pwc-archive/papers-with-abstracts huggingface dataset:
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
Authors: Sahin Cem Geyik, Stuart Ambler, Krishnaram Kenthapadi
Date: 2019-04-30 00:00:00
Tasks: Fairness, Recommendation Systems, Re-Ranking

We present a framework for quantifying and mitigating algorithmic bias in mechanisms designed for ranking individuals, typically used as part of web-scale search and recommendation systems. We first propose complementary measures to quantify bias with respect to protected attributes such as gender and age. We then present algorithms for computing fairness-aware re-ranking of results. For a given search or recommendation task, our algorithms seek to achieve a desired distribution of top ranked results with respect to one or more protected attributes. We show that such a framework can be tailored to achieve fairness criteria such as equality of opportunity and demographic parity depending on the choice of the desired distribution. We evaluate the proposed algorithms via extensive simulations over different parameter choices, and study the effect of fairness-aware ranking on both bias and utility measures. We finally present the online A/B testing results from applying our framework towards representative ranking in LinkedIn Talent Search, and discuss the lessons learned in practice. Our approach resulted in tremendous improvement in the fairness metrics (nearly three fold increase in the number of search queries with representative results) without affecting the business metrics, which paved the way for deployment to 100% of LinkedIn Recruiter users worldwide. Ours is the first large-scale deployed framework for ensuring fairness in the hiring domain, with the potential positive impact for more than 630M LinkedIn members.
Digitally Capturing Physical Prototypes During Early-Stage Engineering Design Projects for Initial Analysis of Project Output and Progression
Authors: Jorgen F. Erichsen, Heikki Sjöman, Martin Steinert, Torgeir Welo
Date: 2019-04-26 00:00:00

Aiming to help researchers capture output from the early stages of engineering design projects, this article presents a new research tool for digitally capturing physical prototypes. The motivation for this work is to collect observations that can aid in understanding prototyping in the early stages of engineering design projects, and this article investigates if and how digital capture of physical prototypes can be used for this purpose. Early-stage prototypes are usually rough and of low-fidelity and are thus often discarded or substantially modified through the projects. Hence, retrospective access to prototypes is a challenge when trying to gather accurate empirical data. To capture the prototypes developed through the early stages of a project, a new research tool has been developed for capturing prototypes through multi-view images, along with metadata describing by whom, why, when and where the prototypes were captured. Over the course of 17 months, this research tool has been used to capture more than 800 physical prototypes from 76 individual users across many projects. In this article, one project is shown in detail to demonstrate how this capturing system can gather empirical data for enriching engineering design project cases that focus on prototyping for concept generation. The authors also analyse the metadata provided by the system to give understanding into prototyping patterns in the projects. Lastly, through enabling digital capture of large quantities of data, the research tool presents the foundations for training artificial intelligence-based predictors and classifiers that can be used for analysis in engineering design research.
Accurate and Fast reconstruction of Porous Media from Extremely Limited Information Using Conditional Generative Adversarial Network
Authors: Junxi Feng, Xiaohai He, Qizhi Teng, Chao Ren, Honggang Chen, Yang Li
Date: 2019-04-04 00:00:00
Tasks: Generative Adversarial Network, Image Reconstruction

