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2,002.09836 | Fill in the BLANC: Human-free quality estimation of document summaries | ['Oleg Vasilyev', 'Vedant Dharnidharka', 'John Bohannon'] | ['cs.CL'] | We present BLANC, a new approach to the automatic estimation of document
summary quality. Our goal is to measure the functional performance of a summary
with an objective, reproducible, and fully automated method. Our approach
achieves this by measuring the performance boost gained by a pre-trained
language model with access to a document summary while carrying out its
language understanding task on the document's text. We present evidence that
BLANC scores have as good correlation with human evaluations as do the ROUGE
family of summary quality measurements. And unlike ROUGE, the BLANC method does
not require human-written reference summaries, allowing for fully human-free
summary quality estimation. | 2020-02-23T06:21:43Z | 10 pages, 9 figures, 3 tables. In: Proceedings of the First Workshop
on Evaluation and Comparison of NLP Systems (Eval4NLP, Nov. 2020) p.11-20,
ACL | Proceedings of the First Workshop on Evaluation and Comparison of
NLP Systems (Nov.2020) 11-20 | null | Fill in the BLANC: Human-free quality estimation of document summaries | ['Oleg V. Vasilyev', 'Vedant Dharnidharka', 'John Bohannon'] | 2,020 | EVAL4NLP | 119 | 32 | ['Computer Science'] |
2,002.10857 | Circle Loss: A Unified Perspective of Pair Similarity Optimization | ['Yifan Sun', 'Changmao Cheng', 'Yuhan Zhang', 'Chi Zhang', 'Liang Zheng', 'Zhongdao Wang', 'Yichen Wei'] | ['cs.CV'] | This paper provides a pair similarity optimization viewpoint on deep feature
learning, aiming to maximize the within-class similarity $s_p$ and minimize the
between-class similarity $s_n$. We find a majority of loss functions, including
the triplet loss and the softmax plus cross-entropy loss, embed $s_n$ and $s_p$
into similarity pairs and seek to reduce $(s_n-s_p)$. Such an optimization
manner is inflexible, because the penalty strength on every single similarity
score is restricted to be equal. Our intuition is that if a similarity score
deviates far from the optimum, it should be emphasized. To this end, we simply
re-weight each similarity to highlight the less-optimized similarity scores. It
results in a Circle loss, which is named due to its circular decision boundary.
The Circle loss has a unified formula for two elemental deep feature learning
approaches, i.e. learning with class-level labels and pair-wise labels.
Analytically, we show that the Circle loss offers a more flexible optimization
approach towards a more definite convergence target, compared with the loss
functions optimizing $(s_n-s_p)$. Experimentally, we demonstrate the
superiority of the Circle loss on a variety of deep feature learning tasks. On
face recognition, person re-identification, as well as several fine-grained
image retrieval datasets, the achieved performance is on par with the state of
the art. | 2020-02-25T13:56:40Z | null | null | null | null | null | null | null | null | null | null |
2,002.10957 | MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression
of Pre-Trained Transformers | ['Wenhui Wang', 'Furu Wei', 'Li Dong', 'Hangbo Bao', 'Nan Yang', 'Ming Zhou'] | ['cs.CL'] | Pre-trained language models (e.g., BERT (Devlin et al., 2018) and its
variants) have achieved remarkable success in varieties of NLP tasks. However,
these models usually consist of hundreds of millions of parameters which brings
challenges for fine-tuning and online serving in real-life applications due to
latency and capacity constraints. In this work, we present a simple and
effective approach to compress large Transformer (Vaswani et al., 2017) based
pre-trained models, termed as deep self-attention distillation. The small model
(student) is trained by deeply mimicking the self-attention module, which plays
a vital role in Transformer networks, of the large model (teacher).
Specifically, we propose distilling the self-attention module of the last
Transformer layer of the teacher, which is effective and flexible for the
student. Furthermore, we introduce the scaled dot-product between values in the
self-attention module as the new deep self-attention knowledge, in addition to
the attention distributions (i.e., the scaled dot-product of queries and keys)
that have been used in existing works. Moreover, we show that introducing a
teacher assistant (Mirzadeh et al., 2019) also helps the distillation of large
pre-trained Transformer models. Experimental results demonstrate that our
monolingual model outperforms state-of-the-art baselines in different parameter
size of student models. In particular, it retains more than 99% accuracy on
SQuAD 2.0 and several GLUE benchmark tasks using 50% of the Transformer
parameters and computations of the teacher model. We also obtain competitive
results in applying deep self-attention distillation to multilingual
pre-trained models. | 2020-02-25T15:21:10Z | Code and models:
https://github.com/microsoft/unilm/tree/master/minilm | null | null | null | null | null | null | null | null | null |
2,002.12328 | Few-shot Natural Language Generation for Task-Oriented Dialog | ['Baolin Peng', 'Chenguang Zhu', 'Chunyuan Li', 'Xiujun Li', 'Jinchao Li', 'Michael Zeng', 'Jianfeng Gao'] | ['cs.CL'] | As a crucial component in task-oriented dialog systems, the Natural Language
Generation (NLG) module converts a dialog act represented in a semantic form
into a response in natural language. The success of traditional template-based
or statistical models typically relies on heavily annotated data, which is
infeasible for new domains. Therefore, it is pivotal for an NLG system to
generalize well with limited labelled data in real applications. To this end,
we present FewShotWoz, the first NLG benchmark to simulate the few-shot
learning setting in task-oriented dialog systems. Further, we develop the
SC-GPT model. It is pre-trained on a large set of annotated NLG corpus to
acquire the controllable generation ability, and fine-tuned with only a few
domain-specific labels to adapt to new domains. Experiments on FewShotWoz and
the large Multi-Domain-WOZ datasets show that the proposed SC-GPT significantly
outperforms existing methods, measured by various automatic metrics and human
evaluations. | 2020-02-27T18:48:33Z | Project website: https://aka.ms/scgpt ; Code and data:
https://github.com/pengbaolin/SC-GPT | null | null | null | null | null | null | null | null | null |
2,003.00104 | AraBERT: Transformer-based Model for Arabic Language Understanding | ['Wissam Antoun', 'Fady Baly', 'Hazem Hajj'] | ['cs.CL'] | The Arabic language is a morphologically rich language with relatively few
resources and a less explored syntax compared to English. Given these
limitations, Arabic Natural Language Processing (NLP) tasks like Sentiment
Analysis (SA), Named Entity Recognition (NER), and Question Answering (QA),
have proven to be very challenging to tackle. Recently, with the surge of
transformers based models, language-specific BERT based models have proven to
be very efficient at language understanding, provided they are pre-trained on a
very large corpus. Such models were able to set new standards and achieve
state-of-the-art results for most NLP tasks. In this paper, we pre-trained BERT
specifically for the Arabic language in the pursuit of achieving the same
success that BERT did for the English language. The performance of AraBERT is
compared to multilingual BERT from Google and other state-of-the-art
approaches. The results showed that the newly developed AraBERT achieved
state-of-the-art performance on most tested Arabic NLP tasks. The pretrained
araBERT models are publicly available on https://github.com/aub-mind/arabert
hoping to encourage research and applications for Arabic NLP. | 2020-02-28T22:59:24Z | Proceedings of the Twelfth International Conference on Language
Resources and Evaluation (LREC 2020), Marseille, France (2020) | null | null | null | null | null | null | null | null | null |
2,003.00196 | First Order Motion Model for Image Animation | ['Aliaksandr Siarohin', 'Stéphane Lathuilière', 'Sergey Tulyakov', 'Elisa Ricci', 'Nicu Sebe'] | ['cs.CV', 'cs.AI'] | Image animation consists of generating a video sequence so that an object in
a source image is animated according to the motion of a driving video. Our
framework addresses this problem without using any annotation or prior
information about the specific object to animate. Once trained on a set of
videos depicting objects of the same category (e.g. faces, human bodies), our
method can be applied to any object of this class. To achieve this, we decouple
appearance and motion information using a self-supervised formulation. To
support complex motions, we use a representation consisting of a set of learned
keypoints along with their local affine transformations. A generator network
models occlusions arising during target motions and combines the appearance
extracted from the source image and the motion derived from the driving video.
Our framework scores best on diverse benchmarks and on a variety of object
categories. Our source code is publicly available. | 2020-02-29T07:08:56Z | NeurIPS 2019 | null | null | null | null | null | null | null | null | null |
2,003.00653 | Adversarial Attacks and Defenses on Graphs: A Review, A Tool and
Empirical Studies | ['Wei Jin', 'Yaxin Li', 'Han Xu', 'Yiqi Wang', 'Shuiwang Ji', 'Charu Aggarwal', 'Jiliang Tang'] | ['cs.LG', 'cs.CR', 'stat.ML'] | Deep neural networks (DNNs) have achieved significant performance in various
tasks. However, recent studies have shown that DNNs can be easily fooled by
small perturbation on the input, called adversarial attacks. As the extensions
of DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated to
inherit this vulnerability. Adversary can mislead GNNs to give wrong
predictions by modifying the graph structure such as manipulating a few edges.
This vulnerability has arisen tremendous concerns for adapting GNNs in
safety-critical applications and has attracted increasing research attention in
recent years. Thus, it is necessary and timely to provide a comprehensive
overview of existing graph adversarial attacks and the countermeasures. In this
survey, we categorize existing attacks and defenses, and review the
corresponding state-of-the-art methods. Furthermore, we have developed a
repository with representative algorithms
(https://github.com/DSE-MSU/DeepRobust/tree/master/deeprobust/graph). The
repository enables us to conduct empirical studies to deepen our understandings
on attacks and defenses on graphs. | 2020-03-02T04:32:38Z | Accepted by SIGKDD Explorations | null | null | null | null | null | null | null | null | null |
2,003.00744 | PhoBERT: Pre-trained language models for Vietnamese | ['Dat Quoc Nguyen', 'Anh Tuan Nguyen'] | ['cs.CL', 'cs.AI'] | We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the
first public large-scale monolingual language models pre-trained for
Vietnamese. Experimental results show that PhoBERT consistently outperforms the
recent best pre-trained multilingual model XLM-R (Conneau et al., 2020) and
improves the state-of-the-art in multiple Vietnamese-specific NLP tasks
including Part-of-speech tagging, Dependency parsing, Named-entity recognition
and Natural language inference. We release PhoBERT to facilitate future
research and downstream applications for Vietnamese NLP. Our PhoBERT models are
available at https://github.com/VinAIResearch/PhoBERT | 2020-03-02T10:21:17Z | EMNLP 2020 (Findings) | null | null | null | null | null | null | null | null | null |
2,003.01309 | Controllable Time-Delay Transformer for Real-Time Punctuation Prediction
and Disfluency Detection | ['Qian Chen', 'Mengzhe Chen', 'Bo Li', 'Wen Wang'] | ['cs.CL', 'cs.SD', 'eess.AS'] | With the increased applications of automatic speech recognition (ASR) in
recent years, it is essential to automatically insert punctuation marks and
remove disfluencies in transcripts, to improve the readability of the
transcripts as well as the performance of subsequent applications, such as
machine translation, dialogue systems, and so forth. In this paper, we propose
a Controllable Time-delay Transformer (CT-Transformer) model that jointly
completes the punctuation prediction and disfluency detection tasks in real
time. The CT-Transformer model facilitates freezing partial outputs with
controllable time delay to fulfill the real-time constraints in partial
decoding required by subsequent applications. We further propose a fast
decoding strategy to minimize latency while maintaining competitive
performance. Experimental results on the IWSLT2011 benchmark dataset and an
in-house Chinese annotated dataset demonstrate that the proposed approach
outperforms the previous state-of-the-art models on F-scores and achieves a
competitive inference speed. | 2020-03-03T03:17:29Z | 4 pages, 2 figures, accepted by ICASSP 2020 | null | null | Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detection | ['Qian Chen', 'Mengzhe Chen', 'Bo Li', 'Wen Wang'] | 2,020 | IEEE International Conference on Acoustics, Speech, and Signal Processing | 36 | 35 | ['Computer Science', 'Engineering'] |
2,003.01355 | CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language
Model | ['Liang Xu', 'Xuanwei Zhang', 'Qianqian Dong'] | ['cs.CL'] | In this paper, we introduce the Chinese corpus from CLUE organization,
CLUECorpus2020, a large-scale corpus that can be used directly for
self-supervised learning such as pre-training of a language model, or language
generation. It has 100G raw corpus with 35 billion Chinese characters, which is
retrieved from Common Crawl. To better understand this corpus, we conduct
language understanding experiments on both small and large scale, and results
show that the models trained on this corpus can achieve excellent performance
on Chinese. We release a new Chinese vocabulary with a size of 8K, which is
only one-third of the vocabulary size used in Chinese Bert released by Google.
It saves computational cost and memory while works as good as original
vocabulary. We also release both large and tiny versions of the pre-trained
model on this corpus. The former achieves the state-of-the-art result, and the
latter retains most precision while accelerating training and prediction speed
for eight times compared to Bert-base. To facilitate future work on
self-supervised learning on Chinese, we release our dataset, new vocabulary,
codes, and pre-trained models on Github. | 2020-03-03T06:39:27Z | 8 pages, 9 tables | null | null | CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model | ['Liang Xu', 'Xuanwei Zhang', 'Qianqian Dong'] | 2,020 | arXiv.org | 71 | 14 | ['Computer Science'] |
2,003.0195 | AlignTTS: Efficient Feed-Forward Text-to-Speech System without Explicit
Alignment | ['Zhen Zeng', 'Jianzong Wang', 'Ning Cheng', 'Tian Xia', 'Jing Xiao'] | ['eess.AS', 'cs.CL', 'cs.SD'] | Targeting at both high efficiency and performance, we propose AlignTTS to
predict the mel-spectrum in parallel. AlignTTS is based on a Feed-Forward
Transformer which generates mel-spectrum from a sequence of characters, and the
duration of each character is determined by a duration predictor.Instead of
adopting the attention mechanism in Transformer TTS to align text to
mel-spectrum, the alignment loss is presented to consider all possible
alignments in training by use of dynamic programming. Experiments on the
LJSpeech dataset show that our model achieves not only state-of-the-art
performance which outperforms Transformer TTS by 0.03 in mean option score
(MOS), but also a high efficiency which is more than 50 times faster than
real-time. | 2020-03-04T08:44:32Z | will be presented in ICASSP 2020 | null | null | Aligntts: Efficient Feed-Forward Text-to-Speech System Without Explicit Alignment | ['Zhen Zeng', 'Jianzong Wang', 'Ning Cheng', 'Tian Xia', 'Jing Xiao'] | 2,020 | IEEE International Conference on Acoustics, Speech, and Signal Processing | 56 | 21 | ['Computer Science', 'Engineering'] |
2,003.02838 | Accelerator-aware Neural Network Design using AutoML | ['Suyog Gupta', 'Berkin Akin'] | ['eess.SP', 'cs.LG', 'stat.ML'] | While neural network hardware accelerators provide a substantial amount of
raw compute throughput, the models deployed on them must be co-designed for the
underlying hardware architecture to obtain the optimal system performance. We
present a class of computer vision models designed using hardware-aware neural
architecture search and customized to run on the Edge TPU, Google's neural
network hardware accelerator for low-power, edge devices. For the Edge TPU in
Coral devices, these models enable real-time image classification performance
while achieving accuracy typically seen only with larger, compute-heavy models
running in data centers. On Pixel 4's Edge TPU, these models improve the
accuracy-latency tradeoff over existing SoTA mobile models. | 2020-03-05T21:34:22Z | Accepted paper at the On-device Intelligence Workshop at MLSys
Conference 2020 | null | null | null | null | null | null | null | null | null |
2,003.05002 | TyDi QA: A Benchmark for Information-Seeking Question Answering in
Typologically Diverse Languages | ['Jonathan H. Clark', 'Eunsol Choi', 'Michael Collins', 'Dan Garrette', 'Tom Kwiatkowski', 'Vitaly Nikolaev', 'Jennimaria Palomaki'] | ['cs.CL', 'cs.LG'] | Confidently making progress on multilingual modeling requires challenging,
trustworthy evaluations. We present TyDi QA---a question answering dataset
covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology---the set of
linguistic features each language expresses---such that we expect models
performing well on this set to generalize across a large number of the world's
languages. We present a quantitative analysis of the data quality and
example-level qualitative linguistic analyses of observed language phenomena
that would not be found in English-only corpora. To provide a realistic
information-seeking task and avoid priming effects, questions are written by
people who want to know the answer, but don't know the answer yet, and the data
is collected directly in each language without the use of translation. | 2020-03-10T21:11:53Z | To appear in Transactions of the Association for Computational
Linguistics (TACL) 2020. Please use this as the citation | null | null | null | null | null | null | null | null | null |
2,003.06505 | AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data | ['Nick Erickson', 'Jonas Mueller', 'Alexander Shirkov', 'Hang Zhang', 'Pedro Larroy', 'Mu Li', 'Alexander Smola'] | ['stat.ML', 'cs.LG'] | We introduce AutoGluon-Tabular, an open-source AutoML framework that requires
only a single line of Python to train highly accurate machine learning models
on an unprocessed tabular dataset such as a CSV file. Unlike existing AutoML
frameworks that primarily focus on model/hyperparameter selection,
AutoGluon-Tabular succeeds by ensembling multiple models and stacking them in
multiple layers. Experiments reveal that our multi-layer combination of many
models offers better use of allocated training time than seeking out the best.
