mehdi Rezagholizadeh
Date: 2019-10-17 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Machine Translation, Quantization, Translation

State-of-the-art neural machine translation methods employ massive amounts of parameters. Drastically reducing computational costs of such methods without affecting performance has been up to this point unsuccessful. To this end, we propose FullyQT: an all-inclusive quantization strategy for the Transformer. To the best of our knowledge, we are the first to show that it is possible to avoid any loss in translation quality with a fully quantized Transformer. Indeed, compared to full-precision, our 8-bit models score greater or equal BLEU on most tasks. Comparing ourselves to all previously proposed methods, we achieve state-of-the-art quantization results.
Does Gender Matter? Towards Fairness in Dialogue Systems
Authors: Haochen Liu, Jamell Dacon, Wenqi Fan, Hui Liu, Zitao Liu, Jiliang Tang
Date: 2019-10-16 00:00:00
Proceeding: COLING 2020 8
Tasks: Fairness

Recently there are increasing concerns about the fairness of Artificial Intelligence (AI) in real-world applications such as computer vision and recommendations. For example, recognition algorithms in computer vision are unfair to black people such as poorly detecting their faces and inappropriately identifying them as "gorillas". As one crucial application of AI, dialogue systems have been extensively applied in our society. They are usually built with real human conversational data; thus they could inherit some fairness issues which are held in the real world. However, the fairness of dialogue systems has not been well investigated. In this paper, we perform a pioneering study about the fairness issues in dialogue systems. In particular, we construct a benchmark dataset and propose quantitative measures to understand fairness in dialogue models. Our studies demonstrate that popular dialogue models show significant prejudice towards different genders and races. Besides, to mitigate the bias in dialogue systems, we propose two simple but effective debiasing methods. Experiments show that our methods can reduce the bias in dialogue systems significantly. The dataset and the implementation are released to foster fairness research in dialogue systems.
Memory-Augmented Recurrent Networks for Dialogue Coherence
Authors: David Donahue, Yuanliang Meng, Anna Rumshisky
Date: 2019-10-16 00:00:00
Tasks: Language Modeling, Language Modelling

Recent dialogue approaches operate by reading each word in a conversation history, and aggregating accrued dialogue information into a single state. This fixed-size vector is not expandable and must maintain a consistent format over time. Other recent approaches exploit an attention mechanism to extract useful information from past conversational utterances, but this introduces an increased computational complexity. In this work, we explore the use of the Neural Turing Machine (NTM) to provide a more permanent and flexible storage mechanism for maintaining dialogue coherence. Specifically, we introduce two separate dialogue architectures based on this NTM design. The first design features a sequence-to-sequence architecture with two separate NTM modules, one for each participant in the conversation. The second memory architecture incorporates a single NTM module, which stores parallel context information for both speakers. This second design also replaces the sequence-to-sequence architecture with a neural language model, to allow for longer context of the NTM and greater understanding of the dialogue history. We report perplexity performance for both models, and compare them to existing baselines.
Injecting Hierarchy with U-Net Transformers
Authors: David Donahue, Vladislav Lialin, Anna Rumshisky
Date: 2019-10-16 00:00:00
Tasks: Machine Translation

The Transformer architecture has become increasingly popular over the past two years, owing to its impressive performance on a number of natural language processing (NLP) tasks. However, all Transformer computations occur at the level of word representations and therefore, it may be argued that Transformer models do not explicitly attempt to learn hierarchical structure which is widely assumed to be integral to language. In the present work, we introduce hierarchical processing into the Transformer model, taking inspiration from the U-Net architecture, popular in computer vision for its hierarchical view of natural images. We empirically demonstrate that the proposed architecture outperforms both the vanilla Transformer and some strong baselines in the domain of chit-chat dialogue.
Automated Text Summarization for the Enhancement of Public Services
Authors: Xingbang Liu, Janyl Jumadinova
Date: 2019-10-16 00:00:00
Tasks: BIG-bench Machine Learning, Text Summarization