Porous media are ubiquitous in both nature and engineering applications, thus
their modelling and understanding is of vital importance. In contrast to direct
acquisition of three-dimensional (3D) images of such medium, obtaining its
sub-region (s) like two-dimensional (2D) images or several small areas could be
much feasible. Therefore, reconstructing whole images from the limited
information is a primary technique in such cases. Specially, in practice the
given data cannot generally be determined by users and may be incomplete or
partially informed, thus making existing reconstruction methods inaccurate or
even ineffective. To overcome this shortcoming, in this study we proposed a
deep learning-based framework for reconstructing full image from its much
smaller sub-area(s). Particularly, conditional generative adversarial network
(CGAN) is utilized to learn the mapping between input (partial image) and
output (full image). To preserve the reconstruction accuracy, two simple but
effective objective functions are proposed and then coupled with the other two
functions to jointly constrain the training procedure. Due to the inherent
essence of this ill-posed problem, a Gaussian noise is introduced for producing
reconstruction diversity, thus allowing for providing multiple candidate
outputs. Extensively tested on a variety of porous materials and demonstrated
by both visual inspection and quantitative comparison, the method is shown to
be accurate, stable yet fast ($\sim0.08s$ for a $128 \times 128$ image
reconstruction). We highlight that the proposed approach can be readily
extended, such as incorporating any user-define conditional data and an
arbitrary number of object functions into reconstruction, and being coupled
with other reconstruction methods.
Teaching on a Budget in Multi-Agent Deep Reinforcement Learning
Authors: Ercüment İlhan, Jeremy Gow, Diego Perez-Liebana
Date: 2019-04-19 00:00:00
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning (RL) algorithms can solve complex sequential decision tasks successfully. However, they have a major drawback of having poor sample efficiency which can often be tackled by knowledge reuse. In Multi-Agent Reinforcement Learning (MARL) this drawback becomes worse, but at the same time, a new set of opportunities to leverage knowledge are also presented through agent interactions. One promising approach among these is peer-to-peer action advising through a teacher-student framework. Despite being introduced for single-agent RL originally, recent studies show that it can also be applied to multi-agent scenarios with promising empirical results. However, studies in this line of research are currently very limited. In this paper, we propose heuristics-based action advising techniques in cooperative decentralised MARL, using a nonlinear function approximation based task-level policy. By adopting Random Network Distillation technique, we devise a measurement for agents to assess their knowledge in any given state and be able to initiate the teacher-student dynamics with no prior role assumptions. Experimental results in a gridworld environment show that such an approach may indeed be useful and needs to be further investigated.
Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition
Authors: Chao Gao, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
Date: 2019-04-20 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

The Pommerman Team Environment is a recently proposed benchmark which
involves a multi-agent domain with challenges such as partial observability,
decentralized execution (without communication), and very sparse and delayed
rewards. The inaugural Pommerman Team Competition held at NeurIPS 2018 hosted
25 participants who submitted a team of 2 agents. Our submission
nn_team_skynet955_skynet955 won 2nd place of the "learning agents'' category.
Our team is composed of 2 neural networks trained with state of the art deep
reinforcement learning algorithms and makes use of concepts like reward
shaping, curriculum learning, and an automatic reasoning module for action
pruning. Here, we describe these elements and additionally we present a
collection of open-sourced agents that can be used for training and testing in
the Pommerman environment. Code available at:
https://github.com/BorealisAI/pommerman-baseline
M2H-GAN: A GAN-based Mapping from Machine to Human Transcripts for Speech Understanding
Authors: Titouan Parcollet, Mohamed Morchid, Xavier Bost, Georges Linarès
Date: 2019-04-13 00:00:00
Tasks: Generative Adversarial Network, Spoken Language Understanding

Deep learning is at the core of recent spoken language understanding (SLU)
related tasks. More precisely, deep neural networks (DNNs) drastically
increased the performances of SLU systems, and numerous architectures have been
proposed. In the real-life context of theme identification of telephone
conversations, it is common to hold both a human, manual (TRS) and an
automatically transcribed (ASR) versions of the conversations. Nonetheless, and
due to production constraints, only the ASR transcripts are considered to build
automatic classifiers. TRS transcripts are only used to measure the
performances of ASR systems. Moreover, the recent performances in term of
classification accuracy, obtained by DNN related systems are close to the
performances reached by humans, and it becomes difficult to further increase
the performances by only considering the ASR transcripts. This paper proposes
to distillates the TRS knowledge available during the training phase within the
ASR representation, by using a new generative adversarial network called
M2H-GAN to generate a TRS-like version of an ASR document, to improve the theme
identification performances.
Text2Node: a Cross-Domain System for Mapping Arbitrary Phrases to a Taxonomy
Authors: Rohollah Soltani, Alexandre Tomberg
Date: 2019-04-11 00:00:00
Tasks: Word Embeddings

