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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,009.01719 | Grounded Language Learning Fast and Slow | ['Felix Hill', 'Olivier Tieleman', 'Tamara von Glehn', 'Nathaniel Wong', 'Hamza Merzic', 'Stephen Clark'] | ['cs.CL', 'cs.AI'] | Recent work has shown that large text-based neural language models, trained
with conventional supervised learning objectives, acquire a surprising
propensity for few- and one-shot learning. Here, we show that an embodied agent
situated in a simulated 3D world, and endowed with a novel dual-coding external
memory, can exhibit similar one-shot word learning when trained with
conventional reinforcement learning algorithms. After a single introduction to
a novel object via continuous visual perception and a language prompt ("This is
a dax"), the agent can re-identify the object and manipulate it as instructed
("Put the dax on the bed"). In doing so, it seamlessly integrates short-term,
within-episode knowledge of the appropriate referent for the word "dax" with
long-term lexical and motor knowledge acquired across episodes (i.e. "bed" and
"putting"). We find that, under certain training conditions and with a
particular memory writing mechanism, the agent's one-shot word-object binding
generalizes to novel exemplars within the same ShapeNet category, and is
effective in settings with unfamiliar numbers of objects. We further show how
dual-coding memory can be exploited as a signal for intrinsic motivation,
stimulating the agent to seek names for objects that may be useful for later
executing instructions. Together, the results demonstrate that deep neural
networks can exploit meta-learning, episodic memory and an explicitly
multi-modal environment to account for 'fast-mapping', a fundamental pillar of
human cognitive development and a potentially transformative capacity for
agents that interact with human users. | 2020-09-03T14:52:03Z | null | null | null | null | null | null | null | null | null | null |
2,009.02252 | KILT: a Benchmark for Knowledge Intensive Language Tasks | ['Fabio Petroni', 'Aleksandra Piktus', 'Angela Fan', 'Patrick Lewis', 'Majid Yazdani', 'Nicola De Cao', 'James Thorne', 'Yacine Jernite', 'Vladimir Karpukhin', 'Jean Maillard', 'Vassilis Plachouras', 'Tim Rocktäschel', 'Sebastian Riedel'] | ['cs.CL', 'cs.AI', 'cs.IR', 'cs.LG'] | Challenging problems such as open-domain question answering, fact checking,
slot filling and entity linking require access to large, external knowledge
sources. While some models do well on individual tasks, developing general
models is difficult as each task might require computationally expensive
indexing of custom knowledge sources, in addition to dedicated infrastructure.
To catalyze research on models that condition on specific information in large
textual resources, we present a benchmark for knowledge-intensive language
tasks (KILT). All tasks in KILT are grounded in the same snapshot of Wikipedia,
reducing engineering turnaround through the re-use of components, as well as
accelerating research into task-agnostic memory architectures. We test both
task-specific and general baselines, evaluating downstream performance in
addition to the ability of the models to provide provenance. We find that a
shared dense vector index coupled with a seq2seq model is a strong baseline,
outperforming more tailor-made approaches for fact checking, open-domain
question answering and dialogue, and yielding competitive results on entity
linking and slot filling, by generating disambiguated text. KILT data and code
are available at https://github.com/facebookresearch/KILT. | 2020-09-04T15:32:19Z | accepted at NAACL 2021 | null | null | null | null | null | null | null | null | null |
2,009.033 | Measuring Massive Multitask Language Understanding | ['Dan Hendrycks', 'Collin Burns', 'Steven Basart', 'Andy Zou', 'Mantas Mazeika', 'Dawn Song', 'Jacob Steinhardt'] | ['cs.CY', 'cs.AI', 'cs.CL', 'cs.LG'] | We propose a new test to measure a text model's multitask accuracy. The test
covers 57 tasks including elementary mathematics, US history, computer science,
law, and more. To attain high accuracy on this test, models must possess
extensive world knowledge and problem solving ability. We find that while most
recent models have near random-chance accuracy, the very largest GPT-3 model
improves over random chance by almost 20 percentage points on average. However,
on every one of the 57 tasks, the best models still need substantial
improvements before they can reach expert-level accuracy. Models also have
lopsided performance and frequently do not know when they are wrong. Worse,
they still have near-random accuracy on some socially important subjects such
as morality and law. By comprehensively evaluating the breadth and depth of a
model's academic and professional understanding, our test can be used to
analyze models across many tasks and to identify important shortcomings. | 2020-09-07T17:59:25Z | ICLR 2021; the test and code is available at
https://github.com/hendrycks/test | null | null | Measuring Massive Multitask Language Understanding | ['Dan Hendrycks', 'Collin Burns', 'Steven Basart', 'Andy Zou', 'Mantas Mazeika', 'D. Song', 'J. Steinhardt'] | 2,020 | International Conference on Learning Representations | 4,587 | 35 | ['Computer Science'] |
2,009.04534 | Pay Attention when Required | ['Swetha Mandava', 'Szymon Migacz', 'Alex Fit Florea'] | ['cs.LG', 'cs.CL'] | Transformer-based models consist of interleaved feed-forward blocks - that
capture content meaning, and relatively more expensive self-attention blocks -
that capture context meaning. In this paper, we explored trade-offs and
ordering of the blocks to improve upon the current Transformer architecture and
proposed PAR Transformer. It needs 35% lower compute time than Transformer-XL
achieved by replacing ~63% of the self-attention blocks with feed-forward
blocks, and retains the perplexity on WikiText-103 language modelling
benchmark. We further validated our results on text8 and enwiki8 datasets, as
well as on the BERT model. | 2020-09-09T19:39:15Z | 9 pages, 5 figures, 7 tables | null | null | null | null | null | null | null | null | null |
2,009.05166 | FILTER: An Enhanced Fusion Method for Cross-lingual Language
Understanding | ['Yuwei Fang', 'Shuohang Wang', 'Zhe Gan', 'Siqi Sun', 'Jingjing Liu'] | ['cs.CL'] | Large-scale cross-lingual language models (LM), such as mBERT, Unicoder and
XLM, have achieved great success in cross-lingual representation learning.
However, when applied to zero-shot cross-lingual transfer tasks, most existing
methods use only single-language input for LM finetuning, without leveraging
the intrinsic cross-lingual alignment between different languages that proves
essential for multilingual tasks. In this paper, we propose FILTER, an enhanced
fusion method that takes cross-lingual data as input for XLM finetuning.
Specifically, FILTER first encodes text input in the source language and its
translation in the target language independently in the shallow layers, then
performs cross-language fusion to extract multilingual knowledge in the
intermediate layers, and finally performs further language-specific encoding.
During inference, the model makes predictions based on the text input in the
target language and its translation in the source language. For simple tasks
such as classification, translated text in the target language shares the same
label as the source language. However, this shared label becomes less accurate
or even unavailable for more complex tasks such as question answering, NER and
POS tagging. To tackle this issue, we further propose an additional
KL-divergence self-teaching loss for model training, based on auto-generated
soft pseudo-labels for translated text in the target language. Extensive
experiments demonstrate that FILTER achieves new state of the art on two
challenging multilingual multi-task benchmarks, XTREME and XGLUE. | 2020-09-10T22:42:15Z | Accepted to AAAI 2021; Top-1 Performance on XTREME
(https://sites.research.google/xtreme, September 8, 2020) and XGLUE
(https://microsoft.github.io/XGLUE, September 14, 2020) benchmark | null | null | FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding | ['Yuwei Fang', 'Shuohang Wang', 'Zhe Gan', 'S. Sun', 'Jingjing Liu'] | 2,020 | AAAI Conference on Artificial Intelligence | 58 | 33 | ['Computer Science'] |
2,009.05387 | IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural
Language Understanding | ['Bryan Wilie', 'Karissa Vincentio', 'Genta Indra Winata', 'Samuel Cahyawijaya', 'Xiaohong Li', 'Zhi Yuan Lim', 'Sidik Soleman', 'Rahmad Mahendra', 'Pascale Fung', 'Syafri Bahar', 'Ayu Purwarianti'] | ['cs.CL'] | Although Indonesian is known to be the fourth most frequently used language
over the internet, the research progress on this language in the natural
language processing (NLP) is slow-moving due to a lack of available resources.
In response, we introduce the first-ever vast resource for the training,
evaluating, and benchmarking on Indonesian natural language understanding
(IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence
classification to pair-sentences sequence labeling with different levels of
complexity. The datasets for the tasks lie in different domains and styles to
ensure task diversity. We also provide a set of Indonesian pre-trained models
(IndoBERT) trained from a large and clean Indonesian dataset Indo4B collected
from publicly available sources such as social media texts, blogs, news, and
websites. We release baseline models for all twelve tasks, as well as the
framework for benchmark evaluation, and thus it enables everyone to benchmark
their system performances. | 2020-09-11T12:21:41Z | This paper will be presented in AACL-IJCNLP 2020 (with new results
and acknowledgment) | null | null | null | null | null | null | null | null | null |
2,009.06978 | Dialogue Response Ranking Training with Large-Scale Human Feedback Data | ['Xiang Gao', 'Yizhe Zhang', 'Michel Galley', 'Chris Brockett', 'Bill Dolan'] | ['cs.CL'] | Existing open-domain dialog models are generally trained to minimize the
perplexity of target human responses. However, some human replies are more
engaging than others, spawning more followup interactions. Current
conversational models are increasingly capable of producing turns that are
context-relevant, but in order to produce compelling agents, these models need
to be able to predict and optimize for turns that are genuinely engaging. We
leverage social media feedback data (number of replies and upvotes) to build a
large-scale training dataset for feedback prediction. To alleviate possible
distortion between the feedback and engagingness, we convert the ranking
problem to a comparison of response pairs which involve few confounding
factors. We trained DialogRPT, a set of GPT-2 based models on 133M pairs of
human feedback data and the resulting ranker outperformed several baselines.
Particularly, our ranker outperforms the conventional dialog perplexity
baseline with a large margin on predicting Reddit feedback. We finally combine
the feedback prediction models and a human-like scoring model to rank the
machine-generated dialog responses. Crowd-sourced human evaluation shows that
our ranking method correlates better with real human preferences than baseline
models. | 2020-09-15T10:50:05Z | Accepted to appear at EMNLP 2020 | null | null | Dialogue Response Ranking Training with Large-Scale Human Feedback Data | ['Xiang Gao', 'Yizhe Zhang', 'Michel Galley', 'Chris Brockett', 'Bill Dolan'] | 2,020 | Conference on Empirical Methods in Natural Language Processing | 107 | 35 | ['Computer Science'] |
2,009.07047 | Old Photo Restoration via Deep Latent Space Translation | ['Ziyu Wan', 'Bo Zhang', 'Dongdong Chen', 'Pan Zhang', 'Dong Chen', 'Jing Liao', 'Fang Wen'] | ['cs.CV', 'cs.GR'] | We propose to restore old photos that suffer from severe degradation through
a deep learning approach. Unlike conventional restoration tasks that can be
solved through supervised learning, the degradation in real photos is complex
and the domain gap between synthetic images and real old photos makes the
network fail to generalize. Therefore, we propose a novel triplet domain
translation network by leveraging real photos along with massive synthetic
image pairs. Specifically, we train two variational autoencoders (VAEs) to
respectively transform old photos and clean photos into two latent spaces. And
the translation between these two latent spaces is learned with synthetic
paired data. This translation generalizes well to real photos because the
domain gap is closed in the compact latent space. Besides, to address multiple
degradations mixed in one old photo, we design a global branch with apartial
nonlocal block targeting to the structured defects, such as scratches and dust
spots, and a local branch targeting to the unstructured defects, such as noises
and blurriness. Two branches are fused in the latent space, leading to improved
capability to restore old photos from multiple defects. Furthermore, we apply
another face refinement network to recover fine details of faces in the old
photos, thus ultimately generating photos with enhanced perceptual quality.
With comprehensive experiments, the proposed pipeline demonstrates superior
performance over state-of-the-art methods as well as existing commercial tools
in terms of visual quality for old photos restoration. | 2020-09-14T08:51:53Z | 15 pages. arXiv admin note: substantial text overlap with
arXiv:2004.09484 | null | null | Old Photo Restoration via Deep Latent Space Translation | ['Ziyu Wan', 'Bo Zhang', 'Dongdong Chen', 'P. Zhang', 'Dong Chen', 'Jing Liao', 'Fang Wen'] | 2,020 | IEEE Transactions on Pattern Analysis and Machine Intelligence | 68 | 88 | ['Computer Science', 'Medicine'] |
2,009.07185 | Critical Thinking for Language Models | ['Gregor Betz', 'Christian Voigt', 'Kyle Richardson'] | ['cs.CL', 'cs.AI'] | This paper takes a first step towards a critical thinking curriculum for
neural auto-regressive language models. We introduce a synthetic corpus of
deductively valid arguments, and generate artificial argumentative texts to
train and evaluate GPT-2. Significant transfer learning effects can be
observed: Training a model on three simple core schemes allows it to accurately
complete conclusions of different, and more complex types of arguments, too.