Natural language processing and machine learning algorithms have been shown to be effective in a variety of applications. In this work, we contribute to the area of AI adoption in the public sector. We present an automated system that was used to process textual information, generate important keywords, and automatically summarize key elements of the Meadville community statements. We also describe the process of collaboration with My Meadville administrators during the development of our system. My Meadville, a community initiative, supported by the city of Meadville conducted a large number of interviews with the residents of Meadville during the community events and transcribed these interviews into textual data files. Their goal was to uncover the issues of importance to the Meadville residents in an attempt to enhance public services. Our AI system cleans and pre-processes the interview data, then using machine learning algorithms it finds important keywords and key excerpts from each interview. It also provides searching functionality to find excerpts from relevant interviews based on specific keywords. Our automated system allowed the city to save over 300 hours of human labor that would have taken to read all interviews and highlight important points. Our findings are being used by My Meadville initiative to locate important information from the collected data set for ongoing community enhancement projects, to highlight relevant community assets, and to assist in identifying the steps to be taken based on the concerns and areas of improvement identified by the community members.
DATA Agent
Authors: Michael Cerny Green, Gabriella A. B. Barros, Antonios Liapis, Julian Togelius
Date: 2018-09-28 00:00:00
Tasks: Game Design

This paper introduces DATA Agent, a system which creates murder mystery adventures from open data. In the game, the player takes on the role of a detective tasked with finding the culprit of a murder. All characters, places, and items in DATA Agent games are generated using open data as source content. The paper discusses the general game design and user interface of DATA Agent, and provides details on the generative algorithms which transform linked data into different game objects. Findings from a user study with 30 participants playing through two games of DATA Agent show that the game is easy and fun to play, and that the mysteries it generates are straightforward to solve.
In Silico Prediction of Cell Traction Forces
Authors: Nicolas Pielawski, Jianjiang Hu, Staffan Strömblad, Carolina Wählby
Date: 2019-10-16 00:00:00
Tasks: Prediction

Traction Force Microscopy (TFM) is a technique used to determine the tensions that a biological cell conveys to the underlying surface. Typically, TFM requires culturing cells on gels with fluorescent beads, followed by bead displacement calculations. We present a new method allowing to predict those forces from a regular fluorescent image of the cell. Using Deep Learning, we trained a Bayesian Neural Network adapted for pixel regression of the forces and show that it generalises on different cells of the same strain. The predicted forces are computed along with an approximated uncertainty, which shows whether the prediction is trustworthy or not. Using the proposed method could help estimating forces when calculating non-trivial bead displacements and can also free one of the fluorescent channels of the microscope. Code is available at \url{https://github.com/wahlby-lab/InSilicoTFM}.
Octave Deep Plane-Sweeping Network: Reducing Spatial Redundancy for Learning-Based Plane-Sweeping Stereo
Authors: R. Komatsu, H. Fujii, Y. Tamura, A. Yamashita, H. Asama
Date: 2019-10-14 00:00:00
Proceeding: IEEE Access 2019 10
Tasks: Depth Estimation, GPU, Stereo Depth Estimation

In this paper, we propose the octave deep plane-sweeping network (OctDPSNet). OctDPSNet is a novel learning-based plane-sweeping stereo, which drastically reduces the required GPU memory and computation time while achieving a state-of-the-art depth estimation accuracy. Inspired by octave convolution, we divide image features into high and low spatial frequency features, and two cost volumes are generated from these using our proposed plane-sweeping module. To reduce spatial redundancy, the resolution of the cost volume from the low spatial frequency features is set to half that of the high spatial frequency features, which enables the memory consumption and computational cost to be reduced. After refinement, the two cost volumes are integrated into a final cost volume through our proposed pixel-wise “squeeze-and-excitation” based attention mechanism, and the depth maps are estimated from the final cost volume. We evaluate the proposed model on five datasets: SUN3D, RGB-D SLAM, MVS, Scenes11, and ETH3D. Our model outperforms previous methods on five datasets while drastically reducing the memory consumption and computational cost. Our source code is available at https://github.com/matsuren/octDPSNet.
LFRT, A MATLAB Toolbox for Load Forecast & Assesment of Additional Capacity for an Electrical Power System
Authors: Hazoor Ahmad, Muhammad Tariq, Awais Qarni, Intisar Ali Sajjad
Date: 2016-04-12 00:00:00
Proceeding: Conference 2016 4
Tasks: Load Forecasting