Electronic health record (EHR) systems are used extensively throughout the
healthcare domain. However, data interchangeability between EHR systems is
limited due to the use of different coding standards across systems. Existing
methods of mapping coding standards based on manual human experts mapping,
dictionary mapping, symbolic NLP and classification are unscalable and cannot
accommodate large scale EHR datasets.
  In this work, we present Text2Node, a cross-domain mapping system capable of
mapping medical phrases to concepts in a large taxonomy (such as SNOMED CT).
The system is designed to generalize from a limited set of training samples and
map phrases to elements of the taxonomy that are not covered by training data.
As a result, our system is scalable, robust to wording variants between coding
systems and can output highly relevant concepts when no exact concept exists in
the target taxonomy. Text2Node operates in three main stages: first, the
lexicon is mapped to word embeddings; second, the taxonomy is vectorized using
node embeddings; and finally, the mapping function is trained to connect the
two embedding spaces. We compared multiple algorithms and architectures for
each stage of the training, including GloVe and FastText word embeddings, CNN
and Bi-LSTM mapping functions, and node2vec for node embeddings. We confirmed
the robustness and generalisation properties of Text2Node by mapping ICD-9-CM
Diagnosis phrases to SNOMED CT and by zero-shot training at comparable
accuracy.
  This system is a novel methodological contribution to the task of normalizing
and linking phrases to a taxonomy, advancing data interchangeability in
healthcare. When applied, the system can use electronic health records to
generate an embedding that incorporates taxonomical medical knowledge to
improve clinical predictive models.
Relation Discovery with Out-of-Relation Knowledge Base as Supervision
Authors: Yan Liang, Xin Liu, Jianwen Zhang, Yangqiu Song
Date: 2019-04-19 00:00:00
Proceeding: NAACL 2019 6
Tasks: Relation

Unsupervised relation discovery aims to discover new relations from a given
text corpus without annotated data. However, it does not consider existing
human annotated knowledge bases even when they are relevant to the relations to
be discovered. In this paper, we study the problem of how to use
out-of-relation knowledge bases to supervise the discovery of unseen relations,
where out-of-relation means that relations to discover from the text corpus and
those in knowledge bases are not overlapped. We construct a set of constraints
between entity pairs based on the knowledge base embedding and then incorporate
constraints into the relation discovery by a variational auto-encoder based
algorithm. Experiments show that our new approach can improve the
state-of-the-art relation discovery performance by a large margin.
Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector
Authors: Mehdi Drissi, Pedro Sandoval, Vivaswat Ojha, Julie Medero
Date: 2019-04-10 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Articles

We investigate the recently developed Bidirectional Encoder Representations
from Transformers (BERT) model for the hyperpartisan news detection task. Using
a subset of hand-labeled articles from SemEval as a validation set, we test the
performance of different parameters for BERT models. We find that accuracy from
two different BERT models using different proportions of the articles is
consistently high, with our best-performing model on the validation set
achieving 85% accuracy and the best-performing model on the test set achieving
77%. We further determined that our model exhibits strong consistency, labeling
independent slices of the same article identically. Finally, we find that
randomizing the order of word pieces dramatically reduces validation accuracy
(to approximately 60%), but that shuffling groups of four or more word pieces
maintains an accuracy of about 80%, indicating the model mainly gains value
from local context.
Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization
Authors: Junxin Liu, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie
Date: 2019-04-26 00:00:00
Tasks: Chinese Word Segmentation, Segmentation