The language models generalize the core argument schemes in a correct way.
Moreover, we obtain consistent and promising results for NLU benchmarks. In
particular, pre-training on the argument schemes raises zero-shot accuracy on
the GLUE diagnostics by up to 15 percentage points. The findings suggest that
intermediary pre-training on texts that exemplify basic reasoning abilities
(such as typically covered in critical thinking textbooks) might help language
models to acquire a broad range of reasoning skills. The synthetic
argumentative texts presented in this paper are a promising starting point for
building such a "critical thinking curriculum for language models." | 2020-09-15T15:49:19Z | null | null | null | null | null | null | null | null | null | null |
2,009.08366 | GraphCodeBERT: Pre-training Code Representations with Data Flow | ['Daya Guo', 'Shuo Ren', 'Shuai Lu', 'Zhangyin Feng', 'Duyu Tang', 'Shujie Liu', 'Long Zhou', 'Nan Duan', 'Alexey Svyatkovskiy', 'Shengyu Fu', 'Michele Tufano', 'Shao Kun Deng', 'Colin Clement', 'Dawn Drain', 'Neel Sundaresan', 'Jian Yin', 'Daxin Jiang', 'Ming Zhou'] | ['cs.SE', 'cs.CL'] | Pre-trained models for programming language have achieved dramatic empirical
improvements on a variety of code-related tasks such as code search, code
completion, code summarization, etc. However, existing pre-trained models
regard a code snippet as a sequence of tokens, while ignoring the inherent
structure of code, which provides crucial code semantics and would enhance the
code understanding process. We present GraphCodeBERT, a pre-trained model for
programming language that considers the inherent structure of code. Instead of
taking syntactic-level structure of code like abstract syntax tree (AST), we
use data flow in the pre-training stage, which is a semantic-level structure of
code that encodes the relation of "where-the-value-comes-from" between
variables. Such a semantic-level structure is neat and does not bring an
unnecessarily deep hierarchy of AST, the property of which makes the model more
efficient. We develop GraphCodeBERT based on Transformer. In addition to using
the task of masked language modeling, we introduce two structure-aware
pre-training tasks. One is to predict code structure edges, and the other is to
align representations between source code and code structure. We implement the
model in an efficient way with a graph-guided masked attention function to
incorporate the code structure. We evaluate our model on four tasks, including
code search, clone detection, code translation, and code refinement. Results
show that code structure and newly introduced pre-training tasks can improve
GraphCodeBERT and achieves state-of-the-art performance on the four downstream
tasks. We further show that the model prefers structure-level attentions over
token-level attentions in the task of code search. | 2020-09-17T15:25:56Z | Accepted by ICLR2021 | null | null | null | null | null | null | null | null | null |
2,009.0882 | FarsTail: A Persian Natural Language Inference Dataset | ['Hossein Amirkhani', 'Mohammad AzariJafari', 'Zohreh Pourjafari', 'Soroush Faridan-Jahromi', 'Zeinab Kouhkan', 'Azadeh Amirak'] | ['cs.CL'] | Natural language inference (NLI) is known as one of the central tasks in
natural language processing (NLP) which encapsulates many fundamental aspects
of language understanding. With the considerable achievements of data-hungry
deep learning methods in NLP tasks, a great amount of effort has been devoted
to develop more diverse datasets for different languages. In this paper, we
present a new dataset for the NLI task in the Persian language, also known as
Farsi, which is one of the dominant languages in the Middle East. This dataset,
named FarsTail, includes 10,367 samples which are provided in both the Persian
language as well as the indexed format to be useful for non-Persian
researchers. The samples are generated from 3,539 multiple-choice questions
with the least amount of annotator interventions in a way similar to the
SciTail dataset. A carefully designed multi-step process is adopted to ensure
the quality of the dataset. We also present the results of traditional and
state-of-the-art methods on FarsTail including different embedding methods such
as word2vec, fastText, ELMo, BERT, and LASER, as well as different modeling
approaches such as DecompAtt, ESIM, HBMP, and ULMFiT to provide a solid
baseline for the future research. The best obtained test accuracy is 83.38%
which shows that there is a big room for improving the current methods to be
useful for real-world NLP applications in different languages. We also
investigate the extent to which the models exploit superficial clues, also
known as dataset biases, in FarsTail, and partition the test set into easy and
hard subsets according to the success of biased models. The dataset is
available at https://github.com/dml-qom/FarsTail | 2020-09-18T13:04:04Z | null | Soft Computing (2023) | 10.1007/s00500-023-08959-3 | null | null | null | null | null | null | null |
2,009.09761 | DiffWave: A Versatile Diffusion Model for Audio Synthesis | ['Zhifeng Kong', 'Wei Ping', 'Jiaji Huang', 'Kexin Zhao', 'Bryan Catanzaro'] | ['eess.AS', 'cs.CL', 'cs.LG', 'cs.SD', 'stat.ML'] | In this work, we propose DiffWave, a versatile diffusion probabilistic model
for conditional and unconditional waveform generation. The model is
non-autoregressive, and converts the white noise signal into structured
waveform through a Markov chain with a constant number of steps at synthesis.
It is efficiently trained by optimizing a variant of variational bound on the
data likelihood. DiffWave produces high-fidelity audios in different waveform
generation tasks, including neural vocoding conditioned on mel spectrogram,
class-conditional generation, and unconditional generation. We demonstrate that
DiffWave matches a strong WaveNet vocoder in terms of speech quality (MOS: 4.44
versus 4.43), while synthesizing orders of magnitude faster. In particular, it
significantly outperforms autoregressive and GAN-based waveform models in the
challenging unconditional generation task in terms of audio quality and sample
diversity from various automatic and human evaluations. | 2020-09-21T11:20:38Z | ICLR 2021 (oral) | null | null | null | null | null | null | null | null | null |
2,009.10053 | Latin BERT: A Contextual Language Model for Classical Philology | ['David Bamman', 'Patrick J. Burns'] | ['cs.CL'] | We present Latin BERT, a contextual language model for the Latin language,
trained on 642.7 million words from a variety of sources spanning the Classical
era to the 21st century. In a series of case studies, we illustrate the
affordances of this language-specific model both for work in natural language
processing for Latin and in using computational methods for traditional
scholarship: we show that Latin BERT achieves a new state of the art for
part-of-speech tagging on all three Universal Dependency datasets for Latin and
can be used for predicting missing text (including critical emendations); we
create a new dataset for assessing word sense disambiguation for Latin and
demonstrate that Latin BERT outperforms static word embeddings; and we show
that it can be used for semantically-informed search by querying contextual
nearest neighbors. We publicly release trained models to help drive future work
in this space. | 2020-09-21T17:47:44Z | null | null | null | Latin BERT: A Contextual Language Model for Classical Philology | ['David Bamman', 'P. Burns'] | 2,020 | arXiv.org | 79 | 61 | ['Computer Science'] |
2,009.10277 | Constructing interval variables via faceted Rasch measurement and
multitask deep learning: a hate speech application | ['Chris J. Kennedy', 'Geoff Bacon', 'Alexander Sahn', 'Claudia von Vacano'] | ['cs.CL', 'cs.LG', 'cs.SI', 'I.2.7'] | We propose a general method for measuring complex variables on a continuous,
interval spectrum by combining supervised deep learning with the Constructing
Measures approach to faceted Rasch item response theory (IRT). We decompose the
target construct, hate speech in our case, into multiple constituent components
that are labeled as ordinal survey items. Those survey responses are
transformed via IRT into a debiased, continuous outcome measure. Our method
estimates the survey interpretation bias of the human labelers and eliminates
that influence on the generated continuous measure. We further estimate the
response quality of each labeler using faceted IRT, allowing responses from
low-quality labelers to be removed.
Our faceted Rasch scaling procedure integrates naturally with a multitask
deep learning architecture for automated prediction on new data. The ratings on
the theorized components of the target outcome are used as supervised, ordinal
variables for the neural networks' internal concept learning. We test the use
of an activation function (ordinal softmax) and loss function (ordinal
cross-entropy) designed to exploit the structure of ordinal outcome variables.
Our multitask architecture leads to a new form of model interpretation because
each continuous prediction can be directly explained by the constituent
components in the penultimate layer.
We demonstrate this new method on a dataset of 50,000 social media comments
sourced from YouTube, Twitter, and Reddit and labeled by 11,000 U.S.-based
Amazon Mechanical Turk workers to measure a continuous spectrum from hate
speech to counterspeech. We evaluate Universal Sentence Encoders, BERT, and
RoBERTa as language representation models for the comment text, and compare our
predictive accuracy to Google Jigsaw's Perspective API models, showing
significant improvement over this standard benchmark. | 2020-09-22T02:15:05Z | 35 pages, 10 figures | null | null | null | null | null | null | null | null | null |
2,009.10297 | CodeBLEU: a Method for Automatic Evaluation of Code Synthesis | ['Shuo Ren', 'Daya Guo', 'Shuai Lu', 'Long Zhou', 'Shujie Liu', 'Duyu Tang', 'Neel Sundaresan', 'Ming Zhou', 'Ambrosio Blanco', 'Shuai Ma'] | ['cs.SE', 'cs.CL'] | Evaluation metrics play a vital role in the growth of an area as it defines
the standard of distinguishing between good and bad models. In the area of code
synthesis, the commonly used evaluation metric is BLEU or perfect accuracy, but
they are not suitable enough to evaluate codes, because BLEU is originally
designed to evaluate the natural language, neglecting important syntactic and
semantic features of codes, and perfect accuracy is too strict thus it
underestimates different outputs with the same semantic logic. To remedy this,
we introduce a new automatic evaluation metric, dubbed CodeBLEU. It absorbs the
strength of BLEU in the n-gram match and further injects code syntax via
abstract syntax trees (AST) and code semantics via data-flow. We conduct
experiments by evaluating the correlation coefficient between CodeBLEU and
quality scores assigned by the programmers on three code synthesis tasks, i.e.,
text-to-code, code translation, and code refinement. Experimental results show
that our proposed CodeBLEU can achieve a better correlation with programmer
assigned scores compared with BLEU and accuracy. | 2020-09-22T03:10:49Z | 8 pages, 6 figures | null | null | CodeBLEU: a Method for Automatic Evaluation of Code Synthesis | ['Shuo Ren', 'Daya Guo', 'Shuai Lu', 'Long Zhou', 'Shujie Liu', 'Duyu Tang', 'M. Zhou', 'Ambrosio Blanco', 'Shuai Ma'] | 2,020 | arXiv.org | 546 | 32 | ['Computer Science'] |
2,009.11462 | RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language
Models | ['Samuel Gehman', 'Suchin Gururangan', 'Maarten Sap', 'Yejin Choi', 'Noah A. Smith'] | ['cs.CL'] | Pretrained neural language models (LMs) are prone to generating racist,
sexist, or otherwise toxic language which hinders their safe deployment. We
investigate the extent to which pretrained LMs can be prompted to generate
toxic language, and the effectiveness of controllable text generation
algorithms at preventing such toxic degeneration. We create and release
RealToxicityPrompts, a dataset of 100K naturally occurring, sentence-level
prompts derived from a large corpus of English web text, paired with toxicity
scores from a widely-used toxicity classifier. Using RealToxicityPrompts, we
find that pretrained LMs can degenerate into toxic text even from seemingly
innocuous prompts. We empirically assess several controllable generation
methods, and find that while data- or compute-intensive methods (e.g., adaptive
pretraining on non-toxic data) are more effective at steering away from
toxicity than simpler solutions (e.g., banning "bad" words), no current method
is failsafe against neural toxic degeneration. To pinpoint the potential cause
of such persistent toxic degeneration, we analyze two web text corpora used to
pretrain several LMs (including GPT-2; Radford et. al, 2019), and find a
significant amount of offensive, factually unreliable, and otherwise toxic
content. Our work provides a test bed for evaluating toxic generations by LMs
and stresses the need for better data selection processes for pretraining. | 2020-09-24T03:17:19Z | Findings in EMNLP 2020 | null | null | null | null | null | null | null | null | null |
2,009.11616 | N-LTP: An Open-source Neural Language Technology Platform for Chinese | ['Wanxiang Che', 'Yunlong Feng', 'Libo Qin', 'Ting Liu'] | ['cs.CL'] | We introduce \texttt{N-LTP}, an open-source neural language technology
platform supporting six fundamental Chinese NLP tasks: {lexical analysis}
(Chinese word segmentation, part-of-speech tagging, and named entity
recognition), {syntactic parsing} (dependency parsing), and {semantic parsing}
(semantic dependency parsing and semantic role labeling). Unlike the existing
state-of-the-art toolkits, such as \texttt{Stanza}, that adopt an independent
model for each task, \texttt{N-LTP} adopts the multi-task framework by using a
shared pre-trained model, which has the advantage of capturing the shared
knowledge across relevant Chinese tasks. In addition, a knowledge distillation
method \cite{DBLP:journals/corr/abs-1907-04829} where the single-task model
teaches the multi-task model is further introduced to encourage the multi-task
model to surpass its single-task teacher. Finally, we provide a collection of
easy-to-use APIs and a visualization tool to make users to use and view the
processing results more easily and directly. To the best of our knowledge, this
is the first toolkit to support six Chinese NLP fundamental tasks. Source code,
documentation, and pre-trained models are available at
\url{https://github.com/HIT-SCIR/ltp}. | 2020-09-24T11:45:39Z | Accepted to appear in EMNLP 2021 (Demo) | null | null | N-LTP: An Open-source Neural Language Technology Platform for Chinese | ['Wanxiang Che', 'ylfeng', 'Libo Qin', 'Ting Liu'] | 2,020 | Conference on Empirical Methods in Natural Language Processing | 113 | 38 | ['Computer Science'] |
2,009.12756 | Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval | ['Wenhan Xiong', 'Xiang Lorraine Li', 'Srini Iyer', 'Jingfei Du', 'Patrick Lewis', 'William Yang Wang', 'Yashar Mehdad', 'Wen-tau Yih', 'Sebastian Riedel', 'Douwe Kiela', 'Barlas Oğuz'] | ['cs.CL'] | We propose a simple and efficient multi-hop dense retrieval approach for
answering complex open-domain questions, which achieves state-of-the-art
performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER.