A developing country like Pakistan with sizable
pressure on their limited financial resources can ill afford
either of these two situations about energy forecast: 1) Too
optimistic 2) Too conservative. Unlike all other field of
research, load forecasting and reliability fields were lagging
such a tool that can provide a juncture of all the function of
load forecast and reliability on a single platform. Before the
evolution of LOLP assessments of additional capacity of a
power generating system was a big problem with different
inaccurate solutions. This paper presents LFRT, a MATLAB
Toolbox that contains set of routines to calculate the Load
Forecast for medium term and long term, Capacity outage
rates, capacity outage probability, and LOLP and LOLE, in
case of unit’s availability-unavailability, load forecast
uncertainty and addition or removal of units. Along with
several other examples, LFRT is employed to solve for the
assessment of additional capacity of hypothetical model of
Tarbela hydro power plant after the installation of Tarbela-IV
Extension Project.
Guided Image-to-Image Translation with Bi-Directional Feature Transformation
Authors: Badour AlBahar, Jia-Bin Huang
Date: 2019-10-24 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image-to-Image Translation, Pose Transfer, Translation

We address the problem of guided image-to-image translation where we translate an input image into another while respecting the constraints provided by an external, user-provided guidance image. Various conditioning methods for leveraging the given guidance image have been explored, including input concatenation , feature concatenation, and conditional affine transformation of feature activations. All these conditioning mechanisms, however, are uni-directional, i.e., no information flow from the input image back to the guidance. To better utilize the constraints of the guidance image, we present a bi-directional feature transformation (bFT) scheme. We show that our bFT scheme outperforms other conditioning schemes and has comparable results to state-of-the-art methods on different tasks.
TexturePose: Supervising Human Mesh Estimation with Texture Consistency
Authors: Georgios Pavlakos, Nikos Kolotouros, Kostas Daniilidis
Date: 2019-10-24 00:00:00
Proceeding: ICCV 2019 10
Tasks: Pose Estimation, Weakly-supervised 3D Human Pose Estimation

This work addresses the problem of model-based human pose estimation. Recent approaches have made significant progress towards regressing the parameters of parametric human body models directly from images. Because of the absence of images with 3D shape ground truth, relevant approaches rely on 2D annotations or sophisticated architecture designs. In this work, we advocate that there are more cues we can leverage, which are available for free in natural images, i.e., without getting more annotations, or modifying the network architecture. We propose a natural form of supervision, that capitalizes on the appearance constancy of a person among different frames (or viewpoints). This seemingly insignificant and often overlooked cue goes a long way for model-based pose estimation. The parametric model we employ allows us to compute a texture map for each frame. Assuming that the texture of the person does not change dramatically between frames, we can apply a novel texture consistency loss, which enforces that each point in the texture map has the same texture value across all frames. Since the texture is transferred in this common texture map space, no camera motion computation is necessary, or even an assumption of smoothness among frames. This makes our proposed supervision applicable in a variety of settings, ranging from monocular video, to multi-view images. We benchmark our approach against strong baselines that require the same or even more annotations that we do and we consistently outperform them. Simultaneously, we achieve state-of-the-art results among model-based pose estimation approaches in different benchmarks. The project website with videos, results, and code can be found at https://seas.upenn.edu/~pavlakos/projects/texturepose.
Progressive Domain Adaptation for Object Detection
Authors: Han-Kai Hsu, Chun-Han Yao, Yi-Hsuan Tsai, Wei-Chih Hung, Hung-Yu Tseng, Maneesh Singh, Ming-Hsuan Yang
Date: 2019-10-24 00:00:00
Tasks: Domain Adaptation, Object, object-detection, Object Detection