Existing methods for CWS usually rely on a large number of labeled sentences
to train word segmentation models, which are expensive and time-consuming to
annotate. Luckily, the unlabeled data is usually easy to collect and many
high-quality Chinese lexicons are off-the-shelf, both of which can provide
useful information for CWS. In this paper, we propose a neural approach for
Chinese word segmentation which can exploit both lexicon and unlabeled data.
Our approach is based on a variant of posterior regularization algorithm, and
the unlabeled data and lexicon are incorporated into model training as indirect
supervision by regularizing the prediction space of CWS models. Extensive
experiments on multiple benchmark datasets in both in-domain and cross-domain
scenarios validate the effectiveness of our approach.
Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation
Authors: Fangzhao Wu, Junxin Liu, Chuhan Wu, Yongfeng Huang, Xing Xie
Date: 2019-04-26 00:00:00
Tasks: Chinese Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Chinese named entity recognition (CNER) is an important task in Chinese
natural language processing field. However, CNER is very challenging since
Chinese entity names are highly context-dependent. In addition, Chinese texts
lack delimiters to separate words, making it difficult to identify the boundary
of entities. Besides, the training data for CNER in many domains is usually
insufficient, and annotating enough training data for CNER is very expensive
and time-consuming. In this paper, we propose a neural approach for CNER.
First, we introduce a CNN-LSTM-CRF neural architecture to capture both local
and long-distance contexts for CNER. Second, we propose a unified framework to
jointly train CNER and word segmentation models in order to enhance the ability
of CNER model in identifying entity boundaries. Third, we introduce an
automatic method to generate pseudo labeled samples from existing labeled data
which can enrich the training data. Experiments on two benchmark datasets show
that our approach can effectively improve the performance of Chinese named
entity recognition, especially when training data is insufficient.
Arabic Text Diacritization Using Deep Neural Networks
Authors: Ali Fadel, Ibraheem Tuffaha, Bara' Al-Jawarneh, Mahmoud Al-Ayyoub
Date: 2019-04-25 00:00:00
Tasks: Arabic Text Diacritization

Diacritization of Arabic text is both an interesting and a challenging
problem at the same time with various applications ranging from speech
synthesis to helping students learning the Arabic language. Like many other
tasks or problems in Arabic language processing, the weak efforts invested into
this problem and the lack of available (open-source) resources hinder the
progress towards solving this problem. This work provides a critical review for
the currently existing systems, measures and resources for Arabic text
diacritization. Moreover, it introduces a much-needed free-for-all cleaned
dataset that can be easily used to benchmark any work on Arabic diacritization.
Extracted from the Tashkeela Corpus, the dataset consists of 55K lines
containing about 2.3M words. After constructing the dataset, existing tools and
systems are tested on it. The results of the experiments show that the neural
Shakkala system significantly outperforms traditional rule-based approaches and
other closed-source tools with a Diacritic Error Rate (DER) of 2.88% compared
with 13.78%, which the best DER for the non-neural approach (obtained by the
Mishkal tool).
An Evaluation of Transfer Learning for Classifying Sales Engagement Emails at Large Scale
Authors: Yong Liu, Pavel Dmitriev, Yifei HUANG, Andrew Brooks, Li Dong
Date: 2019-04-19 00:00:00
Tasks: GPU, Language Modelling, Transfer Learning