Contrary to previous work, our method does not require access to any
corpus-specific information, such as inter-document hyperlinks or
human-annotated entity markers, and can be applied to any unstructured text
corpus. Our system also yields a much better efficiency-accuracy trade-off,
matching the best published accuracy on HotpotQA while being 10 times faster at
inference time. | 2020-09-27T06:12:29Z | null | null | null | Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval | ['Wenhan Xiong', 'Xiang Lorraine Li', 'Srini Iyer', 'Jingfei Du', 'Patrick Lewis', 'William Yang Wang', 'Yashar Mehdad', 'Wen-tau Yih', 'Sebastian Riedel', 'Douwe Kiela', 'Barlas Oğuz'] | 2,020 | International Conference on Learning Representations | 194 | 61 | ['Computer Science'] |
2,009.13013 | SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer
Matching Retrieval | ['Tiancheng Zhao', 'Xiaopeng Lu', 'Kyusong Lee'] | ['cs.CL', 'cs.LG'] | We introduce SPARTA, a novel neural retrieval method that shows great promise
in performance, generalization, and interpretability for open-domain question
answering. Unlike many neural ranking methods that use dense vector nearest
neighbor search, SPARTA learns a sparse representation that can be efficiently
implemented as an Inverted Index. The resulting representation enables scalable
neural retrieval that does not require expensive approximate vector search and
leads to better performance than its dense counterpart. We validated our
approaches on 4 open-domain question answering (OpenQA) tasks and 11 retrieval
question answering (ReQA) tasks. SPARTA achieves new state-of-the-art results
across a variety of open-domain question answering tasks in both English and
Chinese datasets, including open SQuAD, Natuarl Question, CMRC and etc.
Analysis also confirms that the proposed method creates human interpretable
representation and allows flexible control over the trade-off between
performance and efficiency. | 2020-09-28T02:11:02Z | 11 pages | null | null | null | null | null | null | null | null | null |
2,009.13081 | What Disease does this Patient Have? A Large-scale Open Domain Question
Answering Dataset from Medical Exams | ['Di Jin', 'Eileen Pan', 'Nassim Oufattole', 'Wei-Hung Weng', 'Hanyi Fang', 'Peter Szolovits'] | ['cs.CL', 'cs.AI'] | Open domain question answering (OpenQA) tasks have been recently attracting
more and more attention from the natural language processing (NLP) community.
In this work, we present the first free-form multiple-choice OpenQA dataset for
solving medical problems, MedQA, collected from the professional medical board
exams. It covers three languages: English, simplified Chinese, and traditional
Chinese, and contains 12,723, 34,251, and 14,123 questions for the three
languages, respectively. We implement both rule-based and popular neural
methods by sequentially combining a document retriever and a machine
comprehension model. Through experiments, we find that even the current best
method can only achieve 36.7\%, 42.0\%, and 70.1\% of test accuracy on the
English, traditional Chinese, and simplified Chinese questions, respectively.
We expect MedQA to present great challenges to existing OpenQA systems and hope
that it can serve as a platform to promote much stronger OpenQA models from the
NLP community in the future. | 2020-09-28T05:07:51Z | Submitted to AAAI 2021 | null | null | What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams | ['Di Jin', 'Eileen Pan', 'Nassim Oufattole', 'W. Weng', 'Hanyi Fang', 'Peter Szolovits'] | 2,020 | Applied Sciences | 820 | 49 | ['Computer Science'] |
2,009.14725 | A Vietnamese Dataset for Evaluating Machine Reading Comprehension | ['Kiet Van Nguyen', 'Duc-Vu Nguyen', 'Anh Gia-Tuan Nguyen', 'Ngan Luu-Thuy Nguyen'] | ['cs.CL'] | Over 97 million people speak Vietnamese as their native language in the
world. However, there are few research studies on machine reading comprehension
(MRC) for Vietnamese, the task of understanding a text and answering questions
related to it. Due to the lack of benchmark datasets for Vietnamese, we present
the Vietnamese Question Answering Dataset (UIT-ViQuAD), a new dataset for the
low-resource language as Vietnamese to evaluate MRC models. This dataset
comprises over 23,000 human-generated question-answer pairs based on 5,109
passages of 174 Vietnamese articles from Wikipedia. In particular, we propose a
new process of dataset creation for Vietnamese MRC. Our in-depth analyses
illustrate that our dataset requires abilities beyond simple reasoning like
word matching and demands single-sentence and multiple-sentence inferences.
Besides, we conduct experiments on state-of-the-art MRC methods for English and
Chinese as the first experimental models on UIT-ViQuAD. We also estimate human
performance on the dataset and compare it to the experimental results of
powerful machine learning models. As a result, the substantial differences
between human performance and the best model performance on the dataset
indicate that improvements can be made on UIT-ViQuAD in future research. Our
dataset is freely available on our website to encourage the research community
to overcome challenges in Vietnamese MRC. | 2020-09-30T15:06:56Z | Accepted by The 28th International Conference on Computational
Linguistics (COLING 2020) | null | null | null | null | null | null | null | null | null |
2,009.14794 | Rethinking Attention with Performers | ['Krzysztof Choromanski', 'Valerii Likhosherstov', 'David Dohan', 'Xingyou Song', 'Andreea Gane', 'Tamas Sarlos', 'Peter Hawkins', 'Jared Davis', 'Afroz Mohiuddin', 'Lukasz Kaiser', 'David Belanger', 'Lucy Colwell', 'Adrian Weller'] | ['cs.LG', 'cs.CL', 'stat.ML'] | We introduce Performers, Transformer architectures which can estimate regular
(softmax) full-rank-attention Transformers with provable accuracy, but using
only linear (as opposed to quadratic) space and time complexity, without
relying on any priors such as sparsity or low-rankness. To approximate softmax
attention-kernels, Performers use a novel Fast Attention Via positive
Orthogonal Random features approach (FAVOR+), which may be of independent
interest for scalable kernel methods. FAVOR+ can be also used to efficiently
model kernelizable attention mechanisms beyond softmax. This representational
power is crucial to accurately compare softmax with other kernels for the first
time on large-scale tasks, beyond the reach of regular Transformers, and
investigate optimal attention-kernels. Performers are linear architectures
fully compatible with regular Transformers and with strong theoretical
guarantees: unbiased or nearly-unbiased estimation of the attention matrix,
uniform convergence and low estimation variance. We tested Performers on a rich
set of tasks stretching from pixel-prediction through text models to protein
sequence modeling. We demonstrate competitive results with other examined
efficient sparse and dense attention methods, showcasing effectiveness of the
novel attention-learning paradigm leveraged by Performers. | 2020-09-30T17:09:09Z | Published as a conference paper + oral presentation at ICLR 2021. 38
pages. See
https://github.com/google-research/google-research/tree/master/protein_lm for
protein language model code, and
https://github.com/google-research/google-research/tree/master/performer for
Performer code. See
https://ai.googleblog.com/2020/10/rethinking-attention-with-performers.html
for Google AI Blog | null | null | null | null | null | null | null | null | null |
2,010.00133 | CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked
Language Models | ['Nikita Nangia', 'Clara Vania', 'Rasika Bhalerao', 'Samuel R. Bowman'] | ['cs.CL', 'cs.AI'] | Pretrained language models, especially masked language models (MLMs) have
seen success across many NLP tasks. However, there is ample evidence that they
use the cultural biases that are undoubtedly present in the corpora they are
trained on, implicitly creating harm with biased representations. To measure
some forms of social bias in language models against protected demographic
groups in the US, we introduce the Crowdsourced Stereotype Pairs benchmark
(CrowS-Pairs). CrowS-Pairs has 1508 examples that cover stereotypes dealing
with nine types of bias, like race, religion, and age. In CrowS-Pairs a model
is presented with two sentences: one that is more stereotyping and another that
is less stereotyping. The data focuses on stereotypes about historically
disadvantaged groups and contrasts them with advantaged groups. We find that
all three of the widely-used MLMs we evaluate substantially favor sentences
that express stereotypes in every category in CrowS-Pairs. As work on building
less biased models advances, this dataset can be used as a benchmark to
evaluate progress. | 2020-09-30T22:38:40Z | EMNLP 2020 | null | null | null | null | null | null | null | null | null |
2,010.00571 | Understanding tables with intermediate pre-training | ['Julian Martin Eisenschlos', 'Syrine Krichene', 'Thomas Müller'] | ['cs.CL', 'cs.AI', 'cs.IR', 'cs.LG'] | Table entailment, the binary classification task of finding if a sentence is
supported or refuted by the content of a table, requires parsing language and
table structure as well as numerical and discrete reasoning. While there is
extensive work on textual entailment, table entailment is less well studied. We
adapt TAPAS (Herzig et al., 2020), a table-based BERT model, to recognize
entailment. Motivated by the benefits of data augmentation, we create a
balanced dataset of millions of automatically created training examples which
are learned in an intermediate step prior to fine-tuning. This new data is not
only useful for table entailment, but also for SQA (Iyyer et al., 2017), a
sequential table QA task. To be able to use long examples as input of BERT
models, we evaluate table pruning techniques as a pre-processing step to
drastically improve the training and prediction efficiency at a moderate drop
in accuracy. The different methods set the new state-of-the-art on the TabFact
(Chen et al., 2020) and SQA datasets. | 2020-10-01T17:43:27Z | Accepted to EMNLP Findings 2020 | null | null | Understanding tables with intermediate pre-training | ['Julian Martin Eisenschlos', 'Syrine Krichene', 'Thomas Müller'] | 2,020 | Findings | 121 | 58 | ['Computer Science'] |
2,010.00747 | Contrastive Learning of Medical Visual Representations from Paired
Images and Text | ['Yuhao Zhang', 'Hang Jiang', 'Yasuhide Miura', 'Christopher D. Manning', 'Curtis P. Langlotz'] | ['cs.CV', 'cs.CL', 'cs.LG'] | Learning visual representations of medical images (e.g., X-rays) is core to
medical image understanding but its progress has been held back by the scarcity
of human annotations. Existing work commonly relies on fine-tuning weights
transferred from ImageNet pretraining, which is suboptimal due to drastically
different image characteristics, or rule-based label extraction from the
textual report data paired with medical images, which is inaccurate and hard to
generalize. Meanwhile, several recent studies show exciting results from
unsupervised contrastive learning from natural images, but we find these
methods help little on medical images because of their high inter-class
similarity. We propose ConVIRT, an alternative unsupervised strategy to learn
medical visual representations by exploiting naturally occurring paired
descriptive text. Our new method of pretraining medical image encoders with the
paired text data via a bidirectional contrastive objective between the two
modalities is domain-agnostic, and requires no additional expert input. We test
ConVIRT by transferring our pretrained weights to 4 medical image
classification tasks and 2 zero-shot retrieval tasks, and show that it leads to
image representations that considerably outperform strong baselines in most
settings. Notably, in all 4 classification tasks, our method requires only 10\%
as much labeled training data as an ImageNet initialized counterpart to achieve
better or comparable performance, demonstrating superior data efficiency. | 2020-10-02T02:10:18Z | First published in 2020. Accepted at Machine Learning for Healthcare
(MLHC) 2022 | null | null | Contrastive Learning of Medical Visual Representations from Paired Images and Text | ['Yuhao Zhang', 'Hang Jiang', 'Yasuhide Miura', 'Christopher D. Manning', 'C. Langlotz'] | 2,020 | Machine Learning in Health Care | 774 | 59 | ['Computer Science'] |
2,010.00904 | Autoregressive Entity Retrieval | ['Nicola De Cao', 'Gautier Izacard', 'Sebastian Riedel', 'Fabio Petroni'] | ['cs.CL', 'cs.IR', 'cs.LG', 'stat.ML'] | Entities are at the center of how we represent and aggregate knowledge. For
instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one
per Wikipedia article). The ability to retrieve such entities given a query is
fundamental for knowledge-intensive tasks such as entity linking and
open-domain question answering. Current approaches can be understood as
classifiers among atomic labels, one for each entity. Their weight vectors are
dense entity representations produced by encoding entity meta information such
as their descriptions. This approach has several shortcomings: (i) context and
entity affinity is mainly captured through a vector dot product, potentially
missing fine-grained interactions; (ii) a large memory footprint is needed to
store dense representations when considering large entity sets; (iii) an
appropriately hard set of negative data has to be subsampled at training time.