A second contribution is an extensive evaluation of public and commercial
AutoML platforms including TPOT, H2O, AutoWEKA, auto-sklearn, AutoGluon, and
Google AutoML Tables. Tests on a suite of 50 classification and regression
tasks from Kaggle and the OpenML AutoML Benchmark reveal that AutoGluon is
faster, more robust, and much more accurate. We find that AutoGluon often even
outperforms the best-in-hindsight combination of all of its competitors. In two
popular Kaggle competitions, AutoGluon beat 99% of the participating data
scientists after merely 4h of training on the raw data. | 2020-03-13T23:10:39Z | null | null | null | null | null | null | null | null | null | null |
2,003.06713 | Document Ranking with a Pretrained Sequence-to-Sequence Model | ['Rodrigo Nogueira', 'Zhiying Jiang', 'Jimmy Lin'] | ['cs.IR', 'cs.LG'] | This work proposes a novel adaptation of a pretrained sequence-to-sequence
model to the task of document ranking. Our approach is fundamentally different
from a commonly-adopted classification-based formulation of ranking, based on
encoder-only pretrained transformer architectures such as BERT. We show how a
sequence-to-sequence model can be trained to generate relevance labels as
"target words", and how the underlying logits of these target words can be
interpreted as relevance probabilities for ranking. On the popular MS MARCO
passage ranking task, experimental results show that our approach is at least
on par with previous classification-based models and can surpass them with
larger, more-recent models. On the test collection from the TREC 2004 Robust
Track, we demonstrate a zero-shot transfer-based approach that outperforms
previous state-of-the-art models requiring in-dataset cross-validation.
Furthermore, we find that our approach significantly outperforms an
encoder-only model in a data-poor regime (i.e., with few training examples). We
investigate this observation further by varying target words to probe the
model's use of latent knowledge. | 2020-03-14T22:29:50Z | null | null | null | null | null | null | null | null | null | null |
2,003.06792 | Learning Enriched Features for Real Image Restoration and Enhancement | ['Syed Waqas Zamir', 'Aditya Arora', 'Salman Khan', 'Munawar Hayat', 'Fahad Shahbaz Khan', 'Ming-Hsuan Yang', 'Ling Shao'] | ['cs.CV'] | With the goal of recovering high-quality image content from its degraded
version, image restoration enjoys numerous applications, such as in
surveillance, computational photography, medical imaging, and remote sensing.
Recently, convolutional neural networks (CNNs) have achieved dramatic
improvements over conventional approaches for image restoration task. Existing
CNN-based methods typically operate either on full-resolution or on
progressively low-resolution representations. In the former case, spatially
precise but contextually less robust results are achieved, while in the latter
case, semantically reliable but spatially less accurate outputs are generated.
In this paper, we present a novel architecture with the collective goals of
maintaining spatially-precise high-resolution representations through the
entire network and receiving strong contextual information from the
low-resolution representations. The core of our approach is a multi-scale
residual block containing several key elements: (a) parallel multi-resolution
convolution streams for extracting multi-scale features, (b) information
exchange across the multi-resolution streams, (c) spatial and channel attention
mechanisms for capturing contextual information, and (d) attention based
multi-scale feature aggregation. In a nutshell, our approach learns an enriched
set of features that combines contextual information from multiple scales,
while simultaneously preserving the high-resolution spatial details. Extensive
experiments on five real image benchmark datasets demonstrate that our method,
named as MIRNet, achieves state-of-the-art results for a variety of image
processing tasks, including image denoising, super-resolution, and image
enhancement. The source code and pre-trained models are available at
https://github.com/swz30/MIRNet. | 2020-03-15T11:04:30Z | Accepted for publication at ECCV 2020 | null | null | null | null | null | null | null | null | null |
2,003.10286 | PathVQA: 30000+ Questions for Medical Visual Question Answering | ['Xuehai He', 'Yichen Zhang', 'Luntian Mou', 'Eric Xing', 'Pengtao Xie'] | ['cs.CL', 'cs.AI'] | Is it possible to develop an "AI Pathologist" to pass the board-certified
examination of the American Board of Pathology? To achieve this goal, the first
step is to create a visual question answering (VQA) dataset where the AI agent
is presented with a pathology image together with a question and is asked to
give the correct answer. Our work makes the first attempt to build such a
dataset. Different from creating general-domain VQA datasets where the images
are widely accessible and there are many crowdsourcing workers available and
capable of generating question-answer pairs, developing a medical VQA dataset
is much more challenging. First, due to privacy concerns, pathology images are
usually not publicly available. Second, only well-trained pathologists can
understand pathology images, but they barely have time to help create datasets
for AI research. To address these challenges, we resort to pathology textbooks
and online digital libraries. We develop a semi-automated pipeline to extract
pathology images and captions from textbooks and generate question-answer pairs
from captions using natural language processing. We collect 32,799 open-ended
questions from 4,998 pathology images where each question is manually checked
to ensure correctness. To our best knowledge, this is the first dataset for
pathology VQA. Our dataset will be released publicly to promote research in
medical VQA. | 2020-03-07T17:55:41Z | null | null | null | null | null | null | null | null | null | null |
2,003.10555 | ELECTRA: Pre-training Text Encoders as Discriminators Rather Than
Generators | ['Kevin Clark', 'Minh-Thang Luong', 'Quoc V. Le', 'Christopher D. Manning'] | ['cs.CL'] | Masked language modeling (MLM) pre-training methods such as BERT corrupt the
input by replacing some tokens with [MASK] and then train a model to
reconstruct the original tokens. While they produce good results when
transferred to downstream NLP tasks, they generally require large amounts of
compute to be effective. As an alternative, we propose a more sample-efficient
pre-training task called replaced token detection. Instead of masking the
input, our approach corrupts it by replacing some tokens with plausible
alternatives sampled from a small generator network. Then, instead of training
a model that predicts the original identities of the corrupted tokens, we train
a discriminative model that predicts whether each token in the corrupted input
was replaced by a generator sample or not. Thorough experiments demonstrate
this new pre-training task is more efficient than MLM because the task is
defined over all input tokens rather than just the small subset that was masked
out. As a result, the contextual representations learned by our approach
substantially outperform the ones learned by BERT given the same model size,
data, and compute. The gains are particularly strong for small models; for
example, we train a model on one GPU for 4 days that outperforms GPT (trained
using 30x more compute) on the GLUE natural language understanding benchmark.
Our approach also works well at scale, where it performs comparably to RoBERTa
and XLNet while using less than 1/4 of their compute and outperforms them when
using the same amount of compute. | 2020-03-23T21:17:42Z | ICLR 2020 | null | null | null | null | null | null | null | null | null |
2,003.10564 | Improving Yorùbá Diacritic Restoration | ['Iroro Orife', 'David I. Adelani', 'Timi Fasubaa', 'Victor Williamson', 'Wuraola Fisayo Oyewusi', 'Olamilekan Wahab', 'Kola Tubosun'] | ['cs.CL'] | Yor\`ub\'a is a widely spoken West African language with a writing system
rich in orthographic and tonal diacritics. They provide morphological
information, are crucial for lexical disambiguation, pronunciation and are
vital for any computational Speech or Natural Language Processing tasks.
However diacritic marks are commonly excluded from electronic texts due to
limited device and application support as well as general education on proper
usage. We report on recent efforts at dataset cultivation. By aggregating and
improving disparate texts from the web and various personal libraries, we were
able to significantly grow our clean Yor\`ub\'a dataset from a majority
Bibilical text corpora with three sources to millions of tokens from over a
dozen sources. We evaluate updated diacritic restoration models on a new,
general purpose, public-domain Yor\`ub\'a evaluation dataset of modern
journalistic news text, selected to be multi-purpose and reflecting
contemporary usage. All pre-trained models, datasets and source-code have been
released as an open-source project to advance efforts on Yor\`ub\'a language
technology. | 2020-03-23T22:07:15Z | Accepted to ICLR 2020 AfricaNLP workshop | null | null | null | null | null | null | null | null | null |
2,003.1108 | XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating
Cross-lingual Generalization | ['Junjie Hu', 'Sebastian Ruder', 'Aditya Siddhant', 'Graham Neubig', 'Orhan Firat', 'Melvin Johnson'] | ['cs.CL', 'cs.LG'] | Much recent progress in applications of machine learning models to NLP has
been driven by benchmarks that evaluate models across a wide variety of tasks.
However, these broad-coverage benchmarks have been mostly limited to English,
and despite an increasing interest in multilingual models, a benchmark that
enables the comprehensive evaluation of such methods on a diverse range of
languages and tasks is still missing. To this end, we introduce the
Cross-lingual TRansfer Evaluation of Multilingual Encoders XTREME benchmark, a
multi-task benchmark for evaluating the cross-lingual generalization
capabilities of multilingual representations across 40 languages and 9 tasks.
We demonstrate that while models tested on English reach human performance on
many tasks, there is still a sizable gap in the performance of cross-lingually
transferred models, particularly on syntactic and sentence retrieval tasks.
There is also a wide spread of results across languages. We release the
benchmark to encourage research on cross-lingual learning methods that transfer
linguistic knowledge across a diverse and representative set of languages and
tasks. | 2020-03-24T19:09:37Z | In Proceedings of the 37th International Conference on Machine
Learning (ICML). July 2020 | null | null | null | null | null | null | null | null | null |
2,003.11982 | In defence of metric learning for speaker recognition | ['Joon Son Chung', 'Jaesung Huh', 'Seongkyu Mun', 'Minjae Lee', 'Hee Soo Heo', 'Soyeon Choe', 'Chiheon Ham', 'Sunghwan Jung', 'Bong-Jin Lee', 'Icksang Han'] | ['eess.AS', 'cs.SD'] | The objective of this paper is 'open-set' speaker recognition of unseen
speakers, where ideal embeddings should be able to condense information into a
compact utterance-level representation that has small intra-speaker and large
inter-speaker distance.
A popular belief in speaker recognition is that networks trained with
classification objectives outperform metric learning methods. In this paper, we
present an extensive evaluation of most popular loss functions for speaker
recognition on the VoxCeleb dataset. We demonstrate that the vanilla triplet
loss shows competitive performance compared to classification-based losses, and
those trained with our proposed metric learning objective outperform
state-of-the-art methods. | 2020-03-26T15:43:10Z | The code can be found at https://github.com/clovaai/voxceleb_trainer | null | 10.21437/Interspeech.2020-1064 | null | null | null | null | null | null | null |
2,003.12039 | RAFT: Recurrent All-Pairs Field Transforms for Optical Flow | ['Zachary Teed', 'Jia Deng'] | ['cs.CV'] | We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network
architecture for optical flow. RAFT extracts per-pixel features, builds
multi-scale 4D correlation volumes for all pairs of pixels, and iteratively
updates a flow field through a recurrent unit that performs lookups on the
correlation volumes. RAFT achieves state-of-the-art performance. On KITTI, RAFT
achieves an F1-all error of 5.10%, a 16% error reduction from the best
published result (6.10%). On Sintel (final pass), RAFT obtains an
end-point-error of 2.855 pixels, a 30% error reduction from the best published
result (4.098 pixels). In addition, RAFT has strong cross-dataset
generalization as well as high efficiency in inference time, training speed,
and parameter count. Code is available at https://github.com/princeton-vl/RAFT. | 2020-03-26T17:12:42Z | fixed a formatting issue, Eq 7. no change in content | null | null | null | null | null | null | null | null | null |
2,003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | ['Elena Leitner', 'Georg Rehm', 'Julián Moreno-Schneider'] | ['cs.CL', 'cs.IR'] | We describe a dataset developed for Named Entity Recognition in German
federal court decisions. It consists of approx. 67,000 sentences with over 2
million tokens. The resource contains 54,000 manually annotated entities,
mapped to 19 fine-grained semantic classes: person, judge, lawyer, country,
city, street, landscape, organization, company, institution, court, brand, law,
ordinance, European legal norm, regulation, contract, court decision, and legal
literature. The legal documents were, furthermore, automatically annotated with
more than 35,000 TimeML-based time expressions. The dataset, which is available
under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training
an NER service for German legal documents in the EU project Lynx. | 2020-03-29T13:20:43Z | Proceedings of the 12th Language Resources and Evaluation Conference
(LREC 2020). To appear | null | null | A Dataset of German Legal Documents for Named Entity Recognition | ['Elena Leitner', 'Georg Rehm', 'J. Moreno-Schneider'] | 2,020 | International Conference on Language Resources and Evaluation | 51 | 39 | ['Computer Science'] |
2,003.13145 | Can AI help in screening Viral and COVID-19 pneumonia? | ['Muhammad E. H. Chowdhury', 'Tawsifur Rahman', 'Amith Khandakar', 'Rashid Mazhar', 'Muhammad Abdul Kadir', 'Zaid Bin Mahbub', 'Khandaker Reajul Islam', 'Muhammad Salman Khan', 'Atif Iqbal', 'Nasser Al-Emadi', 'Mamun Bin Ibne Reaz', 'T. I. Islam'] | ['cs.LG', 'cs.CV'] | Coronavirus disease (COVID-19) is a pandemic disease, which has already
caused thousands of causalities and infected several millions of people
worldwide. Any technological tool enabling rapid screening of the COVID-19
infection with high accuracy can be crucially helpful to healthcare
professionals. The main clinical tool currently in use for the diagnosis of
COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which
is expensive, less-sensitive and requires specialized medical personnel. X-ray
imaging is an easily accessible tool that can be an excellent alternative in
the COVID-19 diagnosis. This research was taken to investigate the utility of
artificial intelligence (AI) in the rapid and accurate detection of COVID-19
from chest X-ray images. The aim of this paper is to propose a robust technique
for automatic detection of COVID-19 pneumonia from digital chest X-ray images
applying pre-trained deep-learning algorithms while maximizing the detection
accuracy. A public database was created by the authors combining several public
databases and also by collecting images from recently published articles. The
database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579
normal chest X-ray images. Transfer learning technique was used with the help
of image augmentation to train and validate several pre-trained deep
Convolutional Neural Networks (CNNs). The networks were trained to classify two
different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and
COVID-19 pneumonia with and without image augmentation. The classification
accuracy, precision, sensitivity, and specificity for both the schemes were
99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%,
respectively. | 2020-03-29T21:37:21Z | 12 pages, 9 Figures | IEEE Access 2020 | 10.1109/ACCESS.2020.3010287 | Can AI Help in Screening Viral and COVID-19 Pneumonia? | ['M. Chowdhury', 'Tawsifur Rahman', 'A. Khandakar', 'R. Mazhar', 'M. A. Kadir', 'Z. Mahbub', 'Khandakar R. Islam', 'Muhammad Salman Khan', 'A. Iqbal', 'N. Al-Emadi', 'M. Reaz'] | 2,020 | IEEE Access | 1,356 | 74 | ['Computer Science', 'Engineering'] |
2,003.1363 | TResNet: High Performance GPU-Dedicated Architecture | ['Tal Ridnik', 'Hussam Lawen', 'Asaf Noy', 'Emanuel Ben Baruch', 'Gilad Sharir', 'Itamar Friedman'] | ['cs.CV', 'cs.LG', 'eess.IV'] | Many deep learning models, developed in recent years, reach higher ImageNet
accuracy than ResNet50, with fewer or comparable FLOPS count. While FLOPs are
often seen as a proxy for network efficiency, when measuring actual GPU
training and inference throughput, vanilla ResNet50 is usually significantly
faster than its recent competitors, offering better throughput-accuracy
trade-off.
In this work, we introduce a series of architecture modifications that aim to
boost neural networks' accuracy, while retaining their GPU training and
inference efficiency. We first demonstrate and discuss the bottlenecks induced
by FLOPs-optimizations. We then suggest alternative designs that better utilize
GPU structure and assets. Finally, we introduce a new family of GPU-dedicated
models, called TResNet, which achieve better accuracy and efficiency than
previous ConvNets.
