pwc-archive/papers-with-abstracts huggingface dataset:
High-confidence error estimates for learned value functions
Authors: Touqir Sajed, Wesley Chung, Martha White
Date: 2018-08-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Vocal Bursts Intensity Prediction

Estimating the value function for a fixed policy is a fundamental problem in
reinforcement learning. Policy evaluation algorithms---to estimate value
functions---continue to be developed, to improve convergence rates, improve
stability and handle variability, particularly for off-policy learning. To
understand the properties of these algorithms, the experimenter needs
high-confidence estimates of the accuracy of the learned value functions. For
environments with small, finite state-spaces, like chains, the true value
function can be easily computed, to compute accuracy. For large, or continuous
state-spaces, however, this is no longer feasible. In this paper, we address
the largely open problem of how to obtain these high-confidence estimates, for
general state-spaces. We provide a high-confidence bound on an empirical
estimate of the value error to the true value error. We use this bound to
design an offline sampling algorithm, which stores the required quantities to
repeatedly compute value error estimates for any learned value function. We
provide experiments investigating the number of samples required by this
offline algorithm in simple benchmark reinforcement learning domains, and
highlight that there are still many open questions to be solved for this
important problem.
Investigating Human + Machine Complementarity for Recidivism Predictions
Authors: Sarah Tan, Julius Adebayo, Kori Inkpen, Ece Kamar
Date: 2018-08-28 00:00:00
Tasks: Decision Making, Fairness

When might human input help (or not) when assessing risk in fairness domains?
Dressel and Farid (2018) asked Mechanical Turk workers to evaluate a subset of
defendants in the ProPublica COMPAS data for risk of recidivism, and concluded
that COMPAS predictions were no more accurate or fair than predictions made by
humans. We delve deeper into this claim to explore differences in human and
algorithmic decision making. We construct a Human Risk Score based on the
predictions made by multiple Turk workers, characterize the features that
determine agreement and disagreement between COMPAS and Human Scores, and
construct hybrid Human+Machine models to predict recidivism. Our key finding is
that on this data set, Human and COMPAS decision making differed, but not in
ways that could be leveraged to significantly improve ground-truth prediction.
We present the results of our analyses and suggestions for data collection best
practices to leverage complementary strengths of human and machines in the
fairness domain.
WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations
Authors: Mohammad Taher Pilehvar, Jose Camacho-Collados
Date: 2018-08-28 00:00:00
Proceeding: NAACL 2019 6
Tasks: Word Embeddings, Word Sense Disambiguation, Word Similarity

By design, word embeddings are unable to model the dynamic nature of words'
semantics, i.e., the property of words to correspond to potentially different
meanings. To address this limitation, dozens of specialized meaning
representation techniques such as sense or contextualized embeddings have been
proposed. However, despite the popularity of research on this topic, very few
evaluation benchmarks exist that specifically focus on the dynamic semantics of
words. In this paper we show that existing models have surpassed the
performance ceiling of the standard evaluation dataset for the purpose, i.e.,
Stanford Contextual Word Similarity, and highlight its shortcomings. To address
the lack of a suitable benchmark, we put forward a large-scale Word in Context
dataset, called WiC, based on annotations curated by experts, for generic
evaluation of context-sensitive representations. WiC is released in
https://pilehvar.github.io/wic/.
All You Need is "Love": Evading Hate-speech Detection
Authors: Tommi Gröndahl, Luca Pajola, Mika Juuti, Mauro Conti, N. Asokan
Date: 2018-08-28 00:00:00
Tasks: All, Hate Speech Detection

With the spread of social networks and their unfortunate use for hate speech,
automatic detection of the latter has become a pressing problem. In this paper,
we reproduce seven state-of-the-art hate speech detection models from prior
work, and show that they perform well only when tested on the same type of data
they were trained on. Based on these results, we argue that for successful hate
speech detection, model architecture is less important than the type of data
and labeling criteria. We further show that all proposed detection techniques
are brittle against adversaries who can (automatically) insert typos, change
word boundaries or add innocuous words to the original hate speech. A
combination of these methods is also effective against Google Perspective -- a
cutting-edge solution from industry. Our experiments demonstrate that
adversarial training does not completely mitigate the attacks, and using
character-level features makes the models systematically more attack-resistant
than using word-level features.
Unsupervised Learning of Syntactic Structure with Invertible Neural Projections
Authors: Junxian He, Graham Neubig, Taylor Berg-Kirkpatrick
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Constituency Grammar Induction, Dependency Parsing, POS, Unsupervised Dependency Parsing