In this work, we propose GENRE, the first system that retrieves entities by
generating their unique names, left to right, token-by-token in an
autoregressive fashion. This mitigates the aforementioned technical issues
since: (i) the autoregressive formulation directly captures relations between
context and entity name, effectively cross encoding both; (ii) the memory
footprint is greatly reduced because the parameters of our encoder-decoder
architecture scale with vocabulary size, not entity count; (iii) the softmax
loss is computed without subsampling negative data. We experiment with more
than 20 datasets on entity disambiguation, end-to-end entity linking and
document retrieval tasks, achieving new state-of-the-art or very competitive
results while using a tiny fraction of the memory footprint of competing
systems. Finally, we demonstrate that new entities can be added by simply
specifying their names. Code and pre-trained models at
https://github.com/facebookresearch/GENRE. | 2020-10-02T10:13:31Z | Accepted (spotlight) at International Conference on Learning
Representations (ICLR) 2021. Code at
https://github.com/facebookresearch/GENRE. 20 pages, 9 figures, 8 tables | null | null | null | null | null | null | null | null | null |
2,010.0098 | MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on
a Massive Scale | ['Andreas Rücklé', 'Jonas Pfeiffer', 'Iryna Gurevych'] | ['cs.CL', 'cs.IR'] | We study the zero-shot transfer capabilities of text matching models on a
massive scale, by self-supervised training on 140 source domains from community
question answering forums in English. We investigate the model performances on
nine benchmarks of answer selection and question similarity tasks, and show
that all 140 models transfer surprisingly well, where the large majority of
models substantially outperforms common IR baselines. We also demonstrate that
considering a broad selection of source domains is crucial for obtaining the
best zero-shot transfer performances, which contrasts the standard procedure
that merely relies on the largest and most similar domains. In addition, we
extensively study how to best combine multiple source domains. We propose to
incorporate self-supervised with supervised multi-task learning on all
available source domains. Our best zero-shot transfer model considerably
outperforms in-domain BERT and the previous state of the art on six benchmarks.
Fine-tuning of our model with in-domain data results in additional large gains
and achieves the new state of the art on all nine benchmarks. | 2020-10-02T13:22:12Z | EMNLP-2020 | null | null | MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale | ['Andreas Rücklé', 'Jonas Pfeiffer', 'Iryna Gurevych'] | 2,020 | Conference on Empirical Methods in Natural Language Processing | 38 | 46 | ['Computer Science'] |
2,010.01057 | LUKE: Deep Contextualized Entity Representations with Entity-aware
Self-attention | ['Ikuya Yamada', 'Akari Asai', 'Hiroyuki Shindo', 'Hideaki Takeda', 'Yuji Matsumoto'] | ['cs.CL', 'cs.LG'] | Entity representations are useful in natural language tasks involving
entities. In this paper, we propose new pretrained contextualized
representations of words and entities based on the bidirectional transformer.
The proposed model treats words and entities in a given text as independent
tokens, and outputs contextualized representations of them. Our model is
trained using a new pretraining task based on the masked language model of
BERT. The task involves predicting randomly masked words and entities in a
large entity-annotated corpus retrieved from Wikipedia. We also propose an
entity-aware self-attention mechanism that is an extension of the
self-attention mechanism of the transformer, and considers the types of tokens
(words or entities) when computing attention scores. The proposed model
achieves impressive empirical performance on a wide range of entity-related
tasks. In particular, it obtains state-of-the-art results on five well-known
datasets: Open Entity (entity typing), TACRED (relation classification),
CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering),
and SQuAD 1.1 (extractive question answering). Our source code and pretrained
representations are available at https://github.com/studio-ousia/luke. | 2020-10-02T15:38:03Z | EMNLP 2020 | null | null | LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention | ['Ikuya Yamada', 'Akari Asai', 'Hiroyuki Shindo', 'Hideaki Takeda', 'Yuji Matsumoto'] | 2,020 | Conference on Empirical Methods in Natural Language Processing | 676 | 46 | ['Computer Science'] |
2,010.01073 | Efficient Image Super-Resolution Using Pixel Attention | ['Hengyuan Zhao', 'Xiangtao Kong', 'Jingwen He', 'Yu Qiao', 'Chao Dong'] | ['eess.IV', 'cs.CV'] | This work aims at designing a lightweight convolutional neural network for
image super resolution (SR). With simplicity bare in mind, we construct a
pretty concise and effective network with a newly proposed pixel attention
scheme. Pixel attention (PA) is similar as channel attention and spatial
attention in formulation. The difference is that PA produces 3D attention maps
instead of a 1D attention vector or a 2D map. This attention scheme introduces
fewer additional parameters but generates better SR results. On the basis of
PA, we propose two building blocks for the main branch and the reconstruction
branch, respectively. The first one - SC-PA block has the same structure as the
Self-Calibrated convolution but with our PA layer. This block is much more
efficient than conventional residual/dense blocks, for its twobranch
architecture and attention scheme. While the second one - UPA block combines
the nearest-neighbor upsampling, convolution and PA layers. It improves the
final reconstruction quality with little parameter cost. Our final model- PAN
could achieve similar performance as the lightweight networks - SRResNet and
CARN, but with only 272K parameters (17.92% of SRResNet and 17.09% of CARN).
The effectiveness of each proposed component is also validated by ablation
study. The code is available at https://github.com/zhaohengyuan1/PAN. | 2020-10-02T16:04:33Z | 17 pages, 5 figures, conference, accpeted by ECCVW (AIM2020 ESR
Challenge) | null | null | null | null | null | null | null | null | null |
2,010.01815 | High-resolution Piano Transcription with Pedals by Regressing Onset and
Offset Times | ['Qiuqiang Kong', 'Bochen Li', 'Xuchen Song', 'Yuan Wan', 'Yuxuan Wang'] | ['cs.SD', 'eess.AS'] | Automatic music transcription (AMT) is the task of transcribing audio
recordings into symbolic representations. Recently, neural network-based
methods have been applied to AMT, and have achieved state-of-the-art results.
However, many previous systems only detect the onset and offset of notes
frame-wise, so the transcription resolution is limited to the frame hop size.
There is a lack of research on using different strategies to encode onset and
offset targets for training. In addition, previous AMT systems are sensitive to
the misaligned onset and offset labels of audio recordings. Furthermore, there
are limited researches on sustain pedal transcription on large-scale datasets.
In this article, we propose a high-resolution AMT system trained by regressing
precise onset and offset times of piano notes. At inference, we propose an
algorithm to analytically calculate the precise onset and offset times of piano
notes and pedal events. We show that our AMT system is robust to the misaligned
onset and offset labels compared to previous systems. Our proposed system
achieves an onset F1 of 96.72% on the MAESTRO dataset, outperforming previous
onsets and frames system of 94.80%. Our system achieves a pedal onset F1 score
of 91.86\%, which is the first benchmark result on the MAESTRO dataset. We have
released the source code and checkpoints of our work at
https://github.com/bytedance/piano_transcription. | 2020-10-05T06:57:11Z | 12 pages | null | null | null | null | null | null | null | null | null |
2,010.02405 | Simple and Effective Few-Shot Named Entity Recognition with Structured
Nearest Neighbor Learning | ['Yi Yang', 'Arzoo Katiyar'] | ['cs.CL'] | We present a simple few-shot named entity recognition (NER) system based on
nearest neighbor learning and structured inference. Our system uses a
supervised NER model trained on the source domain, as a feature extractor.
Across several test domains, we show that a nearest neighbor classifier in this
feature-space is far more effective than the standard meta-learning approaches.