Using a TResNet model, with similar GPU throughput to ResNet50, we reach 80.8
top-1 accuracy on ImageNet. Our TResNet models also transfer well and achieve
state-of-the-art accuracy on competitive single-label classification datasets
such as Stanford cars (96.0%), CIFAR-10 (99.0%), CIFAR-100 (91.5%) and
Oxford-Flowers (99.1%). They also perform well on multi-label classification
and object detection tasks. Implementation is available at:
https://github.com/mrT23/TResNet. | 2020-03-30T17:04:47Z | 11 pages, 5 figures | null | null | null | null | null | null | null | null | null |
2,003.13678 | Designing Network Design Spaces | ['Ilija Radosavovic', 'Raj Prateek Kosaraju', 'Ross Girshick', 'Kaiming He', 'Piotr Dollár'] | ['cs.CV', 'cs.LG'] | In this work, we present a new network design paradigm. Our goal is to help
advance the understanding of network design and discover design principles that
generalize across settings. Instead of focusing on designing individual network
instances, we design network design spaces that parametrize populations of
networks. The overall process is analogous to classic manual design of
networks, but elevated to the design space level. Using our methodology we
explore the structure aspect of network design and arrive at a low-dimensional
design space consisting of simple, regular networks that we call RegNet. The
core insight of the RegNet parametrization is surprisingly simple: widths and
depths of good networks can be explained by a quantized linear function. We
analyze the RegNet design space and arrive at interesting findings that do not
match the current practice of network design. The RegNet design space provides
simple and fast networks that work well across a wide range of flop regimes.
Under comparable training settings and flops, the RegNet models outperform the
popular EfficientNet models while being up to 5x faster on GPUs. | 2020-03-30T17:57:47Z | CVPR 2020 | null | null | null | null | null | null | null | null | null |
2,004.00033 | Give your Text Representation Models some Love: the Case for Basque | ['Rodrigo Agerri', 'Iñaki San Vicente', 'Jon Ander Campos', 'Ander Barrena', 'Xabier Saralegi', 'Aitor Soroa', 'Eneko Agirre'] | ['cs.CL'] | Word embeddings and pre-trained language models allow to build rich
representations of text and have enabled improvements across most NLP tasks.
Unfortunately they are very expensive to train, and many small companies and
research groups tend to use models that have been pre-trained and made
available by third parties, rather than building their own. This is suboptimal
as, for many languages, the models have been trained on smaller (or lower
quality) corpora. In addition, monolingual pre-trained models for non-English
languages are not always available. At best, models for those languages are
included in multilingual versions, where each language shares the quota of
substrings and parameters with the rest of the languages. This is particularly
true for smaller languages such as Basque. In this paper we show that a number
of monolingual models (FastText word embeddings, FLAIR and BERT language
models) trained with larger Basque corpora produce much better results than
publicly available versions in downstream NLP tasks, including topic
classification, sentiment classification, PoS tagging and NER. This work sets a
new state-of-the-art in those tasks for Basque. All benchmarks and models used
in this work are publicly available. | 2020-03-31T18:01:56Z | Accepted at LREC 2020; 8 pages, 7 tables | null | null | Give your Text Representation Models some Love: the Case for Basque | ['Rodrigo Agerri', 'Iñaki San Vicente', 'Jon Ander Campos', 'Ander Barrena', 'X. Saralegi', 'Aitor Soroa Etxabe', 'Eneko Agirre'] | 2,020 | International Conference on Language Resources and Evaluation | 63 | 24 | ['Computer Science'] |
2,004.00584 | Deep Entity Matching with Pre-Trained Language Models | ['Yuliang Li', 'Jinfeng Li', 'Yoshihiko Suhara', 'AnHai Doan', 'Wang-Chiew Tan'] | ['cs.DB', 'cs.CL'] | We present Ditto, a novel entity matching system based on pre-trained
Transformer-based language models. We fine-tune and cast EM as a sequence-pair
classification problem to leverage such models with a simple architecture. Our
experiments show that a straightforward application of language models such as
BERT, DistilBERT, or RoBERTa pre-trained on large text corpora already
significantly improves the matching quality and outperforms previous
state-of-the-art (SOTA), by up to 29% of F1 score on benchmark datasets. We
also developed three optimization techniques to further improve Ditto's
matching capability. Ditto allows domain knowledge to be injected by
highlighting important pieces of input information that may be of interest when
making matching decisions. Ditto also summarizes strings that are too long so
that only the essential information is retained and used for EM. Finally, Ditto
adapts a SOTA technique on data augmentation for text to EM to augment the
training data with (difficult) examples. This way, Ditto is forced to learn
"harder" to improve the model's matching capability. The optimizations we
developed further boost the performance of Ditto by up to 9.8%. Perhaps more
surprisingly, we establish that Ditto can achieve the previous SOTA results
with at most half the number of labeled data. Finally, we demonstrate Ditto's
effectiveness on a real-world large-scale EM task. On matching two company
datasets consisting of 789K and 412K records, Ditto achieves a high F1 score of
96.5%. | 2020-04-01T17:14:10Z | To appear in VLDB 2021 | null | 10.14778/3421424.3421431 | Deep entity matching with pre-trained language models | ['Yuliang Li', 'Jinfeng Li', 'Yoshihiko Suhara', 'A. Doan', 'W. Tan'] | 2,020 | Proceedings of the VLDB Endowment | 391 | 68 | ['Computer Science'] |
2,004.01092 | NUBES: A Corpus of Negation and Uncertainty in Spanish Clinical Texts | ['Salvador Lima', 'Naiara Perez', 'Montse Cuadros', 'German Rigau'] | ['cs.CL'] | This paper introduces the first version of the NUBes corpus (Negation and
Uncertainty annotations in Biomedical texts in Spanish). The corpus is part of
an on-going research and currently consists of 29,682 sentences obtained from
anonymised health records annotated with negation and uncertainty. The article
includes an exhaustive comparison with similar corpora in Spanish, and presents
the main annotation and design decisions. Additionally, we perform preliminary
experiments using deep learning algorithms to validate the annotated dataset.
As far as we know, NUBes is the largest publicly available corpus for negation
in Spanish and the first that also incorporates the annotation of speculation
cues, scopes, and events. | 2020-04-02T15:51:31Z | Accepted at the Twelfth International Conference on Language
Resources and Evaluation (LREC 2020) | null | null | null | null | null | null | null | null | null |
2,004.01401 | XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training,
Understanding and Generation | ['Yaobo Liang', 'Nan Duan', 'Yeyun Gong', 'Ning Wu', 'Fenfei Guo', 'Weizhen Qi', 'Ming Gong', 'Linjun Shou', 'Daxin Jiang', 'Guihong Cao', 'Xiaodong Fan', 'Ruofei Zhang', 'Rahul Agrawal', 'Edward Cui', 'Sining Wei', 'Taroon Bharti', 'Ying Qiao', 'Jiun-Hung Chen', 'Winnie Wu', 'Shuguang Liu', 'Fan Yang', 'Daniel Campos', 'Rangan Majumder', 'Ming Zhou'] | ['cs.CL'] | In this paper, we introduce XGLUE, a new benchmark dataset that can be used
to train large-scale cross-lingual pre-trained models using multilingual and
bilingual corpora and evaluate their performance across a diverse set of
cross-lingual tasks. Comparing to GLUE(Wang et al., 2019), which is labeled in
English for natural language understanding tasks only, XGLUE has two main
advantages: (1) it provides 11 diversified tasks that cover both natural
language understanding and generation scenarios; (2) for each task, it provides
labeled data in multiple languages. We extend a recent cross-lingual
pre-trained model Unicoder(Huang et al., 2019) to cover both understanding and
generation tasks, which is evaluated on XGLUE as a strong baseline. We also
evaluate the base versions (12-layer) of Multilingual BERT, XLM and XLM-R for
comparison. | 2020-04-03T07:03:12Z | null | null | null | null | null | null | null | null | null | null |
2,004.01804 | Google Landmarks Dataset v2 -- A Large-Scale Benchmark for
Instance-Level Recognition and Retrieval | ['Tobias Weyand', 'Andre Araujo', 'Bingyi Cao', 'Jack Sim'] | ['cs.CV'] | While image retrieval and instance recognition techniques are progressing
rapidly, there is a need for challenging datasets to accurately measure their
performance -- while posing novel challenges that are relevant for practical
applications. We introduce the Google Landmarks Dataset v2 (GLDv2), a new
benchmark for large-scale, fine-grained instance recognition and image
retrieval in the domain of human-made and natural landmarks. GLDv2 is the
largest such dataset to date by a large margin, including over 5M images and
200k distinct instance labels. Its test set consists of 118k images with ground
truth annotations for both the retrieval and recognition tasks. The ground
truth construction involved over 800 hours of human annotator work. Our new
dataset has several challenging properties inspired by real world applications
that previous datasets did not consider: An extremely long-tailed class
distribution, a large fraction of out-of-domain test photos and large
intra-class variability. The dataset is sourced from Wikimedia Commons, the
world's largest crowdsourced collection of landmark photos. We provide baseline
results for both recognition and retrieval tasks based on state-of-the-art
methods as well as competitive results from a public challenge. We further
demonstrate the suitability of the dataset for transfer learning by showing
that image embeddings trained on it achieve competitive retrieval performance
on independent datasets. The dataset images, ground-truth and metric scoring
code are available at https://github.com/cvdfoundation/google-landmark. | 2020-04-03T22:52:17Z | CVPR20 camera-ready (oral) + appendices | null | null | Google Landmarks Dataset v2 – A Large-Scale Benchmark for Instance-Level Recognition and Retrieval | ['Tobias Weyand', 'A. Araújo', 'Bingyi Cao', 'Jack Sim'] | 2,020 | Computer Vision and Pattern Recognition | 373 | 70 | ['Computer Science'] |
2,004.02349 | TAPAS: Weakly Supervised Table Parsing via Pre-training | ['Jonathan Herzig', 'Paweł Krzysztof Nowak', 'Thomas Müller', 'Francesco Piccinno', 'Julian Martin Eisenschlos'] | ['cs.IR', 'cs.AI', 'cs.CL', 'cs.LG'] | Answering natural language questions over tables is usually seen as a
semantic parsing task. To alleviate the collection cost of full logical forms,
one popular approach focuses on weak supervision consisting of denotations
instead of logical forms. However, training semantic parsers from weak
supervision poses difficulties, and in addition, the generated logical forms
are only used as an intermediate step prior to retrieving the denotation. In
this paper, we present TAPAS, an approach to question answering over tables
without generating logical forms. TAPAS trains from weak supervision, and
predicts the denotation by selecting table cells and optionally applying a
corresponding aggregation operator to such selection. TAPAS extends BERT's
architecture to encode tables as input, initializes from an effective joint
pre-training of text segments and tables crawled from Wikipedia, and is trained
end-to-end. We experiment with three different semantic parsing datasets, and
find that TAPAS outperforms or rivals semantic parsing models by improving
state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with
the state-of-the-art on WIKISQL and WIKITQ, but with a simpler model
architecture. We additionally find that transfer learning, which is trivial in
our setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the
state-of-the-art. | 2020-04-05T23:18:37Z | Accepted to ACL 2020 | null | 10.18653/v1/2020.acl-main.398 | null | null | null | null | null | null | null |
2,004.02814 | Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job
Ontology Expansion | ['Jeroen Van Hautte', 'Vincent Schelstraete', 'Mikaël Wornoo'] | ['cs.CL', 'cs.LG'] | Machine learning plays an ever-bigger part in online recruitment, powering
intelligent matchmaking and job recommendations across many of the world's
largest job platforms. However, the main text is rarely enough to fully
understand a job posting: more often than not, much of the required information
is condensed into the job title. Several organised efforts have been made to
map job titles onto a hand-made knowledge base as to provide this information,
but these only cover around 60\% of online vacancies. We introduce a novel,
purely data-driven approach towards the detection of new job titles. Our method
is conceptually simple, extremely efficient and competitive with traditional
NER-based approaches. Although the standalone application of our method does
not outperform a finetuned BERT model, it can be applied as a preprocessing
step as well, substantially boosting accuracy across several architectures. | 2020-04-06T16:55:41Z | Accepted to the Proceedings of the 6th International Workshop on
Computational Terminology (COMPUTERM 2020) | null | null | Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion | ['Jeroen Van Hautte', 'Vincent Schelstraete', 'Mikael Wornoo'] | 2,020 | COMPUTERM | 4 | 16 | ['Computer Science'] |
2,004.02967 | Evolving Normalization-Activation Layers | ['Hanxiao Liu', 'Andrew Brock', 'Karen Simonyan', 'Quoc V. Le'] | ['cs.LG', 'cs.CV', 'cs.NE', 'stat.ML'] | Normalization layers and activation functions are fundamental components in
deep networks and typically co-locate with each other. Here we propose to
design them using an automated approach. Instead of designing them separately,
we unify them into a single tensor-to-tensor computation graph, and evolve its
structure starting from basic mathematical functions. Examples of such
mathematical functions are addition, multiplication and statistical moments.
The use of low-level mathematical functions, in contrast to the use of
high-level modules in mainstream NAS, leads to a highly sparse and large search
space which can be challenging for search methods. To address the challenge, we
develop efficient rejection protocols to quickly filter out candidate layers
that do not work well. We also use multi-objective evolution to optimize each
layer's performance across many architectures to prevent overfitting. Our
method leads to the discovery of EvoNorms, a set of new
normalization-activation layers with novel, and sometimes surprising structures
that go beyond existing design patterns. For example, some EvoNorms do not
assume that normalization and activation functions must be applied
sequentially, nor need to center the feature maps, nor require explicit
activation functions. Our experiments show that EvoNorms work well on image
classification models including ResNets, MobileNets and EfficientNets but also
transfer well to Mask R-CNN with FPN/SpineNet for instance segmentation and to
BigGAN for image synthesis, outperforming BatchNorm and GroupNorm based layers
in many cases. | 2020-04-06T19:52:48Z | null | null | null | null | null | null | null | null | null | null |
2,004.02984 | MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices | ['Zhiqing Sun', 'Hongkun Yu', 'Xiaodan Song', 'Renjie Liu', 'Yiming Yang', 'Denny Zhou'] | ['cs.CL', 'cs.LG'] | Natural Language Processing (NLP) has recently achieved great success by
using huge pre-trained models with hundreds of millions of parameters. However,
these models suffer from heavy model sizes and high latency such that they
cannot be deployed to resource-limited mobile devices. In this paper, we
propose MobileBERT for compressing and accelerating the popular BERT model.
Like the original BERT, MobileBERT is task-agnostic, that is, it can be
generically applied to various downstream NLP tasks via simple fine-tuning.
Basically, MobileBERT is a thin version of BERT_LARGE, while equipped with
bottleneck structures and a carefully designed balance between self-attentions
and feed-forward networks. To train MobileBERT, we first train a specially
designed teacher model, an inverted-bottleneck incorporated BERT_LARGE model.
Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical
studies show that MobileBERT is 4.3x smaller and 5.5x faster than BERT_BASE
while achieving competitive results on well-known benchmarks. On the natural
language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7 (0.6
lower than BERT_BASE), and 62 ms latency on a Pixel 4 phone. On the SQuAD
v1.1/v2.0 question answering task, MobileBERT achieves a dev F1 score of
90.0/79.2 (1.5/2.1 higher than BERT_BASE). | 2020-04-06T20:20:58Z | Accepted to ACL 2020 | null | null | MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices | ['Zhiqing Sun', 'Hongkun Yu', 'Xiaodan Song', 'Renjie Liu', 'Yiming Yang', 'Denny Zhou'] | 2,020 | Annual Meeting of the Association for Computational Linguistics | 820 | 66 | ['Computer Science'] |
2,004.03289 | KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language
Understanding | ['Jiyeon Ham', 'Yo Joong Choe', 'Kyubyong Park', 'Ilji Choi', 'Hyungjoon Soh'] | ['cs.CL'] | Natural language inference (NLI) and semantic textual similarity (STS) are
key tasks in natural language understanding (NLU). Although several benchmark
datasets for those tasks have been released in English and a few other
languages, there are no publicly available NLI or STS datasets in the Korean
language. Motivated by this, we construct and release new datasets for Korean
NLI and STS, dubbed KorNLI and KorSTS, respectively. Following previous
approaches, we machine-translate existing English training sets and manually
translate development and test sets into Korean. To accelerate research on
Korean NLU, we also establish baselines on KorNLI and KorSTS. Our datasets are
publicly available at https://github.com/kakaobrain/KorNLUDatasets. | 2020-04-07T11:49:15Z | Findings of EMNLP 2020. Datasets available at
https://github.com/kakaobrain/KorNLUDatasets | null | null | null | null | null | null | null | null | null |
2,004.03329 | MedDialog: Two Large-scale Medical Dialogue Datasets | ['Xuehai He', 'Shu Chen', 'Zeqian Ju', 'Xiangyu Dong', 'Hongchao Fang', 'Sicheng Wang', 'Yue Yang', 'Jiaqi Zeng', 'Ruisi Zhang', 'Ruoyu Zhang', 'Meng Zhou', 'Penghui Zhu', 'Pengtao Xie'] | ['cs.LG', 'cs.AI', 'cs.CL', 'stat.ML'] | Medical dialogue systems are promising in assisting in telemedicine to
increase access to healthcare services, improve the quality of patient care,
and reduce medical costs. To facilitate the research and development of medical
dialogue systems, we build two large-scale medical dialogue datasets:
MedDialog-EN and MedDialog-CN. MedDialog-EN is an English dataset containing
0.3 million conversations between patients and doctors and 0.5 million
utterances. MedDialog-CN is an Chinese dataset containing 1.1 million
conversations and 4 million utterances. To our best knowledge,
MedDialog-(EN,CN) are the largest medical dialogue datasets to date. The
dataset is available at https://github.com/UCSD-AI4H/Medical-Dialogue-System | 2020-04-07T13:07:09Z | null | null | null | MedDialog: A Large-scale Medical Dialogue Dataset | ['Shu Chen', 'Zeqian Ju', 'Xiangyu Dong', 'Hongchao Fang', 'Sicheng Wang', 'Yue Yang', 'Jiaqi Zeng', 'Ruisi Zhang', 'Ruoyu Zhang', 'Meng Zhou', 'Penghui Zhu', 'Pengtao Xie'] | 2,020 | arXiv.org | 179 | 2 | ['Computer Science', 'Mathematics'] |
2,004.03659 | The Russian Drug Reaction Corpus and Neural Models for Drug Reactions
and Effectiveness Detection in User Reviews | ['Elena Tutubalina', 'Ilseyar Alimova', 'Zulfat Miftahutdinov', 'Andrey Sakhovskiy', 'Valentin Malykh', 'Sergey Nikolenko'] | ['cs.CL'] | The Russian Drug Reaction Corpus (RuDReC) is a new partially annotated corpus
of consumer reviews in Russian about pharmaceutical products for the detection
of health-related named entities and the effectiveness of pharmaceutical
products. The corpus itself consists of two parts, the raw one and the labelled
one. The raw part includes 1.4 million health-related user-generated texts
collected from various Internet sources, including social media. The labelled
part contains 500 consumer reviews about drug therapy with drug- and
disease-related information. Labels for sentences include health-related issues
or their absence. The sentences with one are additionally labelled at the
expression level for identification of fine-grained subtypes such as drug
classes and drug forms, drug indications, and drug reactions. Further, we
present a baseline model for named entity recognition (NER) and multi-label
sentence classification tasks on this corpus. The macro F1 score of 74.85% in
the NER task was achieved by our RuDR-BERT model. For the sentence
classification task, our model achieves the macro F1 score of 68.82% gaining
7.47% over the score of BERT model trained on Russian data. We make the RuDReC
corpus and pretrained weights of domain-specific BERT models freely available
at https://github.com/cimm-kzn/RuDReC | 2020-04-07T19:26:13Z | 9 pages, 9 tables, 4 figures | Bioinformatics, 2020 | 10.1093/bioinformatics/btaa675 | The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews | ['E. Tutubalina', 'I. Alimova', 'Z. Miftahutdinov', 'Andrey Sakhovskiy', 'Valentin Malykh', 'S. Nikolenko'] | 2,020 | Bioinform. | 44 | 26 | ['Computer Science', 'Medicine'] |
2,004.0372 | Byte Pair Encoding is Suboptimal for Language Model Pretraining | ['Kaj Bostrom', 'Greg Durrett'] | ['cs.CL', 'I.2.7'] | The success of pretrained transformer language models (LMs) in natural
language processing has led to a wide range of pretraining setups. In
particular, these models employ a variety of subword tokenization methods, most
notably byte-pair encoding (BPE) (Sennrich et al., 2016; Gage, 1994), the
WordPiece method (Schuster and Nakajima, 2012), and unigram language modeling
(Kudo, 2018), to segment text. However, to the best of our knowledge, the
literature does not contain a direct evaluation of the impact of tokenization
on language model pretraining. We analyze differences between BPE and unigram
LM tokenization, finding that the latter method recovers subword units that
align more closely with morphology and avoids problems stemming from BPE's
greedy construction procedure. We then compare the fine-tuned task performance
of identical transformer masked language models pretrained with these
tokenizations. Across downstream tasks and two languages (English and
Japanese), we find that the unigram LM tokenization method matches or
outperforms BPE. We hope that developers of future pretrained LMs will consider
adopting the unigram LM method over the more prevalent BPE. | 2020-04-07T21:21:06Z | 5 pages, 3 figures. To be published in Findings of EMNLP 2020 | null | null | Byte Pair Encoding is Suboptimal for Language Model Pretraining | ['Kaj Bostrom', 'Greg Durrett'] | 2,020 | Findings | 215 | 28 | ['Computer Science'] |
2,004.04037 | DynaBERT: Dynamic BERT with Adaptive Width and Depth | ['Lu Hou', 'Zhiqi Huang', 'Lifeng Shang', 'Xin Jiang', 'Xiao Chen', 'Qun Liu'] | ['cs.CL', 'cs.LG'] | The pre-trained language models like BERT, though powerful in many natural
language processing tasks, are both computation and memory expensive. To
alleviate this problem, one approach is to compress them for specific tasks
before deployment. However, recent works on BERT compression usually compress
the large BERT model to a fixed smaller size. They can not fully satisfy the
requirements of different edge devices with various hardware performances. In
this paper, we propose a novel dynamic BERT model (abbreviated as DynaBERT),
which can flexibly adjust the size and latency by selecting adaptive width and
depth. The training process of DynaBERT includes first training a
width-adaptive BERT and then allowing both adaptive width and depth, by
distilling knowledge from the full-sized model to small sub-networks. Network
rewiring is also used to keep the more important attention heads and neurons
shared by more sub-networks. Comprehensive experiments under various efficiency
constraints demonstrate that our proposed dynamic BERT (or RoBERTa) at its
largest size has comparable performance as BERT-base (or RoBERTa-base), while
at smaller widths and depths consistently outperforms existing BERT compression
methods. Code is available at
https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/DynaBERT. | 2020-04-08T15:06:28Z | NeurIPS-2020 (Spotlight) | null | null | null | null | null | null | null | null | null |
2,004.0427 | The Spotify Podcast Dataset | ['Ann Clifton', 'Aasish Pappu', 'Sravana Reddy', 'Yongze Yu', 'Jussi Karlgren', 'Ben Carterette', 'Rosie Jones'] | ['cs.CL'] | Podcasts are a relatively new form of audio media. Episodes appear on a
regular cadence, and come in many different formats and levels of formality.
They can be formal news journalism or conversational chat; fiction or
non-fiction. They are rapidly growing in popularity and yet have been
relatively little studied. As an audio format, podcasts are more varied in
style and production types than, say, broadcast news, and contain many more
genres than typically studied in video research. The medium is therefore a rich
domain with many research avenues for the IR and NLP communities. We present
the Spotify Podcast Dataset, a set of approximately 100K podcast episodes
comprised of raw audio files along with accompanying ASR transcripts. This
represents over 47,000 hours of transcribed audio, and is an order of magnitude
larger than previous speech-to-text corpora. | 2020-04-08T21:25:00Z | 4 pages, 3 figures | null | null | null | null | null | null | null | null | null |
2,004.04315 | Large Arabic Twitter Dataset on COVID-19 | ['Sarah Alqurashi', 'Ahmad Alhindi', 'Eisa Alanazi'] | ['cs.SI', 'cs.CL'] | The 2019 coronavirus disease (COVID-19), emerged late December 2019 in China,
is now rapidly spreading across the globe. At the time of writing this paper,
the number of global confirmed cases has passed two millions and half with over
180,000 fatalities. Many countries have enforced strict social distancing
policies to contain the spread of the virus. This have changed the daily life
of tens of millions of people, and urged people to turn their discussions
online, e.g., via online social media sites like Twitter. In this work, we
describe the first Arabic tweets dataset on COVID-19 that we have been
collecting since January 1st, 2020. The dataset would help researchers and
policy makers in studying different societal issues related to the pandemic.
Many other tasks related to behavioral change, information sharing,
misinformation and rumors spreading can also be analyzed. | 2020-04-09T01:07:12Z | null | null | null | null | null | null | null | null | null | null |
2,004.0441 | Att-HACK: An Expressive Speech Database with Social Attitudes | ['Clément Le Moine', 'Nicolas Obin'] | ['eess.AS'] | This paper presents Att-HACK, the first large database of acted speech with
social attitudes. Available databases of expressive speech are rare and very
often restricted to the primary emotions: anger, joy, sadness, fear. This
greatly limits the scope of the research on expressive speech. Besides, a
fundamental aspect of speech prosody is always ignored and missing from such
databases: its variety, i.e. the possibility to repeat an utterance while
varying its prosody. This paper represents a first attempt to widen the scope
of expressivity in speech, by providing a database of acted speech with social
attitudes: friendly, seductive, dominant, and distant. The proposed database
comprises 25 speakers interpreting 100 utterances in 4 social attitudes, with
3-5 repetitions each per attitude for a total of around 30 hours of speech. The
Att-HACK is freely available for academic research under a Creative Commons
Licence. | 2020-04-09T08:09:59Z | 5 pages, 5 figures | null | null | Att-HACK: An Expressive Speech Database with Social Attitudes | ['Clément Le Moine', 'Nicolas Obin'] | 2,020 | Proceedings of the International Conference on Speech Prosody | 19 | 35 | ['Engineering', 'Computer Science', 'Psychology'] |
2,004.0446 | PANDORA Talks: Personality and Demographics on Reddit | ['Matej Gjurković', 'Mladen Karan', 'Iva Vukojević', 'Mihaela Bošnjak', 'Jan Šnajder'] | ['cs.CL', 'cs.CY', 'cs.SI'] | Personality and demographics are important variables in social sciences,
while in NLP they can aid in interpretability and removal of societal biases.
However, datasets with both personality and demographic labels are scarce. To
address this, we present PANDORA, the first large-scale dataset of Reddit
comments labeled with three personality models (including the well-established
Big 5 model) and demographics (age, gender, and location) for more than 10k
users. We showcase the usefulness of this dataset on three experiments, where
we leverage the more readily available data from other personality models to
predict the Big 5 traits, analyze gender classification biases arising from
psycho-demographic variables, and carry out a confirmatory and exploratory
analysis based on psychological theories. Finally, we present benchmark
prediction models for all personality and demographic variables. | 2020-04-09T10:08:05Z | Proceedings of the Ninth International Workshop on Natural Language
Processing for Social Media, NAACL 2021,
https://www.aclweb.org/anthology/2021.socialnlp-1.12 | null | null | null | null | null | null | null | null | null |
2,004.04696 | BLEURT: Learning Robust Metrics for Text Generation | ['Thibault Sellam', 'Dipanjan Das', 'Ankur P. Parikh'] | ['cs.CL'] | Text generation has made significant advances in the last few years. Yet,
evaluation metrics have lagged behind, as the most popular choices (e.g., BLEU
and ROUGE) may correlate poorly with human judgments. We propose BLEURT, a
learned evaluation metric based on BERT that can model human judgments with a
few thousand possibly biased training examples. A key aspect of our approach is
a novel pre-training scheme that uses millions of synthetic examples to help
the model generalize. BLEURT provides state-of-the-art results on the last
three years of the WMT Metrics shared task and the WebNLG Competition dataset.
In contrast to a vanilla BERT-based approach, it yields superior results even
when the training data is scarce and out-of-distribution. | 2020-04-09T17:26:52Z | Accepted at ACL 2020 | null | null | BLEURT: Learning Robust Metrics for Text Generation | ['Thibault Sellam', 'Dipanjan Das', 'Ankur P. Parikh'] | 2,020 | Annual Meeting of the Association for Computational Linguistics | 1,511 | 52 | ['Computer Science'] |
2,004.04906 | Dense Passage Retrieval for Open-Domain Question Answering | ['Vladimir Karpukhin', 'Barlas Oğuz', 'Sewon Min', 'Patrick Lewis', 'Ledell Wu', 'Sergey Edunov', 'Danqi Chen', 'Wen-tau Yih'] | ['cs.CL'] | Open-domain question answering relies on efficient passage retrieval to
select candidate contexts, where traditional sparse vector space models, such
as TF-IDF or BM25, are the de facto method. In this work, we show that
retrieval can be practically implemented using dense representations alone,
where embeddings are learned from a small number of questions and passages by a
simple dual-encoder framework. When evaluated on a wide range of open-domain QA
datasets, our dense retriever outperforms a strong Lucene-BM25 system largely
by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our
end-to-end QA system establish new state-of-the-art on multiple open-domain QA
benchmarks. | 2020-04-10T04:53:17Z | EMNLP 2020 | null | null | null | null | null | null | null | null | null |
2,004.0515 | Longformer: The Long-Document Transformer | ['Iz Beltagy', 'Matthew E. Peters', 'Arman Cohan'] | ['cs.CL'] | Transformer-based models are unable to process long sequences due to their
self-attention operation, which scales quadratically with the sequence length.
To address this limitation, we introduce the Longformer with an attention
mechanism that scales linearly with sequence length, making it easy to process
documents of thousands of tokens or longer. Longformer's attention mechanism is
a drop-in replacement for the standard self-attention and combines a local
windowed attention with a task motivated global attention. Following prior work
on long-sequence transformers, we evaluate Longformer on character-level
language modeling and achieve state-of-the-art results on text8 and enwik8. In
contrast to most prior work, we also pretrain Longformer and finetune it on a
variety of downstream tasks. Our pretrained Longformer consistently outperforms
RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop
and TriviaQA. We finally introduce the Longformer-Encoder-Decoder (LED), a
Longformer variant for supporting long document generative sequence-to-sequence
tasks, and demonstrate its effectiveness on the arXiv summarization dataset. | 2020-04-10T17:54:09Z | Version 2 introduces the Longformer-Encoder-Decoder (LED) model | null | null | null | null | null | null | null | null | null |
2,004.05665 | Minimizing FLOPs to Learn Efficient Sparse Representations | ['Biswajit Paria', 'Chih-Kuan Yeh', 'Ian E. H. Yen', 'Ning Xu', 'Pradeep Ravikumar', 'Barnabás Póczos'] | ['cs.LG', 'stat.ML'] | Deep representation learning has become one of the most widely adopted
approaches for visual search, recommendation, and identification. Retrieval of
such representations from a large database is however computationally
challenging. Approximate methods based on learning compact representations,
have been widely explored for this problem, such as locality sensitive hashing,
product quantization, and PCA. In this work, in contrast to learning compact
representations, we propose to learn high dimensional and sparse
representations that have similar representational capacity as dense embeddings
while being more efficient due to sparse matrix multiplication operations which
can be much faster than dense multiplication. Following the key insight that
the number of operations decreases quadratically with the sparsity of
embeddings provided the non-zero entries are distributed uniformly across
dimensions, we propose a novel approach to learn such distributed sparse
embeddings via the use of a carefully constructed regularization function that
directly minimizes a continuous relaxation of the number of floating-point
operations (FLOPs) incurred during retrieval. Our experiments show that our
approach is competitive to the other baselines and yields a similar or better
speed-vs-accuracy tradeoff on practical datasets. | 2020-04-12T18:09:02Z | Published at ICLR 2020 | null | null | Minimizing FLOPs to Learn Efficient Sparse Representations | ['Biswajit Paria', 'Chih-Kuan Yeh', 'N. Xu', 'B. Póczos', 'Pradeep Ravikumar', 'I. E. Yen'] | 2,020 | International Conference on Learning Representations | 69 | 90 | ['Computer Science', 'Mathematics'] |
2,004.05707 | VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification | ['Zhibin Lu', 'Pan Du', 'Jian-Yun Nie'] | ['cs.CL', 'cs.LG', 'stat.ML', 'I.2.4; I.2.7'] | Much progress has been made recently on text classification with methods
based on neural networks. In particular, models using attention mechanism such
as BERT have shown to have the capability of capturing the contextual
information within a sentence or document. However, their ability of capturing
the global information about the vocabulary of a language is more limited. This
latter is the strength of Graph Convolutional Networks (GCN). In this paper, we
propose VGCN-BERT model which combines the capability of BERT with a Vocabulary
Graph Convolutional Network (VGCN). Local information and global information
interact through different layers of BERT, allowing them to influence mutually
and to build together a final representation for classification. In our
experiments on several text classification datasets, our approach outperforms
BERT and GCN alone, and achieve higher effectiveness than that reported in
previous studies. | 2020-04-12T22:02:33Z | 12 pages, 2 figures | in J. M. Jose et al. (Eds.): ECIR 2020, LNCS 12035, pp.369-382,
2020 | null | VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification | ['Zhibin Lu', 'Pan Du', 'J. Nie'] | 2,020 | European Conference on Information Retrieval | 127 | 34 | ['Computer Science'] |
2,004.06364 | Polar nano-clusters in nominally paraelectric ceramics demonstrating
high microwave tunability for wireless communication | ['Hangfeng Zhang', 'Henry Giddens', 'Yajun Yue', 'Xinzhao Xu', 'Vicente Araullo-Peters', 'Vladimir Koval', 'Matteo Palma', 'Isaac Abrahams', 'Haixue Yan', 'Yang Hao'] | ['physics.app-ph', 'cond-mat.mtrl-sci'] | Dielectric materials, with high tunability at microwave frequencies, are key
components in the design of microwave communication systems. Dense
Ba0.6Sr0.4TiO3 (BST) ceramics, with different grain sizes, were prepared in
order to optimise the dielectric tunability via polar nano cluster effects.