Unsupervised learning of syntactic structure is typically performed using
generative models with discrete latent variables and multinomial parameters. In
most cases, these models have not leveraged continuous word representations. In
this work, we propose a novel generative model that jointly learns discrete
syntactic structure and continuous word representations in an unsupervised
fashion by cascading an invertible neural network with a structured generative
prior. We show that the invertibility condition allows for efficient exact
inference and marginal likelihood computation in our model so long as the prior
is well-behaved. In experiments we instantiate our approach with both Markov
and tree-structured priors, evaluating on two tasks: part-of-speech (POS)
induction, and unsupervised dependency parsing without gold POS annotation. On
the Penn Treebank, our Markov-structured model surpasses state-of-the-art
results on POS induction. Similarly, we find that our tree-structured model
achieves state-of-the-art performance on unsupervised dependency parsing for
the difficult training condition where neither gold POS annotation nor
punctuation-based constraints are available.
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
Authors: Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine
Date: 2018-08-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-based reinforcement learning (RL) has proven to be a data efficient approach for learning control tasks but is difficult to utilize in domains with complex observations such as images. In this paper, we present a method for learning representations that are suitable for iterative model-based policy improvement, even when the underlying dynamical system has complex dynamics and image observations, in that these representations are optimized for inferring simple dynamics and cost models given data from the current policy. This enables a model-based RL method based on the linear-quadratic regulator (LQR) to be used for systems with image observations. We evaluate our approach on a range of robotics tasks, including manipulation with a real-world robotic arm directly from images. We find that our method produces substantially better final performance than other model-based RL methods while being significantly more efficient than model-free RL.
Localization Guided Learning for Pedestrian Attribute Recognition
Authors: Pengze Liu, Xihui Liu, Junjie Yan, Jing Shao
Date: 2018-08-28 00:00:00
Tasks: Attribute, Pedestrian Attribute Recognition, Scene Understanding

Pedestrian attribute recognition has attracted many attentions due to its
wide applications in scene understanding and person analysis from surveillance
videos. Existing methods try to use additional pose, part or viewpoint
information to complement the global feature representation for attribute
classification. However, these methods face difficulties in localizing the
areas corresponding to different attributes. To address this problem, we
propose a novel Localization Guided Network which assigns attribute-specific
weights to local features based on the affinity between proposals pre-extracted
proposals and attribute locations. The advantage of our model is that our local
features are learned automatically for each attribute and emphasized by the
interaction with global features. We demonstrate the effectiveness of our
Localization Guided Network on two pedestrian attribute benchmarks (PA-100K and
RAP). Our result surpasses the previous state-of-the-art in all five metrics on
both datasets.
N-ary Relation Extraction using Graph State LSTM
Authors: Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea
Date: 2018-08-28 00:00:00
Tasks: Relation, Relation Extraction, Sentence

Cross-sentence $n$-ary relation extraction detects relations among $n$
entities across multiple sentences. Typical methods formulate an input as a
\textit{document graph}, integrating various intra-sentential and
inter-sentential dependencies. The current state-of-the-art method splits the
input graph into two DAGs, adopting a DAG-structured LSTM for each. Though
being able to model rich linguistic knowledge by leveraging graph edges,
important information can be lost in the splitting procedure. We propose a
graph-state LSTM model, which uses a parallel state to model each word,
recurrently enriching state values via message passing. Compared with DAG
LSTMs, our graph LSTM keeps the original graph structure, and speeds up
computation by allowing more parallelization. On a standard benchmark, our
model shows the best result in the literature.
Disfluency Detection using Auto-Correlational Neural Networks
Authors: Paria Jamshid Lou, Peter Anderson, Mark Johnson
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Feature Engineering

In recent years, the natural language processing community has moved away from task-specific feature engineering, i.e., researchers discovering ad-hoc feature representations for various tasks, in favor of general-purpose methods that learn the input representation by themselves. However, state-of-the-art approaches to disfluency detection in spontaneous speech transcripts currently still depend on an array of hand-crafted features, and other representations derived from the output of pre-existing systems such as language models or dependency parsers. As an alternative, this paper proposes a simple yet effective model for automatic disfluency detection, called an auto-correlational neural network (ACNN). The model uses a convolutional neural network (CNN) and augments it with a new auto-correlation operator at the lowest layer that can capture the kinds of "rough copy" dependencies that are characteristic of repair disfluencies in speech. In experiments, the ACNN model outperforms the baseline CNN on a disfluency detection task with a 5% increase in f-score, which is close to the previous best result on this task.
Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model
Authors: Paria Jamshid Lou, Mark Johnson
Date: 2018-08-28 00:00:00
Proceeding: ACL 2017 7
Tasks: Language Modeling, Language Modelling, model, Reranking