We further propose a cheap but effective method to capture the label
dependencies between entity tags without expensive CRF training. We show that
our method of combining structured decoding with nearest neighbor learning
achieves state-of-the-art performance on standard few-shot NER evaluation
tasks, improving F1 scores by $6\%$ to $16\%$ absolute points over prior
meta-learning based systems. | 2020-10-06T00:25:50Z | Accepted by EMNLP 2020 | null | null | Frustratingly Simple Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning | ['Yi Yang', 'Arzoo Katiyar'] | 2,020 | Conference on Empirical Methods in Natural Language Processing | 65 | 29 | ['Computer Science'] |
2,010.02502 | Denoising Diffusion Implicit Models | ['Jiaming Song', 'Chenlin Meng', 'Stefano Ermon'] | ['cs.LG', 'cs.CV'] | Denoising diffusion probabilistic models (DDPMs) have achieved high quality
image generation without adversarial training, yet they require simulating a
Markov chain for many steps to produce a sample. To accelerate sampling, we
present denoising diffusion implicit models (DDIMs), a more efficient class of
iterative implicit probabilistic models with the same training procedure as
DDPMs. In DDPMs, the generative process is defined as the reverse of a
Markovian diffusion process. We construct a class of non-Markovian diffusion
processes that lead to the same training objective, but whose reverse process
can be much faster to sample from. We empirically demonstrate that DDIMs can
produce high quality samples $10 \times$ to $50 \times$ faster in terms of
wall-clock time compared to DDPMs, allow us to trade off computation for sample
quality, and can perform semantically meaningful image interpolation directly
in the latent space. | 2020-10-06T06:15:51Z | ICLR 2021; updated connections with ODEs at page 6, fixed some typos
in the proof | null | null | null | null | null | null | null | null | null |
2,010.02559 | LEGAL-BERT: The Muppets straight out of Law School | ['Ilias Chalkidis', 'Manos Fergadiotis', 'Prodromos Malakasiotis', 'Nikolaos Aletras', 'Ion Androutsopoulos'] | ['cs.CL'] | BERT has achieved impressive performance in several NLP tasks. However, there
has been limited investigation on its adaptation guidelines in specialised
domains. Here we focus on the legal domain, where we explore several approaches
for applying BERT models to downstream legal tasks, evaluating on multiple
datasets. Our findings indicate that the previous guidelines for pre-training
and fine-tuning, often blindly followed, do not always generalize well in the
legal domain. Thus we propose a systematic investigation of the available
strategies when applying BERT in specialised domains. These are: (a) use the
original BERT out of the box, (b) adapt BERT by additional pre-training on
domain-specific corpora, and (c) pre-train BERT from scratch on domain-specific
corpora. We also propose a broader hyper-parameter search space when
fine-tuning for downstream tasks and we release LEGAL-BERT, a family of BERT
models intended to assist legal NLP research, computational law, and legal
technology applications. | 2020-10-06T09:06:07Z | 5 pages, short paper in Findings of EMNLP 2020 | null | null | LEGAL-BERT: “Preparing the Muppets for Court’” | ['Ilias Chalkidis', 'Manos Fergadiotis', 'Prodromos Malakasiotis', 'Nikolaos Aletras', 'Ion Androutsopoulos'] | 2,020 | Findings | 265 | 32 | ['Computer Science'] |
2,010.02666 | Improving Efficient Neural Ranking Models with Cross-Architecture
Knowledge Distillation | ['Sebastian Hofstätter', 'Sophia Althammer', 'Michael Schröder', 'Mete Sertkan', 'Allan Hanbury'] | ['cs.IR'] | Retrieval and ranking models are the backbone of many applications such as
web search, open domain QA, or text-based recommender systems. The latency of
neural ranking models at query time is largely dependent on the architecture
and deliberate choices by their designers to trade-off effectiveness for higher
efficiency. This focus on low query latency of a rising number of efficient
ranking architectures make them feasible for production deployment. In machine
learning an increasingly common approach to close the effectiveness gap of more
efficient models is to apply knowledge distillation from a large teacher model
to a smaller student model. We find that different ranking architectures tend
to produce output scores in different magnitudes. Based on this finding, we
propose a cross-architecture training procedure with a margin focused loss
(Margin-MSE), that adapts knowledge distillation to the varying score output
distributions of different BERT and non-BERT passage ranking architectures. We
apply the teachable information as additional fine-grained labels to existing
training triples of the MSMARCO-Passage collection. We evaluate our procedure
of distilling knowledge from state-of-the-art concatenated BERT models to four
different efficient architectures (TK, ColBERT, PreTT, and a BERT CLS dot
product model). We show that across our evaluated architectures our Margin-MSE
knowledge distillation significantly improves re-ranking effectiveness without
compromising their efficiency. Additionally, we show our general distillation
method to improve nearest neighbor based index retrieval with the BERT dot
product model, offering competitive results with specialized and much more
costly training methods. To benefit the community, we publish the teacher-score
training files in a ready-to-use package. | 2020-10-06T12:35:53Z | Updated paper with dense retrieval results and query-level analysis | null | null | null | null | null | null | null | null | null |
2,010.0281 | Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech
to Standard German Text Corpus | ['Michel Plüss', 'Lukas Neukom', 'Christian Scheller', 'Manfred Vogel'] | ['cs.CL', 'cs.LG'] | We present the Swiss Parliaments Corpus (SPC), an automatically aligned Swiss
German speech to Standard German text corpus. This first version of the corpus
is based on publicly available data of the Bernese cantonal parliament and
consists of 293 hours of data. It was created using a novel forced sentence
alignment procedure and an alignment quality estimator, which can be used to
trade off corpus size and quality. We trained Automatic Speech Recognition
(ASR) models as baselines on different subsets of the data and achieved a Word
Error Rate (WER) of 0.278 and a BLEU score of 0.586 on the SPC test set. The
corpus is freely available for download. | 2020-10-06T15:18:21Z | 8 pages, 0 figures | null | null | null | null | null | null | null | null | null |
2,010.03295 | COMETA: A Corpus for Medical Entity Linking in the Social Media | ['Marco Basaldella', 'Fangyu Liu', 'Ehsan Shareghi', 'Nigel Collier'] | ['cs.CL'] | Whilst there has been growing progress in Entity Linking (EL) for general
language, existing datasets fail to address the complex nature of health
terminology in layman's language. Meanwhile, there is a growing need for
applications that can understand the public's voice in the health domain. To
address this we introduce a new corpus called COMETA, consisting of 20k English
biomedical entity mentions from Reddit expert-annotated with links to SNOMED
CT, a widely-used medical knowledge graph. Our corpus satisfies a combination
of desirable properties, from scale and coverage to diversity and quality, that
to the best of our knowledge has not been met by any of the existing resources
in the field. Through benchmark experiments on 20 EL baselines from string- to
neural-based models we shed light on the ability of these systems to perform
complex inference on entities and concepts under 2 challenging evaluation
scenarios. Our experimental results on COMETA illustrate that no golden bullet
exists and even the best mainstream techniques still have a significant
performance gap to fill, while the best solution relies on combining different
views of data. | 2020-10-07T09:16:45Z | Accepted to EMNLP 2020 | null | null | null | null | null | null | null | null | null |
2,010.03636 | MOCHA: A Dataset for Training and Evaluating Generative Reading
Comprehension Metrics | ['Anthony Chen', 'Gabriel Stanovsky', 'Sameer Singh', 'Matt Gardner'] | ['cs.CL', 'cs.LG'] | Posing reading comprehension as a generation problem provides a great deal of
flexibility, allowing for open-ended questions with few restrictions on
possible answers. However, progress is impeded by existing generation metrics,
which rely on token overlap and are agnostic to the nuances of reading
comprehension. To address this, we introduce a benchmark for training and
evaluating generative reading comprehension metrics: MOdeling Correctness with
Human Annotations. MOCHA contains 40K human judgement scores on model outputs
from 6 diverse question answering datasets and an additional set of minimal
pairs for evaluation. Using MOCHA, we train a Learned Evaluation metric for
Reading Comprehension, LERC, to mimic human judgement scores. LERC outperforms
baseline metrics by 10 to 36 absolute Pearson points on held-out annotations.
When we evaluate robustness on minimal pairs, LERC achieves 80% accuracy,
outperforming baselines by 14 to 26 absolute percentage points while leaving
significant room for improvement. MOCHA presents a challenging problem for
developing accurate and robust generative reading comprehension metrics. | 2020-10-07T20:22:54Z | null | Proceedings of the 2020 Conference on Empirical Methods in Natural
Language Processing (EMNLP) | 10.18653/v1/2020.emnlp-main.528 | null | null | null | null | null | null | null |
2,010.04159 | Deformable DETR: Deformable Transformers for End-to-End Object Detection | ['Xizhou Zhu', 'Weijie Su', 'Lewei Lu', 'Bin Li', 'Xiaogang Wang', 'Jifeng Dai'] | ['cs.CV'] | DETR has been recently proposed to eliminate the need for many hand-designed
components in object detection while demonstrating good performance. However,
it suffers from slow convergence and limited feature spatial resolution, due to
the limitation of Transformer attention modules in processing image feature
maps. To mitigate these issues, we proposed Deformable DETR, whose attention
modules only attend to a small set of key sampling points around a reference.
Deformable DETR can achieve better performance than DETR (especially on small
objects) with 10 times less training epochs. Extensive experiments on the COCO
benchmark demonstrate the effectiveness of our approach. Code is released at
https://github.com/fundamentalvision/Deformable-DETR. | 2020-10-08T17:59:21Z | ICLR 2021 Oral | null | null | null | null | null | null | null | null | null |
2,010.04245 | Query-Key Normalization for Transformers | ['Alex Henry', 'Prudhvi Raj Dachapally', 'Shubham Pawar', 'Yuxuan Chen'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Low-resource language translation is a challenging but socially valuable NLP
task. Building on recent work adapting the Transformer's normalization to this
setting, we propose QKNorm, a normalization technique that modifies the
attention mechanism to make the softmax function less prone to arbitrary
saturation without sacrificing expressivity. Specifically, we apply $\ell_2$
normalization along the head dimension of each query and key matrix prior to
multiplying them and then scale up by a learnable parameter instead of dividing
by the square root of the embedding dimension. We show improvements averaging
0.928 BLEU over state-of-the-art bilingual benchmarks for 5 low-resource
translation pairs from the TED Talks corpus and IWSLT'15. | 2020-10-08T20:12:35Z | 8 pages, 2 figures, accepted at Findings of EMNLP 2020 | null | null | Query-Key Normalization for Transformers | ['Alex Henry', 'Prudhvi Raj Dachapally', 'S. Pawar', 'Yuxuan Chen'] | 2,020 | Findings | 91 | 41 | ['Computer Science'] |
2,010.04295 | Widget Captioning: Generating Natural Language Description for Mobile
User Interface Elements | ['Yang Li', 'Gang Li', 'Luheng He', 'Jingjie Zheng', 'Hong Li', 'Zhiwei Guan'] | ['cs.LG', 'cs.AI', 'cs.CL', 'cs.HC'] | Natural language descriptions of user interface (UI) elements such as
alternative text are crucial for accessibility and language-based interaction
in general. Yet, these descriptions are constantly missing in mobile UIs. We
propose widget captioning, a novel task for automatically generating language
descriptions for UI elements from multimodal input including both the image and
the structural representations of user interfaces. We collected a large-scale
dataset for widget captioning with crowdsourcing. Our dataset contains 162,859
language phrases created by human workers for annotating 61,285 UI elements
across 21,750 unique UI screens. We thoroughly analyze the dataset, and train
and evaluate a set of deep model configurations to investigate how each feature
modality as well as the choice of learning strategies impact the quality of
predicted captions. The task formulation and the dataset as well as our
benchmark models contribute a solid basis for this novel multimodal captioning
task that connects language and user interfaces. | 2020-10-08T22:56:03Z | 16 pages, EMNLP 2020 | null | null | Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements | ['Y. Li', 'Gang Li', 'Luheng He', 'Jingjie Zheng', 'Hong Li', 'Zhiwei Guan'] | 2,020 | Conference on Empirical Methods in Natural Language Processing | 110 | 39 | ['Computer Science'] |
2,010.04806 | AutoQA: From Databases To QA Semantic Parsers With Only Synthetic
Training Data | ['Silei Xu', 'Sina J. Semnani', 'Giovanni Campagna', 'Monica S. Lam'] | ['cs.CL'] | We propose AutoQA, a methodology and toolkit to generate semantic parsers
that answer questions on databases, with no manual effort. Given a database
schema and its data, AutoQA automatically generates a large set of high-quality
questions for training that covers different database operations. It uses
automatic paraphrasing combined with template-based parsing to find alternative
expressions of an attribute in different parts of speech. It also uses a novel
filtered auto-paraphraser to generate correct paraphrases of entire sentences.
We apply AutoQA to the Schema2QA dataset and obtain an average logical form
accuracy of 62.9% when tested on natural questions, which is only 6.4% lower
than a model trained with expert natural language annotations and paraphrase
data collected from crowdworkers. To demonstrate the generality of AutoQA, we
also apply it to the Overnight dataset. AutoQA achieves 69.8% answer accuracy,
16.4% higher than the state-of-the-art zero-shot models and only 5.2% lower
than the same model trained with human data. | 2020-10-09T21:06:57Z | To appear in EMNLP 2020 | null | null | null | null | null | null | null | null | null |
2,010.05171 | fairseq S2T: Fast Speech-to-Text Modeling with fairseq | ['Changhan Wang', 'Yun Tang', 'Xutai Ma', 'Anne Wu', 'Sravya Popuri', 'Dmytro Okhonko', 'Juan Pino'] | ['cs.CL', 'eess.AS'] | We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T)
modeling tasks such as end-to-end speech recognition and speech-to-text
translation. It follows fairseq's careful design for scalability and
extensibility. We provide end-to-end workflows from data pre-processing, model
training to offline (online) inference. We implement state-of-the-art
RNN-based, Transformer-based as well as Conformer-based models and open-source
detailed training recipes. Fairseq's machine translation models and language
models can be seamlessly integrated into S2T workflows for multi-task learning
or transfer learning. Fairseq S2T documentation and examples are available at
https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text. | 2020-10-11T05:36:54Z | Post-conference updates (accepted to AACL 2020 Demo) | null | null | null | null | null | null | null | null | null |
2,010.05338 | We Can Detect Your Bias: Predicting the Political Ideology of News
Articles | ['Ramy Baly', 'Giovanni Da San Martino', 'James Glass', 'Preslav Nakov'] | ['cs.CL'] | We explore the task of predicting the leading political ideology or bias of
news articles. First, we collect and release a large dataset of 34,737 articles
that were manually annotated for political ideology -left, center, or right-,
which is well-balanced across both topics and media. We further use a
challenging experimental setup where the test examples come from media that
were not seen during training, which prevents the model from learning to detect
the source of the target news article instead of predicting its political
ideology. From a modeling perspective, we propose an adversarial media
adaptation, as well as a specially adapted triplet loss. We further add
background information about the source, and we show that it is quite helpful
for improving article-level prediction. Our experimental results show very
sizable improvements over using state-of-the-art pre-trained Transformers in
this challenging setup. | 2020-10-11T20:27:55Z | Political bias, bias in news, neural networks bias, adversarial
adaptation, triplet loss, transformers, recurrent neural networks | EMNLP-2020 | null | null | null | null | null | null | null | null |
2,010.05609 | Load What You Need: Smaller Versions of Multilingual BERT | ['Amine Abdaoui', 'Camille Pradel', 'Grégoire Sigel'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Pre-trained Transformer-based models are achieving state-of-the-art results
on a variety of Natural Language Processing data sets. However, the size of
these models is often a drawback for their deployment in real production
applications. In the case of multilingual models, most of the parameters are
located in the embeddings layer. Therefore, reducing the vocabulary size should
have an important impact on the total number of parameters. In this paper, we
propose to generate smaller models that handle fewer number of languages
according to the targeted corpora. We present an evaluation of smaller versions
of multilingual BERT on the XNLI data set, but we believe that this method may
be applied to other multilingual transformers. The obtained results confirm
that we can generate smaller models that keep comparable results, while
reducing up to 45% of the total number of parameters. We compared our models
with DistilmBERT (a distilled version of multilingual BERT) and showed that
unlike language reduction, distillation induced a 1.7% to 6% drop in the
overall accuracy on the XNLI data set. The presented models and code are
publicly available. | 2020-10-12T11:29:06Z | null | SustaiNLP / EMNLP 2020 | null | null | null | null | null | null | null | null |
2,010.05646 | HiFi-GAN: Generative Adversarial Networks for Efficient and High
Fidelity Speech Synthesis | ['Jungil Kong', 'Jaehyeon Kim', 'Jaekyoung Bae'] | ['cs.SD', 'cs.LG', 'eess.AS'] | Several recent work on speech synthesis have employed generative adversarial
networks (GANs) to produce raw waveforms. Although such methods improve the
sampling efficiency and memory usage, their sample quality has not yet reached
that of autoregressive and flow-based generative models. In this work, we
propose HiFi-GAN, which achieves both efficient and high-fidelity speech
synthesis. As speech audio consists of sinusoidal signals with various periods,
we demonstrate that modeling periodic patterns of an audio is crucial for
enhancing sample quality. A subjective human evaluation (mean opinion score,
MOS) of a single speaker dataset indicates that our proposed method
demonstrates similarity to human quality while generating 22.05 kHz
high-fidelity audio 167.9 times faster than real-time on a single V100 GPU. We
further show the generality of HiFi-GAN to the mel-spectrogram inversion of
unseen speakers and end-to-end speech synthesis. Finally, a small footprint
version of HiFi-GAN generates samples 13.4 times faster than real-time on CPU
with comparable quality to an autoregressive counterpart. | 2020-10-12T12:33:43Z | NeurIPS 2020. Code available at https://github.com/jik876/hifi-gan | null | null | null | null | null | null | null | null | null |
2,010.057 | Reformulating Unsupervised Style Transfer as Paraphrase Generation | ['Kalpesh Krishna', 'John Wieting', 'Mohit Iyyer'] | ['cs.CL'] | Modern NLP defines the task of style transfer as modifying the style of a
given sentence without appreciably changing its semantics, which implies that
the outputs of style transfer systems should be paraphrases of their inputs.