Dielectric permittivity and loss measurements were carried at both high and low
frequencies and were supported by results from X-ray powder diffraction,
scanning and transmission electron microscopies, Raman spectroscopy and
piezoresponse force microscopy. The concentration of polar nano clusters, whose
sizes are found to be in the range 20 to 50 nm, and the dielectric tunability
increase with increasing grain size. A novel method for measurement of the
microwave tunability in bulk dielectrics is presented. The highest tunability
of 32% is achieved in ceramics with an average grain size of 10 um. The
tunability of BST ceramics with applied DC field is demonstrated in a prototype
small resonant antenna. | 2020-04-14T09:00:33Z | 25pages, 6 figures | Journal of the European Ceramic Society,2020 | null | Polar nano-clusters in nominally paraelectric ceramics demonstrating high microwave tunability for wireless communication | ['Hangfeng Zhang', 'H. Giddens', 'Y. Yue', 'Xinzhao Xu', 'V. Araullo-Peters', 'V. Koval', 'M. Palma', 'I. Abrahams', 'Haixue Yan', 'Y. Hao'] | 2,020 | null | 37 | 45 | ['Materials Science', 'Physics'] |
2,004.06465 | Deep Learning Models for Multilingual Hate Speech Detection | ['Sai Saketh Aluru', 'Binny Mathew', 'Punyajoy Saha', 'Animesh Mukherjee'] | ['cs.SI', 'cs.CL'] | Hate speech detection is a challenging problem with most of the datasets
available in only one language: English. In this paper, we conduct a large
scale analysis of multilingual hate speech in 9 languages from 16 different
sources. We observe that in low resource setting, simple models such as LASER
embedding with logistic regression performs the best, while in high resource
setting BERT based models perform better. In case of zero-shot classification,
languages such as Italian and Portuguese achieve good results. Our proposed
framework could be used as an efficient solution for low-resource languages.
These models could also act as good baselines for future multilingual hate
speech detection tasks. We have made our code and experimental settings public
for other researchers at https://github.com/punyajoy/DE-LIMIT. | 2020-04-14T13:14:27Z | 16 pages, Accepted at ECML-PKDD 2020 | null | null | null | null | null | null | null | null | null |
2,004.06824 | Melanoma Detection using Adversarial Training and Deep Transfer Learning | ['Hasib Zunair', 'A. Ben Hamza'] | ['eess.IV', 'cs.CV'] | Skin lesion datasets consist predominantly of normal samples with only a
small percentage of abnormal ones, giving rise to the class imbalance problem.
Also, skin lesion images are largely similar in overall appearance owing to the
low inter-class variability. In this paper, we propose a two-stage framework
for automatic classification of skin lesion images using adversarial training
and transfer learning toward melanoma detection. In the first stage, we
leverage the inter-class variation of the data distribution for the task of
conditional image synthesis by learning the inter-class mapping and
synthesizing under-represented class samples from the over-represented ones
using unpaired image-to-image translation. In the second stage, we train a deep
convolutional neural network for skin lesion classification using the original
training set combined with the newly synthesized under-represented class
samples. The training of this classifier is carried out by minimizing the focal
loss function, which assists the model in learning from hard examples, while
down-weighting the easy ones. Experiments conducted on a dermatology image
benchmark demonstrate the superiority of our proposed approach over several
standard baseline methods, achieving significant performance improvements.
Interestingly, we show through feature visualization and analysis that our
method leads to context based lesion assessment that can reach an expert
dermatologist level. | 2020-04-14T22:46:20Z | Published in the Journal of Physics in Medicine and Biology (PMB),
April 2020. Codes at https://github.com/hasibzunair/adversarial-lesions | null | 10.1088/1361-6560/ab86d3 | null | null | null | null | null | null | null |
2,004.0687 | Coreferential Reasoning Learning for Language Representation | ['Deming Ye', 'Yankai Lin', 'Jiaju Du', 'Zhenghao Liu', 'Peng Li', 'Maosong Sun', 'Zhiyuan Liu'] | ['cs.CL'] | Language representation models such as BERT could effectively capture
contextual semantic information from plain text, and have been proved to
achieve promising results in lots of downstream NLP tasks with appropriate
fine-tuning. However, most existing language representation models cannot
explicitly handle coreference, which is essential to the coherent understanding
of the whole discourse. To address this issue, we present CorefBERT, a novel
language representation model that can capture the coreferential relations in
context. The experimental results show that, compared with existing baseline
models, CorefBERT can achieve significant improvements consistently on various
downstream NLP tasks that require coreferential reasoning, while maintaining
comparable performance to previous models on other common NLP tasks. The source
code and experiment details of this paper can be obtained from
https://github.com/thunlp/CorefBERT. | 2020-04-15T03:57:45Z | Accepted by EMNLP2020 | null | null | null | null | null | null | null | null | null |
2,004.07067 | Gestalt: a Stacking Ensemble for SQuAD2.0 | ['Mohamed El-Geish'] | ['cs.CL', 'cs.LG', 'stat.ML'] | We propose a deep-learning system -- for the SQuAD2.0 task -- that finds, or
indicates the lack of, a correct answer to a question in a context paragraph.
Our goal is to learn an ensemble of heterogeneous SQuAD2.0 models that, when
blended properly, outperforms the best model in the ensemble per se. We created
a stacking ensemble that combines top-N predictions from two models, based on
ALBERT and RoBERTa, into a multiclass classification task to pick the best
answer out of their predictions. We explored various ensemble configurations,
input representations, and model architectures. For evaluation, we examined
test-set EM and F1 scores; our best-performing ensemble incorporated a
CNN-based meta-model and scored 87.117 and 90.306, respectively -- a relative
improvement of 0.55% for EM and 0.61% for F1 scores, compared to the baseline
performance of the best model in the ensemble, an ALBERT-based model, at 86.644
for EM and 89.760 for F1. | 2020-04-02T08:09:22Z | 11 pages, 7 figures, Stanford CS224n Natural Language Processing with
Deep Learning | null | null | null | null | null | null | null | null | null |
2,004.0718 | SPECTER: Document-level Representation Learning using Citation-informed
Transformers | ['Arman Cohan', 'Sergey Feldman', 'Iz Beltagy', 'Doug Downey', 'Daniel S. Weld'] | ['cs.CL'] | Representation learning is a critical ingredient for natural language
processing systems. Recent Transformer language models like BERT learn powerful
textual representations, but these models are targeted towards token- and
sentence-level training objectives and do not leverage information on
inter-document relatedness, which limits their document-level representation
power. For applications on scientific documents, such as classification and
recommendation, the embeddings power strong performance on end tasks. We
propose SPECTER, a new method to generate document-level embedding of
scientific documents based on pretraining a Transformer language model on a
powerful signal of document-level relatedness: the citation graph. Unlike
existing pretrained language models, SPECTER can be easily applied to
downstream applications without task-specific fine-tuning. Additionally, to
encourage further research on document-level models, we introduce SciDocs, a
new evaluation benchmark consisting of seven document-level tasks ranging from
citation prediction, to document classification and recommendation. We show
that SPECTER outperforms a variety of competitive baselines on the benchmark. | 2020-04-15T16:05:51Z | ACL 2020 | null | null | null | null | null | null | null | null | null |
2,004.07667 | Null It Out: Guarding Protected Attributes by Iterative Nullspace
Projection | ['Shauli Ravfogel', 'Yanai Elazar', 'Hila Gonen', 'Michael Twiton', 'Yoav Goldberg'] | ['cs.CL', 'cs.LG'] | The ability to control for the kinds of information encoded in neural
representation has a variety of use cases, especially in light of the challenge
of interpreting these models. We present Iterative Null-space Projection
(INLP), a novel method for removing information from neural representations.
Our method is based on repeated training of linear classifiers that predict a
certain property we aim to remove, followed by projection of the
representations on their null-space. By doing so, the classifiers become
oblivious to that target property, making it hard to linearly separate the data
according to it. While applicable for multiple uses, we evaluate our method on
bias and fairness use-cases, and show that our method is able to mitigate bias
in word embeddings, as well as to increase fairness in a setting of multi-class
classification. | 2020-04-16T14:02:50Z | Accepted as a long paper in ACL 2020 | null | null | null | null | null | null | null | null | null |
2,004.07807 | Classification Benchmarks for Under-resourced Bengali Language based on
Multichannel Convolutional-LSTM Network | ['Md. Rezaul Karim', 'Bharathi Raja Chakravarthi', 'John P. McCrae', 'Michael Cochez'] | ['cs.CL', 'cs.LG', 'stat.ML'] | Exponential growths of social media and micro-blogging sites not only provide
platforms for empowering freedom of expressions and individual voices but also
enables people to express anti-social behaviour like online harassment,
cyberbullying, and hate speech. Numerous works have been proposed to utilize
these data for social and anti-social behaviours analysis, document
characterization, and sentiment analysis by predicting the contexts mostly for
highly resourced languages such as English. However, there are languages that
are under-resources, e.g., South Asian languages like Bengali, Tamil, Assamese,
Telugu that lack of computational resources for the NLP tasks. In this paper,
we provide several classification benchmarks for Bengali, an under-resourced
language. We prepared three datasets of expressing hate, commonly used topics,
and opinions for hate speech detection, document classification, and sentiment
analysis, respectively. We built the largest Bengali word embedding models to
date based on 250 million articles, which we call BengFastText. We perform
three different experiments, covering document classification, sentiment
analysis, and hate speech detection. We incorporate word embeddings into a
Multichannel Convolutional-LSTM (MConv-LSTM) network for predicting different
types of hate speech, document classification, and sentiment analysis.
Experiments demonstrate that BengFastText can capture the semantics of words
from respective contexts correctly. Evaluations against several baseline
embedding models, e.g., Word2Vec and GloVe yield up to 92.30%, 82.25%, and
90.45% F1-scores in case of document classification, sentiment analysis, and
hate speech detection, respectively during 5-fold cross-validation tests. | 2020-04-11T22:17:04Z | This paper is under review in the Journal of Natural Language
Engineering | null | null | null | null | null | null | null | null | null |
2,004.08531 | MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network
Architecture for Speech Commands Recognition | ['Somshubra Majumdar', 'Boris Ginsburg'] | ['eess.AS'] | We present an MatchboxNet - an end-to-end neural network for speech command
recognition. MatchboxNet is a deep residual network composed from blocks of 1D
time-channel separable convolution, batch-normalization, ReLU and dropout
layers. MatchboxNet reaches state-of-the-art accuracy on the Google Speech
Commands dataset while having significantly fewer parameters than similar
models. The small footprint of MatchboxNet makes it an attractive candidate for
devices with limited computational resources. The model is highly scalable, so
model accuracy can be improved with modest additional memory and compute.
Finally, we show how intensive data augmentation using an auxiliary noise
dataset improves robustness in the presence of background noise. | 2020-04-18T05:49:27Z | null | null | 10.21437/Interspeech.2020-1058 | null | null | null | null | null | null | null |
2,004.08955 | ResNeSt: Split-Attention Networks | ['Hang Zhang', 'Chongruo Wu', 'Zhongyue Zhang', 'Yi Zhu', 'Haibin Lin', 'Zhi Zhang', 'Yue Sun', 'Tong He', 'Jonas Mueller', 'R. Manmatha', 'Mu Li', 'Alexander Smola'] | ['cs.CV'] | It is well known that featuremap attention and multi-path representation are
important for visual recognition. In this paper, we present a modularized
architecture, which applies the channel-wise attention on different network
branches to leverage their success in capturing cross-feature interactions and
learning diverse representations. Our design results in a simple and unified
computation block, which can be parameterized using only a few variables. Our
model, named ResNeSt, outperforms EfficientNet in accuracy and latency
trade-off on image classification. In addition, ResNeSt has achieved superior
transfer learning results on several public benchmarks serving as the backbone,
and has been adopted by the winning entries of COCO-LVIS challenge. The source
code for complete system and pretrained models are publicly available. | 2020-04-19T20:40:31Z | null | null | null | null | null | null | null | null | null | null |
2,004.09015 | Incorporating External Knowledge through Pre-training for Natural
Language to Code Generation | ['Frank F. Xu', 'Zhengbao Jiang', 'Pengcheng Yin', 'Bogdan Vasilescu', 'Graham Neubig'] | ['cs.CL'] | Open-domain code generation aims to generate code in a general-purpose
programming language (such as Python) from natural language (NL) intents.
Motivated by the intuition that developers usually retrieve resources on the
web when writing code, we explore the effectiveness of incorporating two
varieties of external knowledge into NL-to-code generation: automatically mined
NL-code pairs from the online programming QA forum StackOverflow and
programming language API documentation. Our evaluations show that combining the
two sources with data augmentation and retrieval-based data re-sampling
improves the current state-of-the-art by up to 2.2% absolute BLEU score on the
code generation testbed CoNaLa. The code and resources are available at
https://github.com/neulab/external-knowledge-codegen. | 2020-04-20T01:45:27Z | Accepted by ACL 2020 | null | null | Incorporating External Knowledge through Pre-training for Natural Language to Code Generation | ['Frank F. Xu', 'Zhengbao Jiang', 'Pengcheng Yin', 'Bogdan Vasilescu', 'Graham Neubig'] | 2,020 | Annual Meeting of the Association for Computational Linguistics | 84 | 32 | ['Computer Science'] |
2,004.09813 | Making Monolingual Sentence Embeddings Multilingual using Knowledge
Distillation | ['Nils Reimers', 'Iryna Gurevych'] | ['cs.CL'] | We present an easy and efficient method to extend existing sentence embedding
models to new languages. This allows to create multilingual versions from
previously monolingual models. The training is based on the idea that a
translated sentence should be mapped to the same location in the vector space
as the original sentence. We use the original (monolingual) model to generate
sentence embeddings for the source language and then train a new system on
translated sentences to mimic the original model. Compared to other methods for
training multilingual sentence embeddings, this approach has several
advantages: It is easy to extend existing models with relatively few samples to
new languages, it is easier to ensure desired properties for the vector space,
and the hardware requirements for training is lower. We demonstrate the
effectiveness of our approach for 50+ languages from various language families.
Code to extend sentence embeddings models to more than 400 languages is
publicly available. | 2020-04-21T08:20:25Z | Accepted at EMNLP 2020 | null | null | Making Monolingual Sentence Embeddings Multilingual Using Knowledge Distillation | ['Nils Reimers', 'Iryna Gurevych'] | 2,020 | Conference on Empirical Methods in Natural Language Processing | 1,035 | 39 | ['Computer Science'] |
2,004.10404 | Logical Natural Language Generation from Open-Domain Tables | ['Wenhu Chen', 'Jianshu Chen', 'Yu Su', 'Zhiyu Chen', 'William Yang Wang'] | ['cs.CL', 'cs.AI'] | Neural natural language generation (NLG) models have recently shown
remarkable progress in fluency and coherence. However, existing studies on
neural NLG are primarily focused on surface-level realizations with limited
emphasis on logical inference, an important aspect of human thinking and
language. In this paper, we suggest a new NLG task where a model is tasked with
generating natural language statements that can be \emph{logically entailed} by
the facts in an open-domain semi-structured table. To facilitate the study of
the proposed logical NLG problem, we use the existing TabFact dataset
\cite{chen2019tabfact} featured with a wide range of logical/symbolic
inferences as our testbed, and propose new automatic metrics to evaluate the
fidelity of generation models w.r.t.\ logical inference. The new task poses
challenges to the existing monotonic generation frameworks due to the mismatch
between sequence order and logical order. In our experiments, we
comprehensively survey different generation architectures (LSTM, Transformer,
Pre-Trained LM) trained with different algorithms (RL, Adversarial Training,
Coarse-to-Fine) on the dataset and made following observations: 1) Pre-Trained
LM can significantly boost both the fluency and logical fidelity metrics, 2) RL
and Adversarial Training are trading fluency for fidelity, 3) Coarse-to-Fine
generation can help partially alleviate the fidelity issue while maintaining
high language fluency. The code and data are available at
\url{https://github.com/wenhuchen/LogicNLG}. | 2020-04-22T06:03:10Z | Accepted to ACL 2020 as Long Paper | null | null | null | null | null | null | null | null | null |
2,004.10568 | Up or Down? Adaptive Rounding for Post-Training Quantization | ['Markus Nagel', 'Rana Ali Amjad', 'Mart van Baalen', 'Christos Louizos', 'Tijmen Blankevoort'] | ['cs.LG', 'cs.CV', 'stat.ML'] | When quantizing neural networks, assigning each floating-point weight to its
nearest fixed-point value is the predominant approach. We find that, perhaps
surprisingly, this is not the best we can do. In this paper, we propose
AdaRound, a better weight-rounding mechanism for post-training quantization
that adapts to the data and the task loss. AdaRound is fast, does not require
fine-tuning of the network, and only uses a small amount of unlabelled data. We
start by theoretically analyzing the rounding problem for a pre-trained neural
network. By approximating the task loss with a Taylor series expansion, the
rounding task is posed as a quadratic unconstrained binary optimization
problem. We simplify this to a layer-wise local loss and propose to optimize
this loss with a soft relaxation. AdaRound not only outperforms
rounding-to-nearest by a significant margin but also establishes a new
state-of-the-art for post-training quantization on several networks and tasks.