This paper presents a model for disfluency detection in spontaneous speech
transcripts called LSTM Noisy Channel Model. The model uses a Noisy Channel
Model (NCM) to generate n-best candidate disfluency analyses and a Long
Short-Term Memory (LSTM) language model to score the underlying fluent
sentences of each analysis. The LSTM language model scores, along with other
features, are used in a MaxEnt reranker to identify the most plausible
analysis. We show that using an LSTM language model in the reranking process of
noisy channel disfluency model improves the state-of-the-art in disfluency
detection.
A Framework for Complementary Companion Character Behavior in Video Games
Authors: Gavin Scott, Foaad Khosmood
Date: 2018-08-28 00:00:00
Tasks: Game Design

We propose a game development framework capable of governing the behavior of
complementary companions in a video game. A "complementary" action is
contrasted with a mimicking action and is defined as any action by a friendly
non-player character that furthers the player's strategy. This is determined
through a combination of both player action and game state prediction processes
while allowing the AI companion to experiment. We determine the location of
interest for companion actions based on a dynamic set of regions customized to
the individual player. A user study shows promising results; a majority of
participants familiar with game design react positively to the companion
behavior, stating that they would consider using the frame-work in future games
themselves.
Evaluating the Utility of Hand-crafted Features in Sequence Labelling
Authors: Minghao Wu, Fei Liu, Trevor Cohn
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Conventional wisdom is that hand-crafted features are redundant for deep
learning models, as they already learn adequate representations of text
automatically from corpora. In this work, we test this claim by proposing a new
method for exploiting handcrafted features as part of a novel hybrid learning
approach, incorporating a feature auto-encoder loss component. We evaluate on
the task of named entity recognition (NER), where we show that including manual
features for part-of-speech, word shapes and gazetteers can improve the
performance of a neural CRF model. We obtain a $F_1$ of 91.89 for the
CoNLL-2003 English shared task, which significantly outperforms a collection of
highly competitive baseline models. We also present an ablation study showing
the importance of auto-encoding, over using features as either inputs or
outputs alone, and moreover, show including the autoencoder components reduces
training requirements to 60\%, while retaining the same predictive accuracy.
Cognitive Consistency Routing Algorithm of Capsule-network
Authors: Huayu Li
Date: 2018-08-27 00:00:00

Artificial Neural Networks (ANNs) are computational models inspired by the
central nervous system (especially the brain) of animals and are used to
estimate or generate unknown approximation functions relied on large amounts of
inputs. Capsule Neural Network (Sabour S, et al.[2017]) is a novel structure of
Convolutional Neural Networks which simulates the visual processing system of
human brain. In this paper, we introduce psychological theories which called
Cognitive Consistency to optimize the routing algorithm of Capsnet to make it
more close to the work pattern of human brain. It has been shown in the
experiment that a progress had been made compared with the baseline.
An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing
Authors: Aaron Smith, Miryam de Lhoneux, Sara Stymne, Joakim Nivre
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dependency Parsing, POS, TAG, Word Embeddings

We provide a comprehensive analysis of the interactions between pre-trained
word embeddings, character models and POS tags in a transition-based dependency
parser. While previous studies have shown POS information to be less important
in the presence of character models, we show that in fact there are complex
interactions between all three techniques. In isolation each produces large
improvements over a baseline system using randomly initialised word embeddings
only, but combining them quickly leads to diminishing returns. We categorise
words by frequency, POS tag and language in order to systematically investigate
how each of the techniques affects parsing quality. For many word categories,
applying any two of the three techniques is almost as good as the full combined
system. Character models tend to be more important for low-frequency open-class
words, especially in morphologically rich languages, while POS tags can help
disambiguate high-frequency function words. We also show that large character
embedding sizes help even for languages with small character sets, especially
in morphologically rich languages.
Quantum enhanced cross-validation for near-optimal neural networks architecture selection
Authors: Priscila G. M. dos Santos, Rodrigo S. Sousa, Ismael C. S. Araujo, Adenilton J. da Silva
Date: 2018-08-27 00:00:00

This paper proposes a quantum-classical algorithm to evaluate and select
classical artificial neural networks architectures. The proposed algorithm is
based on a probabilistic quantum memory and the possibility to train artificial
neural networks in superposition. We obtain an exponential quantum speedup in
the evaluation of neural networks. We also verify experimentally through a
reduced experimental analysis that the proposed algorithm can be used to select
near-optimal neural networks.
Loss Functions, Axioms, and Peer Review
Authors: Ritesh Noothigattu, Nihar B. Shah, Ariel D. Procaccia
Date: 2018-08-27 00:00:00