This paper conducts an empirical investigation to evaluate transfer learning
for classifying sales engagement emails arising from digital sales engagement
platforms. Given the complexity of content and context of sales engagement,
lack of standardized large corpora and benchmarks, limited labeled examples and
heterogenous context of intent, this real-world use case poses both a challenge
and an opportunity for adopting a transfer learning approach. We propose an
evaluation framework to assess a high performance transfer learning (HPTL)
approach in three key areas in addition to commonly used accuracy metrics: 1)
effective embeddings and pretrained language model usage, 2) minimum labeled
samples requirement and 3) transfer learning implementation strategies. We use
in-house sales engagement email samples as the experiment dataset, which
includes over 3000 emails labeled as positive, objection, unsubscribe, or
not-sure. We discuss our findings on evaluating BERT, ELMo, Flair and GloVe
embeddings with both feature-based and fine-tuning approaches and their
scalability on a GPU cluster with increasingly larger labeled samples. Our
results show that fine-tuning of the BERT model outperforms with as few as 300
labeled samples, but underperforms with fewer than 300 labeled samples,
relative to all the feature-based approaches using different embeddings.
A Self-Attentive Emotion Recognition Network
Authors: Harris Partaourides, Kostantinos Papadamou, Nicolas Kourtellis, Ilias Leontiadis, Sotirios Chatzis
Date: 2019-04-24 00:00:00
Tasks: Decoder, Emotion Recognition

Modern deep learning approaches have achieved groundbreaking performance in
modeling and classifying sequential data. Specifically, attention networks
constitute the state-of-the-art paradigm for capturing long temporal dynamics.
This paper examines the efficacy of this paradigm in the challenging task of
emotion recognition in dyadic conversations. In contrast to existing
approaches, our work introduces a novel attention mechanism capable of
inferring the immensity of the effect of each past utterance on the current
speaker emotional state. The proposed attention mechanism performs this
inference procedure without the need of a decoder network; this is achieved by
means of innovative self-attention arguments. Our self-attention networks
capture the correlation patterns among consecutive encoder network states, thus
allowing to robustly and effectively model temporal dynamics over arbitrary
long temporal horizons. Thus, we enable capturing strong affective patterns
over the course of long discussions. We exhibit the effectiveness of our
approach considering the challenging IEMOCAP benchmark. As we show, our devised
methodology outperforms state-of-the-art alternatives and commonly used
approaches, giving rise to promising new research directions in the context of
Online Social Network (OSN) analysis tasks.
Who wrote this book? A challenge for e-commerce
Authors: Béranger Dumont, Simona Maggio, Ghiles Sidi Said, Quoc-Tien Au
Date: 2019-04-19 00:00:00
Proceeding: WS 2019 11

Modern e-commerce catalogs contain millions of references, associated with
textual and visual information that is of paramount importance for the products
to be found via search or browsing. Of particular significance is the book
category, where the author name(s) field poses a significant challenge. Indeed,
books written by a given author (such as F. Scott Fitzgerald) might be listed
with different authors' names in a catalog due to abbreviations and spelling
variants and mistakes, among others. To solve this problem at scale, we design
a composite system involving open data sources for books as well as machine
learning components leveraging deep learning-based techniques for natural
language processing. In particular, we use Siamese neural networks for an
approximate match with known author names, and direct correction of the
provided author's name using sequence-to-sequence learning with neural
networks. We evaluate this approach on product data from the e-commerce website
Rakuten France, and find that the top proposal of the system is the normalized
author name with 72% accuracy.
A Content-Based Approach to Email Triage Action Prediction: Exploration and Evaluation
Authors: Sudipto Mukherjee, Ke Jiang
Date: 2019-04-30 00:00:00

Email has remained a principal form of communication among people, both in
enterprise and social settings. With a deluge of emails crowding our mailboxes
daily, there is a dire need of smart email systems that can recover important
emails and make personalized recommendations. In this work, we study the
problem of predicting user triage actions to incoming emails where we take the
reply prediction as a working example. Different from existing methods, we
formulate the triage action prediction as a recommendation problem and focus on
the content-based approach, where the users are represented using the content
of current and past emails. We also introduce additional similarity features to
further explore the affinities between users and emails. Experiments on the
publicly available Avocado email collection demonstrate the advantages of our
proposed recommendation framework and our method is able to achieve better
performance compared to the state-of-the-art deep recommendation methods. More
importantly, we provide valuable insight into the effectiveness of different
textual and user representations and show that traditional bag-of-words
approaches, with the help from the similarity features, compete favorably with
the more advanced neural embedding methods.