Without fine-tuning, we can quantize the weights of Resnet18 and Resnet50 to 4
bits while staying within an accuracy loss of 1%. | 2020-04-22T13:44:28Z | Published as a conference paper at ICML 2020 | null | null | null | null | null | null | null | null | null |
2,004.10934 | YOLOv4: Optimal Speed and Accuracy of Object Detection | ['Alexey Bochkovskiy', 'Chien-Yao Wang', 'Hong-Yuan Mark Liao'] | ['cs.CV', 'eess.IV'] | There are a huge number of features which are said to improve Convolutional
Neural Network (CNN) accuracy. Practical testing of combinations of such
features on large datasets, and theoretical justification of the result, is
required. Some features operate on certain models exclusively and for certain
problems exclusively, or only for small-scale datasets; while some features,
such as batch-normalization and residual-connections, are applicable to the
majority of models, tasks, and datasets. We assume that such universal features
include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections
(CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT)
and Mish-activation. We use new features: WRC, CSP, CmBN, SAT, Mish activation,
Mosaic data augmentation, CmBN, DropBlock regularization, and CIoU loss, and
combine some of them to achieve state-of-the-art results: 43.5% AP (65.7% AP50)
for the MS COCO dataset at a realtime speed of ~65 FPS on Tesla V100. Source
code is at https://github.com/AlexeyAB/darknet | 2020-04-23T02:10:02Z | null | null | null | null | null | null | null | null | null | null |
2,004.10964 | Don't Stop Pretraining: Adapt Language Models to Domains and Tasks | ['Suchin Gururangan', 'Ana Marasović', 'Swabha Swayamdipta', 'Kyle Lo', 'Iz Beltagy', 'Doug Downey', 'Noah A. Smith'] | ['cs.CL', 'cs.LG'] | Language models pretrained on text from a wide variety of sources form the
foundation of today's NLP. In light of the success of these broad-coverage
models, we investigate whether it is still helpful to tailor a pretrained model
to the domain of a target task. We present a study across four domains
(biomedical and computer science publications, news, and reviews) and eight
classification tasks, showing that a second phase of pretraining in-domain
(domain-adaptive pretraining) leads to performance gains, under both high- and
low-resource settings. Moreover, adapting to the task's unlabeled data
(task-adaptive pretraining) improves performance even after domain-adaptive
pretraining. Finally, we show that adapting to a task corpus augmented using
simple data selection strategies is an effective alternative, especially when
resources for domain-adaptive pretraining might be unavailable. Overall, we
consistently find that multi-phase adaptive pretraining offers large gains in
task performance. | 2020-04-23T04:21:19Z | ACL 2020 | null | null | Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks | ['Suchin Gururangan', 'Ana Marasović', 'Swabha Swayamdipta', 'Kyle Lo', 'Iz Beltagy', 'Doug Downey', 'Noah A. Smith'] | 2,020 | Annual Meeting of the Association for Computational Linguistics | 2,454 | 77 | ['Computer Science'] |
2,004.11362 | Supervised Contrastive Learning | ['Prannay Khosla', 'Piotr Teterwak', 'Chen Wang', 'Aaron Sarna', 'Yonglong Tian', 'Phillip Isola', 'Aaron Maschinot', 'Ce Liu', 'Dilip Krishnan'] | ['cs.LG', 'cs.CV', 'stat.ML'] | Contrastive learning applied to self-supervised representation learning has
seen a resurgence in recent years, leading to state of the art performance in
the unsupervised training of deep image models. Modern batch contrastive
approaches subsume or significantly outperform traditional contrastive losses
such as triplet, max-margin and the N-pairs loss. In this work, we extend the
self-supervised batch contrastive approach to the fully-supervised setting,
allowing us to effectively leverage label information. Clusters of points
belonging to the same class are pulled together in embedding space, while
simultaneously pushing apart clusters of samples from different classes. We
analyze two possible versions of the supervised contrastive (SupCon) loss,
identifying the best-performing formulation of the loss. On ResNet-200, we
achieve top-1 accuracy of 81.4% on the ImageNet dataset, which is 0.8% above
the best number reported for this architecture. We show consistent
outperformance over cross-entropy on other datasets and two ResNet variants.
The loss shows benefits for robustness to natural corruptions and is more
stable to hyperparameter settings such as optimizers and data augmentations.
Our loss function is simple to implement, and reference TensorFlow code is
released at https://t.ly/supcon. | 2020-04-23T17:58:56Z | null | null | null | null | null | null | null | null | null | null |
2,004.11579 | Probabilistically Masked Language Model Capable of Autoregressive
Generation in Arbitrary Word Order | ['Yi Liao', 'Xin Jiang', 'Qun Liu'] | ['cs.CL'] | Masked language model and autoregressive language model are two types of
language models. While pretrained masked language models such as BERT overwhelm
the line of natural language understanding (NLU) tasks, autoregressive language
models such as GPT are especially capable in natural language generation (NLG).
In this paper, we propose a probabilistic masking scheme for the masked
language model, which we call probabilistically masked language model (PMLM).
We implement a specific PMLM with a uniform prior distribution on the masking
ratio named u-PMLM. We prove that u-PMLM is equivalent to an autoregressive
permutated language model. One main advantage of the model is that it supports
text generation in arbitrary order with surprisingly good quality, which could
potentially enable new applications over traditional unidirectional generation.
Besides, the pretrained u-PMLM also outperforms BERT on a set of downstream NLU
tasks. | 2020-04-24T07:38:19Z | Accepted by ACL 2020 | null | null | Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order | ['Yi Liao', 'Xin Jiang', 'Qun Liu'] | 2,020 | Annual Meeting of the Association for Computational Linguistics | 40 | 34 | ['Computer Science'] |
2,004.11867 | Improving Massively Multilingual Neural Machine Translation and
Zero-Shot Translation | ['Biao Zhang', 'Philip Williams', 'Ivan Titov', 'Rico Sennrich'] | ['cs.CL'] | Massively multilingual models for neural machine translation (NMT) are
theoretically attractive, but often underperform bilingual models and deliver
poor zero-shot translations. In this paper, we explore ways to improve them. We
argue that multilingual NMT requires stronger modeling capacity to support
language pairs with varying typological characteristics, and overcome this
bottleneck via language-specific components and deepening NMT architectures. We
identify the off-target translation issue (i.e. translating into a wrong target
language) as the major source of the inferior zero-shot performance, and
propose random online backtranslation to enforce the translation of unseen
training language pairs. Experiments on OPUS-100 (a novel multilingual dataset
with 100 languages) show that our approach substantially narrows the
performance gap with bilingual models in both one-to-many and many-to-many
settings, and improves zero-shot performance by ~10 BLEU, approaching
conventional pivot-based methods. | 2020-04-24T17:21:32Z | ACL2020 | null | null | null | null | null | null | null | null | null |
2,004.12184 | A Named Entity Based Approach to Model Recipes | ['Nirav Diwan', 'Devansh Batra', 'Ganesh Bagler'] | ['cs.CL', 'cs.IR'] | Traditional cooking recipes follow a structure which can be modelled very
well if the rules and semantics of the different sections of the recipe text
are analyzed and represented accurately. We propose a structure that can
accurately represent the recipe as well as a pipeline to infer the best
representation of the recipe in this uniform structure. The Ingredients section
in a recipe typically lists down the ingredients required and corresponding
attributes such as quantity, temperature, and processing state. This can be
modelled by defining these attributes and their values. The physical entities
which make up a recipe can be broadly classified into utensils, ingredients and
their combinations that are related by cooking techniques. The instruction
section lists down a series of events in which a cooking technique or process
is applied upon these utensils and ingredients. We model these relationships in
the form of tuples. Thus, using a combination of these methods we model cooking
recipe in the dataset RecipeDB to show the efficacy of our method. This mined
information model can have several applications which include translating
recipes between languages, determining similarity between recipes, generation
of novel recipes and estimation of the nutritional profile of recipes. For the
purpose of recognition of ingredient attributes, we train the Named Entity
Relationship (NER) models and analyze the inferences with the help of K-Means
clustering. Our model presented with an F1 score of 0.95 across all datasets.
We use a similar NER tagging model for labelling cooking techniques (F1 score =
0.88) and utensils (F1 score = 0.90) within the instructions section. Finally,
we determine the temporal sequence of relationships between ingredients,
utensils and cooking techniques for modeling the instruction steps. | 2020-04-25T16:37:26Z | 36th IEEE International Conference on Data Engineering (ICDE 2020),
DECOR Workshop; 6 pages, 5 figures | null | null | A Named Entity Based Approach to Model Recipes | ['Nirav Diwan', 'Devansh Batra', 'Ganesh Bagler'] | 2,020 | 2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW) | 12 | 13 | ['Computer Science', 'Physics'] |
2,004.12832 | ColBERT: Efficient and Effective Passage Search via Contextualized Late
Interaction over BERT | ['Omar Khattab', 'Matei Zaharia'] | ['cs.IR', 'cs.CL'] | Recent progress in Natural Language Understanding (NLU) is driving fast-paced
advances in Information Retrieval (IR), largely owed to fine-tuning deep
language models (LMs) for document ranking. While remarkably effective, the
ranking models based on these LMs increase computational cost by orders of
magnitude over prior approaches, particularly as they must feed each
query-document pair through a massive neural network to compute a single
relevance score. To tackle this, we present ColBERT, a novel ranking model that
adapts deep LMs (in particular, BERT) for efficient retrieval. ColBERT
introduces a late interaction architecture that independently encodes the query
and the document using BERT and then employs a cheap yet powerful interaction
step that models their fine-grained similarity. By delaying and yet retaining
this fine-granular interaction, ColBERT can leverage the expressiveness of deep
LMs while simultaneously gaining the ability to pre-compute document
representations offline, considerably speeding up query processing. Beyond
reducing the cost of re-ranking the documents retrieved by a traditional model,
ColBERT's pruning-friendly interaction mechanism enables leveraging
vector-similarity indexes for end-to-end retrieval directly from a large
document collection. We extensively evaluate ColBERT using two recent passage
search datasets. Results show that ColBERT's effectiveness is competitive with
existing BERT-based models (and outperforms every non-BERT baseline), while
executing two orders-of-magnitude faster and requiring four orders-of-magnitude
fewer FLOPs per query. | 2020-04-27T14:21:03Z | Accepted at SIGIR 2020 | null | null | ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT | ['O. Khattab', 'M. Zaharia'] | 2,020 | Annual International ACM SIGIR Conference on Research and Development in Information Retrieval | 1,389 | 45 | ['Computer Science'] |
2,004.12992 | MakeItTalk: Speaker-Aware Talking-Head Animation | ['Yang Zhou', 'Xintong Han', 'Eli Shechtman', 'Jose Echevarria', 'Evangelos Kalogerakis', 'Dingzeyu Li'] | ['cs.CV', 'cs.GR'] | We present a method that generates expressive talking heads from a single
facial image with audio as the only input. In contrast to previous approaches
that attempt to learn direct mappings from audio to raw pixels or points for
creating talking faces, our method first disentangles the content and speaker
information in the input audio signal. The audio content robustly controls the
motion of lips and nearby facial regions, while the speaker information
determines the specifics of facial expressions and the rest of the talking head
dynamics. Another key component of our method is the prediction of facial
landmarks reflecting speaker-aware dynamics. Based on this intermediate
representation, our method is able to synthesize photorealistic videos of
entire talking heads with full range of motion and also animate artistic
paintings, sketches, 2D cartoon characters, Japanese mangas, stylized
caricatures in a single unified framework. We present extensive quantitative
and qualitative evaluation of our method, in addition to user studies,
demonstrating generated talking heads of significantly higher quality compared
to prior state-of-the-art. | 2020-04-27T17:56:15Z | SIGGRAPH Asia 2020, 15 pages, 13 figures | null | 10.1145/3414685.3417774 | null | null | null | null | null | null | null |
2,004.13637 | Recipes for building an open-domain chatbot | ['Stephen Roller', 'Emily Dinan', 'Naman Goyal', 'Da Ju', 'Mary Williamson', 'Yinhan Liu', 'Jing Xu', 'Myle Ott', 'Kurt Shuster', 'Eric M. Smith', 'Y-Lan Boureau', 'Jason Weston'] | ['cs.CL', 'cs.AI'] | Building open-domain chatbots is a challenging area for machine learning
research. While prior work has shown that scaling neural models in the number
of parameters and the size of the data they are trained on gives improved
results, we show that other ingredients are important for a high-performing
chatbot. Good conversation requires a number of skills that an expert
conversationalist blends in a seamless way: providing engaging talking points
and listening to their partners, and displaying knowledge, empathy and
personality appropriately, while maintaining a consistent persona. We show that
large scale models can learn these skills when given appropriate training data
and choice of generation strategy. We build variants of these recipes with 90M,
2.7B and 9.4B parameter models, and make our models and code publicly
available. Human evaluations show our best models are superior to existing
approaches in multi-turn dialogue in terms of engagingness and humanness
measurements. We then discuss the limitations of this work by analyzing failure
cases of our models. | 2020-04-28T16:33:25Z | null | null | null | Recipes for Building an Open-Domain Chatbot | ['Stephen Roller', 'Emily Dinan', 'Naman Goyal', 'Da Ju', 'Mary Williamson', 'Yinhan Liu', 'Jing Xu', 'Myle Ott', 'Kurt Shuster', 'Eric Michael Smith', 'Y-Lan Boureau', 'J. Weston'] | 2,020 | Conference of the European Chapter of the Association for Computational Linguistics | 1,021 | 84 | ['Computer Science'] |
2,004.13796 | TextGAIL: Generative Adversarial Imitation Learning for Text Generation | ['Qingyang Wu', 'Lei Li', 'Zhou Yu'] | ['cs.CL', 'cs.LG'] | Generative Adversarial Networks (GANs) for text generation have recently
received many criticisms, as they perform worse than their MLE counterparts. We
suspect previous text GANs' inferior performance is due to the lack of a
reliable guiding signal in their discriminators. To address this problem, we
propose a generative adversarial imitation learning framework for text
generation that uses large pre-trained language models to provide more reliable
reward guidance. Our approach uses contrastive discriminator, and proximal
policy optimization (PPO) to stabilize and improve text generation performance.