However, many existing systems purportedly designed for style transfer
inherently warp the input's meaning through attribute transfer, which changes
semantic properties such as sentiment. In this paper, we reformulate
unsupervised style transfer as a paraphrase generation problem, and present a
simple methodology based on fine-tuning pretrained language models on
automatically generated paraphrase data. Despite its simplicity, our method
significantly outperforms state-of-the-art style transfer systems on both human
and automatic evaluations. We also survey 23 style transfer papers and discover
that existing automatic metrics can be easily gamed and propose fixed variants.
Finally, we pivot to a more real-world style transfer setting by collecting a
large dataset of 15M sentences in 11 diverse styles, which we use for an
in-depth analysis of our system. | 2020-10-12T13:31:01Z | EMNLP 2020 camera-ready (26 pages) | null | null | Reformulating Unsupervised Style Transfer as Paraphrase Generation | ['Kalpesh Krishna', 'J. Wieting', 'Mohit Iyyer'] | 2,020 | Conference on Empirical Methods in Natural Language Processing | 242 | 112 | ['Computer Science'] |
2,010.05987 | SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search | ['Sean MacAvaney', 'Arman Cohan', 'Nazli Goharian'] | ['cs.CL', 'cs.IR'] | With worldwide concerns surrounding the Severe Acute Respiratory Syndrome
Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of scientific
literature on the virus. Clinicians, researchers, and policy-makers need to be
able to search these articles effectively. In this work, we present a zero-shot
ranking algorithm that adapts to COVID-related scientific literature. Our
approach filters training data from another collection down to medical-related
queries, uses a neural re-ranking model pre-trained on scientific text
(SciBERT), and filters the target document collection. This approach ranks top
among zero-shot methods on the TREC COVID Round 1 leaderboard, and exhibits a
P@5 of 0.80 and an nDCG@10 of 0.68 when evaluated on both Round 1 and 2
judgments. Despite not relying on TREC-COVID data, our method outperforms
models that do. As one of the first search methods to thoroughly evaluate
COVID-19 search, we hope that this serves as a strong baseline and helps in the
global crisis. | 2020-10-12T19:28:29Z | EMNLP 2020. This article draws heavily from arXiv:2005.02365 | null | null | null | null | null | null | null | null | null |
2,010.06 | MedICaT: A Dataset of Medical Images, Captions, and Textual References | ['Sanjay Subramanian', 'Lucy Lu Wang', 'Sachin Mehta', 'Ben Bogin', 'Madeleine van Zuylen', 'Sravanthi Parasa', 'Sameer Singh', 'Matt Gardner', 'Hannaneh Hajishirzi'] | ['cs.CV', 'cs.CL'] | Understanding the relationship between figures and text is key to scientific
document understanding. Medical figures in particular are quite complex, often
consisting of several subfigures (75% of figures in our dataset), with detailed
text describing their content. Previous work studying figures in scientific
papers focused on classifying figure content rather than understanding how
images relate to the text. To address challenges in figure retrieval and
figure-to-text alignment, we introduce MedICaT, a dataset of medical images in
context. MedICaT consists of 217K images from 131K open access biomedical
papers, and includes captions, inline references for 74% of figures, and
manually annotated subfigures and subcaptions for a subset of figures. Using
MedICaT, we introduce the task of subfigure to subcaption alignment in compound
figures and demonstrate the utility of inline references in image-text
matching. Our data and code can be accessed at
https://github.com/allenai/medicat. | 2020-10-12T19:56:08Z | EMNLP-Findings 2020 | null | null | MedICaT: A Dataset of Medical Images, Captions, and Textual References | ['Sanjay Subramanian', 'Lucy Lu Wang', 'Sachin Mehta', 'Ben Bogin', 'Madeleine van Zuylen', 'S. Parasa', 'Sameer Singh', 'Matt Gardner', 'Hannaneh Hajishirzi'] | 2,020 | Findings | 74 | 24 | ['Computer Science'] |
2,010.06032 | Measuring and Reducing Gendered Correlations in Pre-trained Models | ['Kellie Webster', 'Xuezhi Wang', 'Ian Tenney', 'Alex Beutel', 'Emily Pitler', 'Ellie Pavlick', 'Jilin Chen', 'Ed Chi', 'Slav Petrov'] | ['cs.CL'] | Pre-trained models have revolutionized natural language understanding.
However, researchers have found they can encode artifacts undesired in many
applications, such as professions correlating with one gender more than
another. We explore such gendered correlations as a case study for how to
address unintended correlations in pre-trained models. We define metrics and
reveal that it is possible for models with similar accuracy to encode
correlations at very different rates. We show how measured correlations can be
reduced with general-purpose techniques, and highlight the trade offs different
strategies have. With these results, we make recommendations for training
robust models: (1) carefully evaluate unintended correlations, (2) be mindful
of seemingly innocuous configuration differences, and (3) focus on general
mitigations. | 2020-10-12T21:15:29Z | null | null | null | null | null | null | null | null | null | null |
2,010.0606 | BioMegatron: Larger Biomedical Domain Language Model | ['Hoo-Chang Shin', 'Yang Zhang', 'Evelina Bakhturina', 'Raul Puri', 'Mostofa Patwary', 'Mohammad Shoeybi', 'Raghav Mani'] | ['cs.CL'] | There has been an influx of biomedical domain-specific language models,
showing language models pre-trained on biomedical text perform better on
biomedical domain benchmarks than those trained on general domain text corpora
such as Wikipedia and Books. Yet, most works do not study the factors affecting
each domain language application deeply. Additionally, the study of model size
on domain-specific models has been mostly missing. We empirically study and
evaluate several factors that can affect performance on domain language
applications, such as the sub-word vocabulary set, model size, pre-training
corpus, and domain transfer. We show consistent improvements on benchmarks with
our larger BioMegatron model trained on a larger domain corpus, contributing to
our understanding of domain language model applications. We demonstrate
noticeable improvements over the previous state-of-the-art (SOTA) on standard
biomedical NLP benchmarks of named entity recognition, relation extraction, and
question answering. Model checkpoints and code are available at
[https://ngc.nvidia.com] and [https://github.com/NVIDIA/NeMo]. | 2020-10-12T22:46:10Z | Accepted for publication at EMNLP 2020 | null | null | null | null | null | null | null | null | null |
2,010.06192 | Revisiting BFloat16 Training | ['Pedram Zamirai', 'Jian Zhang', 'Christopher R. Aberger', 'Christopher De Sa'] | ['cs.LG', 'stat.ML'] | State-of-the-art generic low-precision training algorithms use a mix of
16-bit and 32-bit precision, creating the folklore that 16-bit hardware compute
units alone are not enough to maximize model accuracy. As a result, deep
learning accelerators are forced to support both 16-bit and 32-bit
floating-point units (FPUs), which is more costly than only using 16-bit FPUs
for hardware design. We ask: can we train deep learning models only with 16-bit
floating-point units, while still matching the model accuracy attained by
32-bit training? Towards this end, we study 16-bit-FPU training on the widely
adopted BFloat16 unit. While these units conventionally use nearest rounding to
cast output to 16-bit precision, we show that nearest rounding for model weight
updates often cancels small updates, which degrades the convergence and model
accuracy. Motivated by this, we study two simple techniques well-established in
numerical analysis, stochastic rounding and Kahan summation, to remedy the
model accuracy degradation in 16-bit-FPU training. We demonstrate that these
two techniques can enable up to 7% absolute validation accuracy gain in
16-bit-FPU training. This leads to 0.1% lower to 0.2% higher validation
accuracy compared to 32-bit training across seven deep learning applications. | 2020-10-13T05:38:07Z | null | null | null | null | null | null | null | null | null | null |
2,010.06354 | The Tatoeba Translation Challenge -- Realistic Data Sets for Low
Resource and Multilingual MT | ['Jörg Tiedemann'] | ['cs.CL'] | This paper describes the development of a new benchmark for machine
translation that provides training and test data for thousands of language
pairs covering over 500 languages and tools for creating state-of-the-art
translation models from that collection. The main goal is to trigger the
development of open translation tools and models with a much broader coverage
of the World's languages. Using the package it is possible to work on realistic
low-resource scenarios avoiding artificially reduced setups that are common
when demonstrating zero-shot or few-shot learning. For the first time, this
package provides a comprehensive collection of diverse data sets in hundreds of
languages with systematic language and script annotation and data splits to
extend the narrow coverage of existing benchmarks. Together with the data
release, we also provide a growing number of pre-trained baseline models for
individual language pairs and selected language groups. | 2020-10-13T13:12:21Z | to be appear at the 5th Conference on Machine Translation (WMT20) | null | null | The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT | ['J. Tiedemann'] | 2,020 | Conference on Machine Translation | 171 | 16 | ['Computer Science'] |
2,010.06395 | Aspect-based Document Similarity for Research Papers | ['Malte Ostendorff', 'Terry Ruas', 'Till Blume', 'Bela Gipp', 'Georg Rehm'] | ['cs.CL', 'cs.IR'] | Traditional document similarity measures provide a coarse-grained distinction
between similar and dissimilar documents. Typically, they do not consider in
what aspects two documents are similar. This limits the granularity of
applications like recommender systems that rely on document similarity. In this
paper, we extend similarity with aspect information by performing a pairwise
document classification task. We evaluate our aspect-based document similarity
for research papers. Paper citations indicate the aspect-based similarity,
i.e., the section title in which a citation occurs acts as a label for the pair
of citing and cited paper. We apply a series of Transformer models such as
RoBERTa, ELECTRA, XLNet, and BERT variations and compare them to an LSTM
baseline. We perform our experiments on two newly constructed datasets of
172,073 research paper pairs from the ACL Anthology and CORD-19 corpus. Our
results show SciBERT as the best performing system. A qualitative examination
validates our quantitative results. Our findings motivate future research of
aspect-based document similarity and the development of a recommender system
based on the evaluated techniques. We make our datasets, code, and trained
models publicly available. | 2020-10-13T13:51:21Z | Accepted for publication at COLING 2020 | null | null | Aspect-based Document Similarity for Research Papers | ['Malte Ostendorff', 'Terry Ruas', 'Till Blume', 'Bela Gipp', 'Georg Rehm'] | 2,020 | International Conference on Computational Linguistics | 27 | 56 | ['Computer Science'] |
2,010.0729 | XPDNet for MRI Reconstruction: an application to the 2020 fastMRI
challenge | ['Zaccharie Ramzi', 'Philippe Ciuciu', 'Jean-Luc Starck'] | ['eess.IV', 'cs.CV', 'cs.LG', 'physics.med-ph', 'stat.ML'] | We present a new neural network, the XPDNet, for MRI reconstruction from
periodically under-sampled multi-coil data. We inform the design of this
network by taking best practices from MRI reconstruction and computer vision.