It is common to see a handful of reviewers reject a highly novel paper, because they view, say, extensive experiments as far more important than novelty, whereas the community as a whole would have embraced the paper. More generally, the disparate mapping of criteria scores to final recommendations by different reviewers is a major source of inconsistency in peer review. In this paper we present a framework inspired by empirical risk minimization (ERM) for learning the community's aggregate mapping. The key challenge that arises is the specification of a loss function for ERM. We consider the class of $L(p,q)$ loss functions, which is a matrix-extension of the standard class of $L_p$ losses on vectors; here the choice of the loss function amounts to choosing the hyperparameters $p, q \in [1,\infty]$. To deal with the absence of ground truth in our problem, we instead draw on computational social choice to identify desirable values of the hyperparameters $p$ and $q$. Specifically, we characterize $p=q=1$ as the only choice of these hyperparameters that satisfies three natural axiomatic properties. Finally, we implement and apply our approach to reviews from IJCAI 2017.
Parameter sharing between dependency parsers for related languages
Authors: Miryam de Lhoneux, Johannes Bjerva, Isabelle Augenstein, Anders Søgaard
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10

Previous work has suggested that parameter sharing between transition-based
neural dependency parsers for related languages can lead to better performance,
but there is no consensus on what parameters to share. We present an evaluation
of 27 different parameter sharing strategies across 10 languages, representing
five pairs of related languages, each pair from a different language family. We
find that sharing transition classifier parameters always helps, whereas the
usefulness of sharing word and/or character LSTM parameters varies. Based on
this result, we propose an architecture where the transition classifier is
shared, and the sharing of word and character parameters is controlled by a
parameter that can be tuned on validation data. This model is linguistically
motivated and obtains significant improvements over a monolingually trained
baseline. We also find that sharing transition classifier parameters helps when
training a parser on unrelated language pairs, but we find that, in the case of
unrelated languages, sharing too many parameters does not help.
Adversarial Feature Learning of Online Monitoring Data for Operational Risk Assessment in Distribution Networks
Authors: Xin Shi, Robert Qiu, Tiebin Mi, Xing He, Yongli Zhu
Date: 2018-08-27 00:00:00

With the deployment of online monitoring systems in distribution networks, massive amounts of data collected through them contains rich information on the operating states of the networks. By leveraging the data, an unsupervised approach based on bidirectional generative adversarial networks (BiGANs) is proposed for operational risk assessment in distribution networks in this paper. The approach includes two stages: (1) adversarial feature learning. The most representative features are extracted from the online monitoring data and a statistical index $\mathcal{N}_{\phi}$ is calculated for the features, during which we make no assumptions or simplifications on the real data. (2) operational risk assessment. The confidence level $1-\alpha$ for the population mean of the standardized $\mathcal{N}_{\phi}$ is combined with the operational risk levels which are divided into emergency, high risk, preventive and normal, and the p value for each data point is calculated and compared with $\frac{\alpha}{2}$ to determine the risk levels. The proposed approach is capable of discovering the latent structure of the real data and providing more accurate assessment result. The synthetic data is employed to illustrate the selection of parameters involved in the proposed approach. Case studies on the real-world online monitoring data validate the effectiveness and advantages of the proposed approach in risk assessment.
Single Shot Scene Text Retrieval
Authors: Lluís Gómez, Andrés Mafla, Marçal Rusiñol, Dimosthenis Karatzas
Date: 2018-08-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Retrieval, Retrieval, Scene Understanding, Text Retrieval

Textual information found in scene images provides high level semantic
information about the image and its context and it can be leveraged for better
scene understanding. In this paper we address the problem of scene text
retrieval: given a text query, the system must return all images containing the
queried text. The novelty of the proposed model consists in the usage of a
single shot CNN architecture that predicts at the same time bounding boxes and
a compact text representation of the words in them. In this way, the text based
image retrieval task can be casted as a simple nearest neighbor search of the
query text representation over the outputs of the CNN over the entire image
database. Our experiments demonstrate that the proposed architecture
outperforms previous state-of-the-art while it offers a significant increase in
processing speed.
Adversarial Decomposition of Text Representation
Authors: Alexey Romanov, Anna Rumshisky, Anna Rogers, David Donahue
Date: 2018-08-27 00:00:00
Proceeding: NAACL 2019 6
Tasks: Sentence

In this paper, we present a method for adversarial decomposition of text
representation. This method can be used to decompose a representation of an
input sentence into several independent vectors, each of them responsible for a
specific aspect of the input sentence. We evaluate the proposed method on two
case studies: the conversion between different social registers and diachronic
language change. We show that the proposed method is capable of fine-grained
controlled change of these aspects of the input sentence. It is also learning a
continuous (rather than categorical) representation of the style of the
sentence, which is more linguistically realistic. The model uses
adversarial-motivational training and includes a special motivational loss,
which acts opposite to the discriminator and encourages a better decomposition.
Furthermore, we evaluate the obtained meaning embeddings on a downstream task
of paraphrase detection and show that they significantly outperform the
embeddings of a regular autoencoder.