For evaluation, we conduct experiments on a diverse set of unconditional and
conditional text generation tasks. Experimental results show that TextGAIL
achieves better performance in terms of both quality and diversity than the MLE
baseline. We also validate our intuition that TextGAIL's discriminator
demonstrates the capability of providing reasonable rewards with an additional
task. | 2020-04-07T00:24:35Z | AAAI 2021 | null | null | null | null | null | null | null | null | null |
2,004.13845 | DARE: Data Augmented Relation Extraction with GPT-2 | ['Yannis Papanikolaou', 'Andrea Pierleoni'] | ['cs.CL', 'cs.LG', 'stat.ML'] | Real-world Relation Extraction (RE) tasks are challenging to deal with,
either due to limited training data or class imbalance issues. In this work, we
present Data Augmented Relation Extraction(DARE), a simple method to augment
training data by properly fine-tuning GPT-2 to generate examples for specific
relation types. The generated training data is then used in combination with
the gold dataset to train a BERT-based RE classifier. In a series of
experiments we show the advantages of our method, which leads in improvements
of up to 11 F1 score points against a strong base-line. Also, DARE achieves new
state of the art in three widely used biomedical RE datasets surpassing the
previous best results by 4.7 F1 points on average. | 2020-04-06T14:38:36Z | null | null | null | null | null | null | null | null | null | null |
2,004.13922 | Revisiting Pre-Trained Models for Chinese Natural Language Processing | ['Yiming Cui', 'Wanxiang Che', 'Ting Liu', 'Bing Qin', 'Shijin Wang', 'Guoping Hu'] | ['cs.CL'] | Bidirectional Encoder Representations from Transformers (BERT) has shown
marvelous improvements across various NLP tasks, and consecutive variants have
been proposed to further improve the performance of the pre-trained language
models. In this paper, we target on revisiting Chinese pre-trained language
models to examine their effectiveness in a non-English language and release the
Chinese pre-trained language model series to the community. We also propose a
simple but effective model called MacBERT, which improves upon RoBERTa in
several ways, especially the masking strategy that adopts MLM as correction
(Mac). We carried out extensive experiments on eight Chinese NLP tasks to
revisit the existing pre-trained language models as well as the proposed
MacBERT. Experimental results show that MacBERT could achieve state-of-the-art
performances on many NLP tasks, and we also ablate details with several
findings that may help future research. Resources available:
https://github.com/ymcui/MacBERT | 2020-04-29T02:08:30Z | 12 pages, to appear at Findings of EMNLP 2020 | null | 10.18653/v1/2020.findings-emnlp.58 | null | null | null | null | null | null | null |
2,004.14166 | SpellGCN: Incorporating Phonological and Visual Similarities into
Language Models for Chinese Spelling Check | ['Xingyi Cheng', 'Weidi Xu', 'Kunlong Chen', 'Shaohua Jiang', 'Feng Wang', 'Taifeng Wang', 'Wei Chu', 'Yuan Qi'] | ['cs.CL'] | Chinese Spelling Check (CSC) is a task to detect and correct spelling errors
in Chinese natural language. Existing methods have made attempts to incorporate
the similarity knowledge between Chinese characters. However, they take the
similarity knowledge as either an external input resource or just heuristic
rules. This paper proposes to incorporate phonological and visual similarity
knowledge into language models for CSC via a specialized graph convolutional
network (SpellGCN). The model builds a graph over the characters, and SpellGCN
is learned to map this graph into a set of inter-dependent character
classifiers. These classifiers are applied to the representations extracted by
another network, such as BERT, enabling the whole network to be end-to-end
trainable. Experiments (The dataset and all code for this paper are available
at https://github.com/ACL2020SpellGCN/SpellGCN) are conducted on three
human-annotated datasets. Our method achieves superior performance against
previous models by a large margin. | 2020-04-26T03:34:06Z | Accepted by ACL2020 | null | null | null | null | null | null | null | null | null |
2,004.14253 | GePpeTto Carves Italian into a Language Model | ['Lorenzo De Mattei', 'Michele Cafagna', "Felice Dell'Orletta", 'Malvina Nissim', 'Marco Guerini'] | ['cs.CL'] | In the last few years, pre-trained neural architectures have provided
impressive improvements across several NLP tasks. Still, generative language
models are available mainly for English. We develop GePpeTto, the first
generative language model for Italian, built using the GPT-2 architecture. We
provide a thorough analysis of GePpeTto's quality by means of both an automatic
and a human-based evaluation. The automatic assessment consists in (i)
calculating perplexity across different genres and (ii) a profiling analysis
over GePpeTto's writing characteristics. We find that GePpeTto's production is
a sort of bonsai version of human production, with shorter but yet complex
sentences. Human evaluation is performed over a sentence completion task, where
GePpeTto's output is judged as natural more often than not, and much closer to
the original human texts than to a simpler language model which we take as
baseline. | 2020-04-29T15:02:01Z | null | null | null | null | null | null | null | null | null | null |
2,004.14255 | Efficient Document Re-Ranking for Transformers by Precomputing Term
Representations | ['Sean MacAvaney', 'Franco Maria Nardini', 'Raffaele Perego', 'Nicola Tonellotto', 'Nazli Goharian', 'Ophir Frieder'] | ['cs.IR'] | Deep pretrained transformer networks are effective at various ranking tasks,
such as question answering and ad-hoc document ranking. However, their
computational expenses deem them cost-prohibitive in practice. Our proposed
approach, called PreTTR (Precomputing Transformer Term Representations),
considerably reduces the query-time latency of deep transformer networks (up to
a 42x speedup on web document ranking) making these networks more practical to
use in a real-time ranking scenario. Specifically, we precompute part of the
document term representations at indexing time (without a query), and merge
them with the query representation at query time to compute the final ranking
score. Due to the large size of the token representations, we also propose an
effective approach to reduce the storage requirement by training a compression
layer to match attention scores. Our compression technique reduces the storage
required up to 95% and it can be applied without a substantial degradation in
ranking performance. | 2020-04-29T15:04:22Z | Accepted at SIGIR 2020 (long) | null | 10.1145/3397271.3401093 | Efficient Document Re-Ranking for Transformers by Precomputing Term Representations | ['Sean MacAvaney', 'F. M. Nardini', 'R. Perego', 'N. Tonellotto', 'Nazli Goharian', 'O. Frieder'] | 2,020 | Annual International ACM SIGIR Conference on Research and Development in Information Retrieval | 120 | 53 | ['Computer Science'] |
2,004.149 | MLSUM: The Multilingual Summarization Corpus | ['Thomas Scialom', 'Paul-Alexis Dray', 'Sylvain Lamprier', 'Benjamin Piwowarski', 'Jacopo Staiano'] | ['cs.CL'] | We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
Obtained from online newspapers, it contains 1.5M+ article/summary pairs in
five different languages -- namely, French, German, Spanish, Russian, Turkish.
Together with English newspapers from the popular CNN/Daily mail dataset, the
collected data form a large scale multilingual dataset which can enable new
research directions for the text summarization community. We report
cross-lingual comparative analyses based on state-of-the-art systems. These
highlight existing biases which motivate the use of a multi-lingual dataset. | 2020-04-30T15:58:34Z | null | null | null | MLSUM: The Multilingual Summarization Corpus | ['Thomas Scialom', 'Paul-Alexis Dray', 'S. Lamprier', 'Benjamin Piwowarski', 'Jacopo Staiano'] | 2,020 | Conference on Empirical Methods in Natural Language Processing | 177 | 75 | ['Computer Science'] |
2,004.14963 | Data and Representation for Turkish Natural Language Inference | ['Emrah Budur', 'Rıza Özçelik', 'Tunga Güngör', 'Christopher Potts'] | ['cs.CL'] | Large annotated datasets in NLP are overwhelmingly in English. This is an
obstacle to progress in other languages. Unfortunately, obtaining new annotated
resources for each task in each language would be prohibitively expensive. At
the same time, commercial machine translation systems are now robust. Can we
leverage these systems to translate English-language datasets automatically? In
this paper, we offer a positive response for natural language inference (NLI)
in Turkish. We translated two large English NLI datasets into Turkish and had a
team of experts validate their translation quality and fidelity to the original
labels. Using these datasets, we address core issues of representation for
Turkish NLI. We find that in-language embeddings are essential and that
morphological parsing can be avoided where the training set is large. Finally,
we show that models trained on our machine-translated datasets are successful
on human-translated evaluation sets. We share all code, models, and data
publicly. | 2020-04-30T17:12:52Z | Accepted to EMNLP 2020 | null | null | null | null | null | null | null | null | null |
2,004.15011 | TLDR: Extreme Summarization of Scientific Documents | ['Isabel Cachola', 'Kyle Lo', 'Arman Cohan', 'Daniel S. Weld'] | ['cs.CL'] | We introduce TLDR generation, a new form of extreme summarization, for
scientific papers. TLDR generation involves high source compression and
requires expert background knowledge and understanding of complex
domain-specific language. To facilitate study on this task, we introduce
SciTLDR, a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SciTLDR
contains both author-written and expert-derived TLDRs, where the latter are
collected using a novel annotation protocol that produces high-quality
summaries while minimizing annotation burden. We propose CATTS, a simple yet
effective learning strategy for generating TLDRs that exploits titles as an
auxiliary training signal. CATTS improves upon strong baselines under both
automated metrics and human evaluations. Data and code are publicly available
at https://github.com/allenai/scitldr. | 2020-04-30T17:56:18Z | null | null | null | null | null | null | null | null | null | null |
2,005.00052 | MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer | ['Jonas Pfeiffer', 'Ivan Vulić', 'Iryna Gurevych', 'Sebastian Ruder'] | ['cs.CL'] | The main goal behind state-of-the-art pre-trained multilingual models such as
multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in
low-resource languages through zero-shot or few-shot cross-lingual transfer.
However, due to limited model capacity, their transfer performance is the
weakest exactly on such low-resource languages and languages unseen during
pre-training. We propose MAD-X, an adapter-based framework that enables high
portability and parameter-efficient transfer to arbitrary tasks and languages
by learning modular language and task representations. In addition, we
introduce a novel invertible adapter architecture and a strong baseline method
for adapting a pre-trained multilingual model to a new language. MAD-X
outperforms the state of the art in cross-lingual transfer across a
representative set of typologically diverse languages on named entity
recognition and causal commonsense reasoning, and achieves competitive results
on question answering. Our code and adapters are available at AdapterHub.ml | 2020-04-30T18:54:43Z | EMNLP 2020 | null | null | null | null | null | null | null | null | null |
2,005.00085 | AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for
Indic Languages | ['Anoop Kunchukuttan', 'Divyanshu Kakwani', 'Satish Golla', 'Gokul N. C.', 'Avik Bhattacharyya', 'Mitesh M. Khapra', 'Pratyush Kumar'] | ['cs.CL'] | We present the IndicNLP corpus, a large-scale, general-domain corpus
containing 2.7 billion words for 10 Indian languages from two language
families. We share pre-trained word embeddings trained on these corpora. We
create news article category classification datasets for 9 languages to
evaluate the embeddings. We show that the IndicNLP embeddings significantly
outperform publicly available pre-trained embedding on multiple evaluation
tasks. We hope that the availability of the corpus will accelerate Indic NLP
research. The resources are available at
https://github.com/ai4bharat-indicnlp/indicnlp_corpus. | 2020-04-30T20:21:02Z | 7 pages, 8 tables,
https://github.com/ai4bharat-indicnlp/indicnlp_corpus | null | null | AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages | ['Anoop Kunchukuttan', 'Divyanshu Kakwani', 'S. Golla', 'C. GokulN.', 'Avik Bhattacharyya', 'Mitesh M. Khapra', 'Pratyush Kumar'] | 2,020 | arXiv.org | 83 | 27 | ['Computer Science'] |
2,005.00247 | AdapterFusion: Non-Destructive Task Composition for Transfer Learning | ['Jonas Pfeiffer', 'Aishwarya Kamath', 'Andreas Rücklé', 'Kyunghyun Cho', 'Iryna Gurevych'] | ['cs.CL'] | Sequential fine-tuning and multi-task learning are methods aiming to
incorporate knowledge from multiple tasks; however, they suffer from
catastrophic forgetting and difficulties in dataset balancing. To address these
shortcomings, we propose AdapterFusion, a new two stage learning algorithm that
leverages knowledge from multiple tasks. First, in the knowledge extraction
stage we learn task specific parameters called adapters, that encapsulate the
task-specific information. We then combine the adapters in a separate knowledge
composition step. We show that by separating the two stages, i.e., knowledge
extraction and knowledge composition, the classifier can effectively exploit
the representations learned from multiple tasks in a non-destructive manner. We
empirically evaluate AdapterFusion on 16 diverse NLU tasks, and find that it
effectively combines various types of knowledge at different layers of the
model. We show that our approach outperforms traditional strategies such as
full fine-tuning as well as multi-task learning. Our code and adapters are
available at AdapterHub.ml. | 2020-05-01T07:03:42Z | null | Proceedings of EACL 2021 | null | null | null | null | null | null | null | null |
2,005.00341 | Jukebox: A Generative Model for Music | ['Prafulla Dhariwal', 'Heewoo Jun', 'Christine Payne', 'Jong Wook Kim', 'Alec Radford', 'Ilya Sutskever'] | ['eess.AS', 'cs.LG', 'cs.SD', 'stat.ML'] | We introduce Jukebox, a model that generates music with singing in the raw
audio domain. We tackle the long context of raw audio using a multi-scale
VQ-VAE to compress it to discrete codes, and modeling those using
autoregressive Transformers. We show that the combined model at scale can
generate high-fidelity and diverse songs with coherence up to multiple minutes.
We can condition on artist and genre to steer the musical and vocal style, and
on unaligned lyrics to make the singing more controllable. We are releasing
thousands of non cherry-picked samples at https://jukebox.openai.com, along
with model weights and code at https://github.com/openai/jukebox | 2020-04-30T09:02:45Z | null | null | null | null | null | null | null | null | null | null |
2,005.00547 | GoEmotions: A Dataset of Fine-Grained Emotions | ['Dorottya Demszky', 'Dana Movshovitz-Attias', 'Jeongwoo Ko', 'Alan Cowen', 'Gaurav Nemade', 'Sujith Ravi'] | ['cs.CL'] | Understanding emotion expressed in language has a wide range of applications,
from building empathetic chatbots to detecting harmful online behavior.
Advancement in this area can be improved using large-scale datasets with a
fine-grained typology, adaptable to multiple downstream tasks. We introduce
GoEmotions, the largest manually annotated dataset of 58k English Reddit
comments, labeled for 27 emotion categories or Neutral. We demonstrate the high
quality of the annotations via Principal Preserved Component Analysis. We
conduct transfer learning experiments with existing emotion benchmarks to show
that our dataset generalizes well to other domains and different emotion
taxonomies. Our BERT-based model achieves an average F1-score of .46 across our
proposed taxonomy, leaving much room for improvement. | 2020-05-01T18:00:02Z | Accepted to ACL 2020 | null | null | GoEmotions: A Dataset of Fine-Grained Emotions | ['Dorottya Demszky', 'Dana Movshovitz-Attias', 'Jeongwoo Ko', 'Alan S. Cowen', 'Gaurav Nemade', 'Sujith Ravi'] | 2,020 | Annual Meeting of the Association for Computational Linguistics | 726 | 44 | ['Computer Science'] |
2,005.00628 | Intermediate-Task Transfer Learning with Pretrained Models for Natural
Language Understanding: When and Why Does It Work? | ['Yada Pruksachatkun', 'Jason Phang', 'Haokun Liu', 'Phu Mon Htut', 'Xiaoyi Zhang', 'Richard Yuanzhe Pang', 'Clara Vania', 'Katharina Kann', 'Samuel R. Bowman'] | ['cs.CL'] | While pretrained models such as BERT have shown large gains across natural
language understanding tasks, their performance can be improved by further
training the model on a data-rich intermediate task, before fine-tuning it on a
target task. However, it is still poorly understood when and why
intermediate-task training is beneficial for a given target task. To
investigate this, we perform a large-scale study on the pretrained RoBERTa
model with 110 intermediate-target task combinations. We further evaluate all
trained models with 25 probing tasks meant to reveal the specific skills that
drive transfer. We observe that intermediate tasks requiring high-level
inference and reasoning abilities tend to work best. We also observe that
target task performance is strongly correlated with higher-level abilities such
as coreference resolution. However, we fail to observe more granular
correlations between probing and target task performance, highlighting the need
for further work on broad-coverage probing benchmarks. We also observe evidence
that the forgetting of knowledge learned during pretraining may limit our
analysis, highlighting the need for further work on transfer learning methods
in these settings. | 2020-05-01T21:49:34Z | ACL 2020 | null | null | Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? | ['Yada Pruksachatkun', 'Jason Phang', 'Haokun Liu', 'Phu Mon Htut', 'Xiaoyi Zhang', 'Richard Yuanzhe Pang', 'Clara Vania', 'Katharina Kann', 'Samuel R. Bowman'] | 2,020 | Annual Meeting of the Association for Computational Linguistics | 197 | 60 | ['Computer Science'] |
2,005.0063 | KLEJ: Comprehensive Benchmark for Polish Language Understanding | ['Piotr Rybak', 'Robert Mroczkowski', 'Janusz Tracz', 'Ireneusz Gawlik'] | ['cs.CL'] | In recent years, a series of Transformer-based models unlocked major
improvements in general natural language understanding (NLU) tasks. Such a fast
pace of research would not be possible without general NLU benchmarks, which
allow for a fair comparison of the proposed methods. However, such benchmarks
are available only for a handful of languages. To alleviate this issue, we
introduce a comprehensive multi-task benchmark for the Polish language
understanding, accompanied by an online leaderboard. It consists of a diverse
set of tasks, adopted from existing datasets for named entity recognition,
question-answering, textual entailment, and others. We also introduce a new
sentiment analysis task for the e-commerce domain, named Allegro Reviews (AR).
To ensure a common evaluation scheme and promote models that generalize to
different NLU tasks, the benchmark includes datasets from varying domains and
applications. Additionally, we release HerBERT, a Transformer-based model
trained specifically for the Polish language, which has the best average
performance and obtains the best results for three out of nine tasks. Finally,
we provide an extensive evaluation, including several standard baselines and
recently proposed, multilingual Transformer-based models. | 2020-05-01T21:55:40Z | null | null | null | null | null | null | null | null | null | null |
2,005.00661 | On Faithfulness and Factuality in Abstractive Summarization | ['Joshua Maynez', 'Shashi Narayan', 'Bernd Bohnet', 'Ryan McDonald'] | ['cs.CL'] | It is well known that the standard likelihood training and approximate
decoding objectives in neural text generation models lead to less human-like
responses for open-ended tasks such as language modeling and story generation.