We show that this network can achieve state-of-the-art reconstruction results,
as shown by its ranking of second in the fastMRI 2020 challenge. | 2020-10-15T14:45:00Z | 8 pages, 3 figures, presented as an oral to the 2021 ISMRM conference | null | null | null | null | null | null | null | null | null |
2,010.07611 | Layer-adaptive sparsity for the Magnitude-based Pruning | ['Jaeho Lee', 'Sejun Park', 'Sangwoo Mo', 'Sungsoo Ahn', 'Jinwoo Shin'] | ['cs.LG'] | Recent discoveries on neural network pruning reveal that, with a carefully
chosen layerwise sparsity, a simple magnitude-based pruning achieves
state-of-the-art tradeoff between sparsity and performance. However, without a
clear consensus on "how to choose," the layerwise sparsities are mostly
selected algorithm-by-algorithm, often resorting to handcrafted heuristics or
an extensive hyperparameter search. To fill this gap, we propose a novel
importance score for global pruning, coined layer-adaptive magnitude-based
pruning (LAMP) score; the score is a rescaled version of weight magnitude that
incorporates the model-level $\ell_2$ distortion incurred by pruning, and does
not require any hyperparameter tuning or heavy computation. Under various image
classification setups, LAMP consistently outperforms popular existing schemes
for layerwise sparsity selection. Furthermore, we observe that LAMP continues
to outperform baselines even in weight-rewinding setups, while the
connectivity-oriented layerwise sparsity (the strongest baseline overall)
performs worse than a simple global magnitude-based pruning in this case. Code:
https://github.com/jaeho-lee/layer-adaptive-sparsity | 2020-10-15T09:14:02Z | ICLR 2021. Changed title (previous ver: A deeper look at the
layerwise sparsity of magnitude-based pruning) | null | null | null | null | null | null | null | null | null |
2,010.0824 | Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for
Pairwise Sentence Scoring Tasks | ['Nandan Thakur', 'Nils Reimers', 'Johannes Daxenberger', 'Iryna Gurevych'] | ['cs.CL'] | There are two approaches for pairwise sentence scoring: Cross-encoders, which
perform full-attention over the input pair, and Bi-encoders, which map each
input independently to a dense vector space. While cross-encoders often achieve
higher performance, they are too slow for many practical use cases.
Bi-encoders, on the other hand, require substantial training data and
fine-tuning over the target task to achieve competitive performance. We present
a simple yet efficient data augmentation strategy called Augmented SBERT, where
we use the cross-encoder to label a larger set of input pairs to augment the
training data for the bi-encoder. We show that, in this process, selecting the
sentence pairs is non-trivial and crucial for the success of the method. We
evaluate our approach on multiple tasks (in-domain) as well as on a domain
adaptation task. Augmented SBERT achieves an improvement of up to 6 points for
in-domain and of up to 37 points for domain adaptation tasks compared to the
original bi-encoder performance. | 2020-10-16T08:43:27Z | Accepted at NAACL 2021 | null | null | null | null | null | null | null | null | null |
2,010.08895 | Fourier Neural Operator for Parametric Partial Differential Equations | ['Zongyi Li', 'Nikola Kovachki', 'Kamyar Azizzadenesheli', 'Burigede Liu', 'Kaushik Bhattacharya', 'Andrew Stuart', 'Anima Anandkumar'] | ['cs.LG', 'cs.NA', 'math.NA'] | The classical development of neural networks has primarily focused on
learning mappings between finite-dimensional Euclidean spaces. Recently, this
has been generalized to neural operators that learn mappings between function
spaces. For partial differential equations (PDEs), neural operators directly
learn the mapping from any functional parametric dependence to the solution.
Thus, they learn an entire family of PDEs, in contrast to classical methods
which solve one instance of the equation. In this work, we formulate a new
neural operator by parameterizing the integral kernel directly in Fourier
space, allowing for an expressive and efficient architecture. We perform
experiments on Burgers' equation, Darcy flow, and Navier-Stokes equation. The
Fourier neural operator is the first ML-based method to successfully model
turbulent flows with zero-shot super-resolution. It is up to three orders of
magnitude faster compared to traditional PDE solvers. Additionally, it achieves
superior accuracy compared to previous learning-based solvers under fixed
resolution. | 2020-10-18T00:34:21Z | null | null | null | null | null | null | null | null | null | null |
2,010.09885 | ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular
Property Prediction | ['Seyone Chithrananda', 'Gabriel Grand', 'Bharath Ramsundar'] | ['cs.LG', 'cs.CL', 'physics.chem-ph', 'q-bio.BM', 'I.2.7; I.2.1; J.2; J.3'] | GNNs and chemical fingerprints are the predominant approaches to representing
molecules for property prediction. However, in NLP, transformers have become
the de-facto standard for representation learning thanks to their strong
downstream task transfer. In parallel, the software ecosystem around
transformers is maturing rapidly, with libraries like HuggingFace and BertViz
enabling streamlined training and introspection. In this work, we make one of
the first attempts to systematically evaluate transformers on molecular
property prediction tasks via our ChemBERTa model. ChemBERTa scales well with
pretraining dataset size, offering competitive downstream performance on
MoleculeNet and useful attention-based visualization modalities. Our results
suggest that transformers offer a promising avenue of future work for molecular
representation learning and property prediction. To facilitate these efforts,
we release a curated dataset of 77M SMILES from PubChem suitable for
large-scale self-supervised pretraining. | 2020-10-19T21:41:41Z | Submitted to NeurIPS 2020 ML for Molecules Workshop | null | null | null | null | null | null | null | null | null |
2,010.09931 | Smooth activations and reproducibility in deep networks | ['Gil I. Shamir', 'Dong Lin', 'Lorenzo Coviello'] | ['cs.LG', 'cs.NE', 'stat.ML'] | Deep networks are gradually penetrating almost every domain in our lives due
to their amazing success. However, with substantive performance accuracy
improvements comes the price of \emph{irreproducibility}. Two identical models,
trained on the exact same training dataset may exhibit large differences in
predictions on individual examples even when average accuracy is similar,
especially when trained on highly distributed parallel systems. The popular
Rectified Linear Unit (ReLU) activation has been key to recent success of deep
networks. We demonstrate, however, that ReLU is also a catalyzer to
irreproducibility in deep networks. We show that not only can activations
smoother than ReLU provide better accuracy, but they can also provide better
accuracy-reproducibility tradeoffs. We propose a new family of activations;
Smooth ReLU (\emph{SmeLU}), designed to give such better tradeoffs, while also
keeping the mathematical expression simple, and thus implementation cheap.
SmeLU is monotonic, mimics ReLU, while providing continuous gradients, yielding
better reproducibility. We generalize SmeLU to give even more flexibility and
then demonstrate that SmeLU and its generalized form are special cases of a
more general methodology of REctified Smooth Continuous Unit (RESCU)
activations. Empirical results demonstrate the superior
accuracy-reproducibility tradeoffs with smooth activations, SmeLU in
particular. | 2020-10-20T00:06:47Z | null | null | null | null | null | null | null | null | null | null |
2,010.10137 | PROP: Pre-training with Representative Words Prediction for Ad-hoc
Retrieval | ['Xinyu Ma', 'Jiafeng Guo', 'Ruqing Zhang', 'Yixing Fan', 'Xiang Ji', 'Xueqi Cheng'] | ['cs.IR', 'H.3.3'] | Recently pre-trained language representation models such as BERT have shown
great success when fine-tuned on downstream tasks including information
retrieval (IR). However, pre-training objectives tailored for ad-hoc retrieval
have not been well explored. In this paper, we propose Pre-training with
Representative wOrds Prediction (PROP) for ad-hoc retrieval. PROP is inspired
by the classical statistical language model for IR, specifically the query
likelihood model, which assumes that the query is generated as the piece of
text representative of the "ideal" document. Based on this idea, we construct
the representative words prediction (ROP) task for pre-training. Given an input
document, we sample a pair of word sets according to the document language
model, where the set with higher likelihood is deemed as more representative of
the document. We then pre-train the Transformer model to predict the pairwise
preference between the two word sets, jointly with the Masked Language Model
(MLM) objective. By further fine-tuning on a variety of representative
downstream ad-hoc retrieval tasks, PROP achieves significant improvements over
baselines without pre-training or with other pre-training methods. We also show
that PROP can achieve exciting performance under both the zero- and
low-resource IR settings. The code and pre-trained models are available at
https://github.com/Albert-Ma/PROP. | 2020-10-20T09:04:56Z | Accepted by WSDM2021 | null | 10.1145/3437963.3441777 | PROP: Pre-training with Representative Words Prediction for Ad-hoc Retrieval | ['Xinyu Ma', 'Jiafeng Guo', 'Ruqing Zhang', 'Yixing Fan', 'Xiang Ji', 'Xueqi Cheng'] | 2,020 | Web Search and Data Mining | 98 | 50 | ['Computer Science'] |
2,010.10392 | CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary
Representations From Characters | ['Hicham El Boukkouri', 'Olivier Ferret', 'Thomas Lavergne', 'Hiroshi Noji', 'Pierre Zweigenbaum', 'Junichi Tsujii'] | ['cs.CL'] | Due to the compelling improvements brought by BERT, many recent
representation models adopted the Transformer architecture as their main
building block, consequently inheriting the wordpiece tokenization system
despite it not being intrinsically linked to the notion of Transformers. While
this system is thought to achieve a good balance between the flexibility of
characters and the efficiency of full words, using predefined wordpiece
vocabularies from the general domain is not always suitable, especially when
building models for specialized domains (e.g., the medical domain). Moreover,
adopting a wordpiece tokenization shifts the focus from the word level to the
subword level, making the models conceptually more complex and arguably less
convenient in practice. For these reasons, we propose CharacterBERT, a new
variant of BERT that drops the wordpiece system altogether and uses a
Character-CNN module instead to represent entire words by consulting their
characters. We show that this new model improves the performance of BERT on a
variety of medical domain tasks while at the same time producing robust,
word-level and open-vocabulary representations. | 2020-10-20T15:58:53Z | 13 pages, 8 figures and 3 tables. Accepted at COLING 2020 | null | null | null | null | null | null | null | null | null |
2,010.10499 | Optimal Subarchitecture Extraction For BERT | ['Adrian de Wynter', 'Daniel J. Perry'] | ['cs.CL', 'cs.LG'] | We extract an optimal subset of architectural parameters for the BERT
architecture from Devlin et al. (2018) by applying recent breakthroughs in
algorithms for neural architecture search. This optimal subset, which we refer
to as "Bort", is demonstrably smaller, having an effective (that is, not
counting the embedding layer) size of $5.5\%$ the original BERT-large
architecture, and $16\%$ of the net size. Bort is also able to be pretrained in
$288$ GPU hours, which is $1.2\%$ of the time required to pretrain the
highest-performing BERT parametric architectural variant, RoBERTa-large (Liu et
al., 2019), and about $33\%$ of that of the world-record, in GPU hours,
required to train BERT-large on the same hardware. It is also $7.9$x faster on
a CPU, as well as being better performing than other compressed variants of the
architecture, and some of the non-compressed variants: it obtains performance
improvements of between $0.3\%$ and $31\%$, absolute, with respect to
BERT-large, on multiple public natural language understanding (NLU) benchmarks. | 2020-10-20T17:53:01Z | Preprint. Under review. Corrected typos on v2 | null | null | null | null | null | null | null | null | null |
2,010.10864 | A Short Note on the Kinetics-700-2020 Human Action Dataset | ['Lucas Smaira', 'João Carreira', 'Eric Noland', 'Ellen Clancy', 'Amy Wu', 'Andrew Zisserman'] | ['cs.CV', 'cs.LG'] | We describe the 2020 edition of the DeepMind Kinetics human action dataset,
which replenishes and extends the Kinetics-700 dataset. In this new version,
there are at least 700 video clips from different YouTube videos for each of
the 700 classes. This paper details the changes introduced for this new release
of the dataset and includes a comprehensive set of statistics as well as
baseline results using the I3D network. | 2020-10-21T09:47:09Z | null | null | null | null | null | null | null | null | null | null |
2,010.10906 | German's Next Language Model | ['Branden Chan', 'Stefan Schweter', 'Timo Möller'] | ['cs.CL', 'cs.LG'] | In this work we present the experiments which lead to the creation of our
BERT and ELECTRA based German language models, GBERT and GELECTRA. By varying
the input training data, model size, and the presence of Whole Word Masking
(WWM) we were able to attain SoTA performance across a set of document
classification and named entity recognition (NER) tasks for both models of base
and large size. We adopt an evaluation driven approach in training these models
and our results indicate that both adding more data and utilizing WWM improve
model performance. By benchmarking against existing German models, we show that
these models are the best German models to date. Our trained models will be
made publicly available to the research community. | 2020-10-21T11:28:23Z | Accepted by COLING2020 | null | null | null | null | null | null | null | null | null |
2,010.10999 | Is Retriever Merely an Approximator of Reader? | ['Sohee Yang', 'Minjoon Seo'] | ['cs.CL'] | The state of the art in open-domain question answering (QA) relies on an
efficient retriever that drastically reduces the search space for the expensive
reader. A rather overlooked question in the community is the relationship
between the retriever and the reader, and in particular, if the whole purpose
of the retriever is just a fast approximation for the reader. Our empirical
evidence indicates that the answer is no, and that the reader and the retriever
are complementary to each other even in terms of accuracy only. We make a
careful conjecture that the architectural constraint of the retriever, which
has been originally intended for enabling approximate search, seems to also
make the model more robust in large-scale search. We then propose to distill
the reader into the retriever so that the retriever absorbs the strength of the
reader while keeping its own benefit. Experimental results show that our method
can enhance the document recall rate as well as the end-to-end QA accuracy of
off-the-shelf retrievers in open-domain QA tasks. | 2020-10-21T13:40:15Z | null | null | null | null | null | null | null | null | null | null |
2,010.11125 | Beyond English-Centric Multilingual Machine Translation | ['Angela Fan', 'Shruti Bhosale', 'Holger Schwenk', 'Zhiyi Ma', 'Ahmed El-Kishky', 'Siddharth Goyal', 'Mandeep Baines', 'Onur Celebi', 'Guillaume Wenzek', 'Vishrav Chaudhary', 'Naman Goyal', 'Tom Birch', 'Vitaliy Liptchinsky', 'Sergey Edunov', 'Edouard Grave', 'Michael Auli', 'Armand Joulin'] | ['cs.CL', 'cs.LG'] | Existing work in translation demonstrated the potential of massively
multilingual machine translation by training a single model able to translate
between any pair of languages. However, much of this work is English-Centric by
training only on data which was translated from or to English. While this is
supported by large sources of training data, it does not reflect translation
needs worldwide. In this work, we create a true Many-to-Many multilingual
translation model that can translate directly between any pair of 100
languages. We build and open source a training dataset that covers thousands of
language directions with supervised data, created through large-scale mining.