Recent deep learning methods for object detection rely on a large amount of bounding box annotations. Collecting these annotations is laborious and costly, yet supervised models do not generalize well when testing on images from a different distribution. Domain adaptation provides a solution by adapting existing labels to the target testing data. However, a large gap between domains could make adaptation a challenging task, which leads to unstable training processes and sub-optimal results. In this paper, we propose to bridge the domain gap with an intermediate domain and progressively solve easier adaptation subtasks. This intermediate domain is constructed by translating the source images to mimic the ones in the target domain. To tackle the domain-shift problem, we adopt adversarial learning to align distributions at the feature level. In addition, a weighted task loss is applied to deal with unbalanced image quality in the intermediate domain. Experimental results show that our method performs favorably against the state-of-the-art method in terms of the performance on the target domain.
Community-Level Anomaly Detection for Anti-Money Laundering
Authors: Andra Baltoiu, Andrei Patrascu, Paul Irofti
Date: 2019-10-24 00:00:00
Tasks: Anomaly Detection, Dictionary Learning, Specificity

Anomaly detection in networks often boils down to identifying an underlying graph structure on which the abnormal occurrence rests on. Financial fraud schemes are one such example, where more or less intricate schemes are employed in order to elude transaction security protocols. We investigate the problem of learning graph structure representations using adaptations of dictionary learning aimed at encoding connectivity patterns. In particular, we adapt dictionary learning strategies to the specificity of network topologies and propose new methods that impose Laplacian structure on the dictionaries themselves. In one adaption we focus on classifying topologies by working directly on the graph Laplacian and cast the learning problem to accommodate its 2D structure. We tackle the same problem by learning dictionaries which consist of vectorized atomic Laplacians, and provide a block coordinate descent scheme to solve the new dictionary learning formulation. Imposing Laplacian structure on the dictionaries is also proposed in an adaptation of the Single Block Orthogonal learning method. Results on synthetic graph datasets comprising different graph topologies confirm the potential of dictionaries to directly represent graph structure information.
Controllable Attention for Structured Layered Video Decomposition
Authors: Jean-Baptiste Alayrac, João Carreira, Relja Arandjelović, Andrew Zisserman
Date: 2019-10-24 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Recognition, Reflection Removal

The objective of this paper is to be able to separate a video into its natural layers, and to control which of the separated layers to attend to. For example, to be able to separate reflections, transparency or object motion. We make the following three contributions: (i) we introduce a new structured neural network architecture that explicitly incorporates layers (as spatial masks) into its design. This improves separation performance over previous general purpose networks for this task; (ii) we demonstrate that we can augment the architecture to leverage external cues such as audio for controllability and to help disambiguation; and (iii) we experimentally demonstrate the effectiveness of our approach and training procedure with controlled experiments while also showing that the proposed model can be successfully applied to real-word applications such as reflection removal and action recognition in cluttered scenes.
Cross-Lingual Vision-Language Navigation
Authors: An Yan, Xin Eric Wang, Jiangtao Feng, Lei LI, William Yang Wang
Date: 2019-10-24 00:00:00
Tasks: Domain Adaptation, Navigate, Vision-Language Navigation, Zero-Shot Learning

Commanding a robot to navigate with natural language instructions is a long-term goal for grounded language understanding and robotics. But the dominant language is English, according to previous studies on vision-language navigation (VLN). To go beyond English and serve people speaking different languages, we collect a bilingual Room-to-Room (BL-R2R) dataset, extending the original benchmark with new Chinese instructions. Based on this newly introduced dataset, we study how an agent can be trained on existing English instructions but navigate effectively with another language under a zero-shot learning scenario. Without any training data of the target language, our model shows competitive results even compared to a model with full access to the target language training data. Moreover, we investigate the transferring ability of our model when given a certain amount of target language training data.