In this paper we have analyzed limitations of these models for abstractive
document summarization and found that these models are highly prone to
hallucinate content that is unfaithful to the input document. We conducted a
large scale human evaluation of several neural abstractive summarization
systems to better understand the types of hallucinations they produce. Our
human annotators found substantial amounts of hallucinated content in all model
generated summaries. However, our analysis does show that pretrained models are
better summarizers not only in terms of raw metrics, i.e., ROUGE, but also in
generating faithful and factual summaries as evaluated by humans. Furthermore,
we show that textual entailment measures better correlate with faithfulness
than standard metrics, potentially leading the way to automatic evaluation
metrics as well as training and decoding criteria. | 2020-05-02T00:09:16Z | ACL 2020, 14 pages | null | null | null | null | null | null | null | null | null |
2,005.01107 | Simplifying Paragraph-level Question Generation via Transformer Language
Models | ['Luis Enrico Lopez', 'Diane Kathryn Cruz', 'Jan Christian Blaise Cruz', 'Charibeth Cheng'] | ['cs.CL'] | Question generation (QG) is a natural language generation task where a model
is trained to ask questions corresponding to some input text. Most recent
approaches frame QG as a sequence-to-sequence problem and rely on additional
features and mechanisms to increase performance; however, these often increase
model complexity, and can rely on auxiliary data unavailable in practical use.
A single Transformer-based unidirectional language model leveraging transfer
learning can be used to produce high quality questions while disposing of
additional task-specific complexity. Our QG model, finetuned from GPT-2 Small,
outperforms several paragraph-level QG baselines on the SQuAD dataset by 0.95
METEOR points. Human evaluators rated questions as easy to answer, relevant to
their context paragraph, and corresponding well to natural human speech. Also
introduced is a new set of baseline scores on the RACE dataset, which has not
previously been used for QG tasks. Further experimentation with varying model
capacities and datasets with non-identification type questions is recommended
in order to further verify the robustness of pretrained Transformer-based LMs
as question generators. | 2020-05-03T14:57:24Z | To appear in PRICAI 2021. Formerly titled "Transformer-based
End-to-End Question Generation." | null | null | Simplifying Paragraph-Level Question Generation via Transformer Language Models | ['Luis Enrico Lopez', 'Diane Kathryn Cruz', 'Jan Christian Blaise Cruz', 'C. Cheng'] | 2,020 | Pacific Rim International Conference on Artificial Intelligence | 28 | 20 | ['Computer Science'] |
2,005.01643 | Offline Reinforcement Learning: Tutorial, Review, and Perspectives on
Open Problems | ['Sergey Levine', 'Aviral Kumar', 'George Tucker', 'Justin Fu'] | ['cs.LG', 'cs.AI', 'stat.ML'] | In this tutorial article, we aim to provide the reader with the conceptual
tools needed to get started on research on offline reinforcement learning
algorithms: reinforcement learning algorithms that utilize previously collected
data, without additional online data collection. Offline reinforcement learning
algorithms hold tremendous promise for making it possible to turn large
datasets into powerful decision making engines. Effective offline reinforcement
learning methods would be able to extract policies with the maximum possible
utility out of the available data, thereby allowing automation of a wide range
of decision-making domains, from healthcare and education to robotics. However,
the limitations of current algorithms make this difficult. We will aim to
provide the reader with an understanding of these challenges, particularly in
the context of modern deep reinforcement learning methods, and describe some
potential solutions that have been explored in recent work to mitigate these
challenges, along with recent applications, and a discussion of perspectives on
open problems in the field. | 2020-05-04T17:00:15Z | null | null | null | null | null | null | null | null | null | null |
2,005.01996 | NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and
Results | ['Andreas Lugmayr', 'Martin Danelljan', 'Radu Timofte', 'Namhyuk Ahn', 'Dongwoon Bai', 'Jie Cai', 'Yun Cao', 'Junyang Chen', 'Kaihua Cheng', 'SeYoung Chun', 'Wei Deng', 'Mostafa El-Khamy', 'Chiu Man Ho', 'Xiaozhong Ji', 'Amin Kheradmand', 'Gwantae Kim', 'Hanseok Ko', 'Kanghyu Lee', 'Jungwon Lee', 'Hao Li', 'Ziluan Liu', 'Zhi-Song Liu', 'Shuai Liu', 'Yunhua Lu', 'Zibo Meng', 'Pablo Navarrete Michelini', 'Christian Micheloni', 'Kalpesh Prajapati', 'Haoyu Ren', 'Yong Hyeok Seo', 'Wan-Chi Siu', 'Kyung-Ah Sohn', 'Ying Tai', 'Rao Muhammad Umer', 'Shuangquan Wang', 'Huibing Wang', 'Timothy Haoning Wu', 'Haoning Wu', 'Biao Yang', 'Fuzhi Yang', 'Jaejun Yoo', 'Tongtong Zhao', 'Yuanbo Zhou', 'Haijie Zhuo', 'Ziyao Zong', 'Xueyi Zou'] | ['eess.IV', 'cs.CV'] | This paper reviews the NTIRE 2020 challenge on real world super-resolution.
It focuses on the participating methods and final results. The challenge
addresses the real world setting, where paired true high and low-resolution
images are unavailable. For training, only one set of source input images is
therefore provided along with a set of unpaired high-quality target images. In
Track 1: Image Processing artifacts, the aim is to super-resolve images with
synthetically generated image processing artifacts. This allows for
quantitative benchmarking of the approaches \wrt a ground-truth image. In Track
2: Smartphone Images, real low-quality smart phone images have to be
super-resolved. In both tracks, the ultimate goal is to achieve the best
perceptual quality, evaluated using a human study. This is the second challenge
on the subject, following AIM 2019, targeting to advance the state-of-the-art
in super-resolution. To measure the performance we use the benchmark protocol
from AIM 2019. In total 22 teams competed in the final testing phase,
demonstrating new and innovative solutions to the problem. | 2020-05-05T08:17:04Z | null | null | null | null | null | null | null | null | null | null |
2,005.02068 | Establishing Baselines for Text Classification in Low-Resource Languages | ['Jan Christian Blaise Cruz', 'Charibeth Cheng'] | ['cs.CL'] | While transformer-based finetuning techniques have proven effective in tasks
that involve low-resource, low-data environments, a lack of properly
established baselines and benchmark datasets make it hard to compare different
approaches that are aimed at tackling the low-resource setting. In this work,
we provide three contributions. First, we introduce two previously unreleased
datasets as benchmark datasets for text classification and low-resource
multilabel text classification for the low-resource language Filipino. Second,
we pretrain better BERT and DistilBERT models for use within the Filipino
setting. Third, we introduce a simple degradation test that benchmarks a
model's resistance to performance degradation as the number of training samples
are reduced. We analyze our pretrained model's degradation speeds and look
towards the use of this method for comparing models aimed at operating within
the low-resource setting. We release all our models and datasets for the
research community to use. | 2020-05-05T11:17:07Z | We release all our models, finetuning code, and data at
https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks | null | null | null | null | null | null | null | null | null |
2,005.02539 | Speak to your Parser: Interactive Text-to-SQL with Natural Language
Feedback | ['Ahmed Elgohary', 'Saghar Hosseini', 'Ahmed Hassan Awadallah'] | ['cs.CL'] | We study the task of semantic parse correction with natural language
feedback. Given a natural language utterance, most semantic parsing systems
pose the problem as one-shot translation where the utterance is mapped to a
corresponding logical form. In this paper, we investigate a more interactive
scenario where humans can further interact with the system by providing
free-form natural language feedback to correct the system when it generates an
inaccurate interpretation of an initial utterance. We focus on natural language
to SQL systems and construct, SPLASH, a dataset of utterances, incorrect SQL
interpretations and the corresponding natural language feedback. We compare
various reference models for the correction task and show that incorporating
such a rich form of feedback can significantly improve the overall semantic
parsing accuracy while retaining the flexibility of natural language
interaction. While we estimated human correction accuracy is 81.5%, our best
model achieves only 25.1%, which leaves a large gap for improvement in future
research. SPLASH is publicly available at https://aka.ms/Splash_dataset. | 2020-05-05T23:58:09Z | ACL 2020 | null | null | null | null | null | null | null | null | null |
2,005.03521 | The Danish Gigaword Project | ['Leon Strømberg-Derczynski', 'Manuel R. Ciosici', 'Rebekah Baglini', 'Morten H. Christiansen', 'Jacob Aarup Dalsgaard', 'Riccardo Fusaroli', 'Peter Juel Henrichsen', 'Rasmus Hvingelby', 'Andreas Kirkedal', 'Alex Speed Kjeldsen', 'Claus Ladefoged', 'Finn Årup Nielsen', 'Malte Lau Petersen', 'Jonathan Hvithamar Rystrøm', 'Daniel Varab'] | ['cs.CL'] | Danish language technology has been hindered by a lack of broad-coverage
corpora at the scale modern NLP prefers. This paper describes the Danish
Gigaword Corpus, the result of a focused effort to provide a diverse and
freely-available one billion word corpus of Danish text. The Danish Gigaword
corpus covers a wide array of time periods, domains, speakers' socio-economic
status, and Danish dialects. | 2020-05-07T14:40:56Z | Identical to the NoDaLiDa 2021 version | null | null | The Danish Gigaword Corpus | ['Leon Derczynski', 'Manuel R. Ciosici', 'R. Baglini', 'Morten H. Christiansen', 'Jacob Aarup Dalsgaard', 'Riccardo Fusaroli', 'P. Henrichsen', 'Rasmus Hvingelby', 'Andreas Søeborg Kirkedal', 'Alex Speed Kjeldsen', 'Claus Ladefoged', 'F. Nielsen', 'Jens Madsen', 'M. Petersen', 'Jonathan H. Rystrøm', 'Daniel Varab'] | 2,020 | Nordic Conference of Computational Linguistics | 19 | 34 | ['Computer Science'] |
2,005.03754 | FEQA: A Question Answering Evaluation Framework for Faithfulness
Assessment in Abstractive Summarization | ['Esin Durmus', 'He He', 'Mona Diab'] | ['cs.CL'] | Neural abstractive summarization models are prone to generate content
inconsistent with the source document, i.e. unfaithful. Existing automatic
metrics do not capture such mistakes effectively. We tackle the problem of
evaluating faithfulness of a generated summary given its source document. We
first collected human annotations of faithfulness for outputs from numerous
models on two datasets. We find that current models exhibit a trade-off between
abstractiveness and faithfulness: outputs with less word overlap with the
source document are more likely to be unfaithful. Next, we propose an automatic
question answering (QA) based metric for faithfulness, FEQA, which leverages
recent advances in reading comprehension. Given question-answer pairs generated
from the summary, a QA model extracts answers from the document; non-matched
answers indicate unfaithful information in the summary. Among metrics based on
word overlap, embedding similarity, and learned language understanding models,
our QA-based metric has significantly higher correlation with human
faithfulness scores, especially on highly abstractive summaries. | 2020-05-07T21:00:08Z | Accepted to ACL 2020 | null | 10.18653/v1/2020.acl-main.454 | null | null | null | null | null | null | null |
2,005.04132 | Asteroid: the PyTorch-based audio source separation toolkit for
researchers | ['Manuel Pariente', 'Samuele Cornell', 'Joris Cosentino', 'Sunit Sivasankaran', 'Efthymios Tzinis', 'Jens Heitkaemper', 'Michel Olvera', 'Fabian-Robert Stöter', 'Mathieu Hu', 'Juan M. Martín-Doñas', 'David Ditter', 'Ariel Frank', 'Antoine Deleforge', 'Emmanuel Vincent'] | ['eess.AS', 'cs.SD'] | This paper describes Asteroid, the PyTorch-based audio source separation
toolkit for researchers. Inspired by the most successful neural source
separation systems, it provides all neural building blocks required to build
such a system. To improve reproducibility, Kaldi-style recipes on common audio
source separation datasets are also provided. This paper describes the software
architecture of Asteroid and its most important features. By showing
experimental results obtained with Asteroid's recipes, we show that our
implementations are at least on par with most results reported in reference
papers. The toolkit is publicly available at
https://github.com/mpariente/asteroid . | 2020-05-08T16:18:34Z | Submitted to Interspeech 2020 | null | null | null | null | null | null | null | null | null |
2,005.05106 | Multi-band MelGAN: Faster Waveform Generation for High-Quality
Text-to-Speech | ['Geng Yang', 'Shan Yang', 'Kai Liu', 'Peng Fang', 'Wei Chen', 'Lei Xie'] | ['cs.SD', 'eess.AS'] | In this paper, we propose multi-band MelGAN, a much faster waveform
generation model targeting to high-quality text-to-speech. Specifically, we
improve the original MelGAN by the following aspects. First, we increase the
receptive field of the generator, which is proven to be beneficial to speech
generation. Second, we substitute the feature matching loss with the
multi-resolution STFT loss to better measure the difference between fake and
real speech. Together with pre-training, this improvement leads to both better
quality and better training stability. More importantly, we extend MelGAN with
multi-band processing: the generator takes mel-spectrograms as input and
produces sub-band signals which are subsequently summed back to full-band
signals as discriminator input. The proposed multi-band MelGAN has achieved
high MOS of 4.34 and 4.22 in waveform generation and TTS, respectively. With
only 1.91M parameters, our model effectively reduces the total computational
complexity of the original MelGAN from 5.85 to 0.95 GFLOPS. Our Pytorch
implementation, which will be open-resourced shortly, can achieve a real-time
factor of 0.03 on CPU without hardware specific optimization. | 2020-05-11T13:48:41Z | Submitted to Interspeech2020 | null | null | null | null | null | null | null | null | null |
2,005.05535 | DeepFaceLab: Integrated, flexible and extensible face-swapping framework | ['Ivan Perov', 'Daiheng Gao', 'Nikolay Chervoniy', 'Kunlin Liu', 'Sugasa Marangonda', 'Chris Umé', 'Dpfks', 'Carl Shift Facenheim', 'Luis RP', 'Jian Jiang', 'Sheng Zhang', 'Pingyu Wu', 'Bo Zhou', 'Weiming Zhang'] | ['cs.CV', 'cs.LG', 'cs.MM', 'eess.IV'] | Deepfake defense not only requires the research of detection but also
requires the efforts of generation methods. However, current deepfake methods
suffer the effects of obscure workflow and poor performance. To solve this
problem, we present DeepFaceLab, the current dominant deepfake framework for
face-swapping. It provides the necessary tools as well as an easy-to-use way to
conduct high-quality face-swapping. It also offers a flexible and loose
coupling structure for people who need to strengthen their pipeline with other
features without writing complicated boilerplate code. We detail the principles
that drive the implementation of DeepFaceLab and introduce its pipeline,
through which every aspect of the pipeline can be modified painlessly by users
to achieve their customization purpose. It is noteworthy that DeepFaceLab could
achieve cinema-quality results with high fidelity. We demonstrate the advantage
of our system by comparing our approach with other face-swapping methods.For
more information, please visit:https://github.com/iperov/DeepFaceLab/. | 2020-05-12T03:26:55Z | null | null | null | Deepfacelab: Integrated, flexible and extensible face-swapping framework | ['Kunlin Liu', 'Ivan Perov', 'Daiheng Gao', 'Nikolay Chervoniy', 'Wenbo Zhou', 'Weiming Zhang'] | 2,020 | Pattern Recognition | 232 | 46 | ['Computer Science', 'Engineering'] |
2,005.05635 | SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis | ['Hao Tian', 'Can Gao', 'Xinyan Xiao', 'Hao Liu', 'Bolei He', 'Hua Wu', 'Haifeng Wang', 'Feng Wu'] | ['cs.CL'] | Recently, sentiment analysis has seen remarkable advance with the help of
pre-training approaches. However, sentiment knowledge, such as sentiment words
and aspect-sentiment pairs, is ignored in the process of pre-training, despite
the fact that they are widely used in traditional sentiment analysis
approaches. In this paper, we introduce Sentiment Knowledge Enhanced
Pre-training (SKEP) in order to learn a unified sentiment representation for
multiple sentiment analysis tasks. With the help of automatically-mined
knowledge, SKEP conducts sentiment masking and constructs three sentiment
knowledge prediction objectives, so as to embed sentiment information at the
word, polarity and aspect level into pre-trained sentiment representation. In
particular, the prediction of aspect-sentiment pairs is converted into
multi-label classification, aiming to capture the dependency between words in a
pair. Experiments on three kinds of sentiment tasks show that SKEP
significantly outperforms strong pre-training baseline, and achieves new
state-of-the-art results on most of the test datasets. We release our code at
https://github.com/baidu/Senta. | 2020-05-12T09:23:32Z | Accepted by ACL2020 | null | null | null | null | null | null | null | null | null |
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