Then, we explore how to effectively increase model capacity through a
combination of dense scaling and language-specific sparse parameters to create
high quality models. Our focus on non-English-Centric models brings gains of
more than 10 BLEU when directly translating between non-English directions
while performing competitively to the best single systems of WMT. We
open-source our scripts so that others may reproduce the data, evaluation, and
final M2M-100 model. | 2020-10-21T17:01:23Z | null | null | null | null | null | null | null | null | null | null |
2,010.11386 | Distilling Dense Representations for Ranking using Tightly-Coupled
Teachers | ['Sheng-Chieh Lin', 'Jheng-Hong Yang', 'Jimmy Lin'] | ['cs.IR', 'cs.CL'] | We present an approach to ranking with dense representations that applies
knowledge distillation to improve the recently proposed late-interaction
ColBERT model. Specifically, we distill the knowledge from ColBERT's expressive
MaxSim operator for computing relevance scores into a simple dot product, thus
enabling single-step ANN search. Our key insight is that during distillation,
tight coupling between the teacher model and the student model enables more
flexible distillation strategies and yields better learned representations. We
empirically show that our approach improves query latency and greatly reduces
the onerous storage requirements of ColBERT, while only making modest
sacrifices in terms of effectiveness. By combining our dense representations
with sparse representations derived from document expansion, we are able to
approach the effectiveness of a standard cross-encoder reranker using BERT that
is orders of magnitude slower. | 2020-10-22T02:26:01Z | null | null | null | Distilling Dense Representations for Ranking using Tightly-Coupled Teachers | ['Sheng-Chieh Lin', 'Jheng-Hong Yang', 'Jimmy J. Lin'] | 2,020 | arXiv.org | 122 | 28 | ['Computer Science'] |
2,010.1143 | Self-training and Pre-training are Complementary for Speech Recognition | ['Qiantong Xu', 'Alexei Baevski', 'Tatiana Likhomanenko', 'Paden Tomasello', 'Alexis Conneau', 'Ronan Collobert', 'Gabriel Synnaeve', 'Michael Auli'] | ['cs.LG', 'cs.SD', 'eess.AS'] | Self-training and unsupervised pre-training have emerged as effective
approaches to improve speech recognition systems using unlabeled data. However,
it is not clear whether they learn similar patterns or if they can be
effectively combined. In this paper, we show that pseudo-labeling and
pre-training with wav2vec 2.0 are complementary in a variety of labeled data
setups. Using just 10 minutes of labeled data from Libri-light as well as 53k
hours of unlabeled data from LibriVox achieves WERs of 3.0%/5.2% on the clean
and other test sets of Librispeech - rivaling the best published systems
trained on 960 hours of labeled data only a year ago. Training on all labeled
data of Librispeech achieves WERs of 1.5%/3.1%. | 2020-10-22T04:15:37Z | null | null | null | Self-Training and Pre-Training are Complementary for Speech Recognition | ['Qiantong Xu', 'Alexei Baevski', 'Tatiana Likhomanenko', 'Paden Tomasello', 'Alexis Conneau', 'R. Collobert', 'Gabriel Synnaeve', 'Michael Auli'] | 2,020 | IEEE International Conference on Acoustics, Speech, and Signal Processing | 173 | 38 | ['Computer Science', 'Engineering'] |
2,010.11784 | Self-Alignment Pretraining for Biomedical Entity Representations | ['Fangyu Liu', 'Ehsan Shareghi', 'Zaiqiao Meng', 'Marco Basaldella', 'Nigel Collier'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Despite the widespread success of self-supervised learning via masked
language models (MLM), accurately capturing fine-grained semantic relationships
in the biomedical domain remains a challenge. This is of paramount importance
for entity-level tasks such as entity linking where the ability to model entity
relations (especially synonymy) is pivotal. To address this challenge, we
propose SapBERT, a pretraining scheme that self-aligns the representation space
of biomedical entities. We design a scalable metric learning framework that can
leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts.
In contrast with previous pipeline-based hybrid systems, SapBERT offers an
elegant one-model-for-all solution to the problem of medical entity linking
(MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking
datasets. In the scientific domain, we achieve SOTA even without task-specific
supervision. With substantial improvement over various domain-specific
pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining
scheme proves to be both effective and robust. | 2020-10-22T14:59:57Z | NAACL 2021 camera-ready version | null | null | null | null | null | null | null | null | null |
2,010.11856 | XOR QA: Cross-lingual Open-Retrieval Question Answering | ['Akari Asai', 'Jungo Kasai', 'Jonathan H. Clark', 'Kenton Lee', 'Eunsol Choi', 'Hannaneh Hajishirzi'] | ['cs.CL'] | Multilingual question answering tasks typically assume answers exist in the
same language as the question. Yet in practice, many languages face both
information scarcity -- where languages have few reference articles -- and
information asymmetry -- where questions reference concepts from other
cultures. This work extends open-retrieval question answering to a
cross-lingual setting enabling questions from one language to be answered via
answer content from another language. We construct a large-scale dataset built
on questions from TyDi QA lacking same-language answers. Our task formulation,
called Cross-lingual Open Retrieval Question Answering (XOR QA), includes 40k
information-seeking questions from across 7 diverse non-English languages.
Based on this dataset, we introduce three new tasks that involve cross-lingual
document retrieval using multi-lingual and English resources. We establish
baselines with state-of-the-art machine translation systems and cross-lingual
pretrained models. Experimental results suggest that XOR QA is a challenging
task that will facilitate the development of novel techniques for multilingual
question answering. Our data and code are available at
https://nlp.cs.washington.edu/xorqa. | 2020-10-22T16:47:17Z | Published as a conference paper at NAACL-HLT 2021 (long) | null | null | null | null | null | null | null | null | null |
2,010.11929 | An Image is Worth 16x16 Words: Transformers for Image Recognition at
Scale | ['Alexey Dosovitskiy', 'Lucas Beyer', 'Alexander Kolesnikov', 'Dirk Weissenborn', 'Xiaohua Zhai', 'Thomas Unterthiner', 'Mostafa Dehghani', 'Matthias Minderer', 'Georg Heigold', 'Sylvain Gelly', 'Jakob Uszkoreit', 'Neil Houlsby'] | ['cs.CV', 'cs.AI', 'cs.LG'] | While the Transformer architecture has become the de-facto standard for
natural language processing tasks, its applications to computer vision remain
limited. In vision, attention is either applied in conjunction with
convolutional networks, or used to replace certain components of convolutional
networks while keeping their overall structure in place. We show that this
reliance on CNNs is not necessary and a pure transformer applied directly to
sequences of image patches can perform very well on image classification tasks.
When pre-trained on large amounts of data and transferred to multiple mid-sized
or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision
Transformer (ViT) attains excellent results compared to state-of-the-art
convolutional networks while requiring substantially fewer computational
resources to train. | 2020-10-22T17:55:59Z | Fine-tuning code and pre-trained models are available at
https://github.com/google-research/vision_transformer. ICLR camera-ready
version with 2 small modifications: 1) Added a discussion of CLS vs GAP
classifier in the appendix, 2) Fixed an error in exaFLOPs computation in
Figure 5 and Table 6 (relative performance of models is basically not
affected) | null | null | null | null | null | null | null | null | null |
2,010.11934 | mT5: A massively multilingual pre-trained text-to-text transformer | ['Linting Xue', 'Noah Constant', 'Adam Roberts', 'Mihir Kale', 'Rami Al-Rfou', 'Aditya Siddhant', 'Aditya Barua', 'Colin Raffel'] | ['cs.CL'] | The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified
text-to-text format and scale to attain state-of-the-art results on a wide
variety of English-language NLP tasks. In this paper, we introduce mT5, a
multilingual variant of T5 that was pre-trained on a new Common Crawl-based
dataset covering 101 languages. We detail the design and modified training of
mT5 and demonstrate its state-of-the-art performance on many multilingual
benchmarks. We also describe a simple technique to prevent "accidental
translation" in the zero-shot setting, where a generative model chooses to
(partially) translate its prediction into the wrong language. All of the code
and model checkpoints used in this work are publicly available. | 2020-10-22T17:58:14Z | null | null | null | mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer | ['Linting Xue', 'Noah Constant', 'Adam Roberts', 'Mihir Kale', 'Rami Al-Rfou', 'Aditya Siddhant', 'Aditya Barua', 'Colin Raffel'] | 2,020 | North American Chapter of the Association for Computational Linguistics | 2,570 | 55 | ['Computer Science'] |
2,010.12148 | ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling
for Natural Language Understanding | ['Dongling Xiao', 'Yu-Kun Li', 'Han Zhang', 'Yu Sun', 'Hao Tian', 'Hua Wu', 'Haifeng Wang'] | ['cs.CL', 'cs.LG'] | Coarse-grained linguistic information, such as named entities or phrases,
facilitates adequately representation learning in pre-training. Previous works
mainly focus on extending the objective of BERT's Masked Language Modeling
(MLM) from masking individual tokens to contiguous sequences of n tokens. We
argue that such contiguously masking method neglects to model the
intra-dependencies and inter-relation of coarse-grained linguistic information.
As an alternative, we propose ERNIE-Gram, an explicitly n-gram masking method
to enhance the integration of coarse-grained information into pre-training. In
ERNIE-Gram, n-grams are masked and predicted directly using explicit n-gram
identities rather than contiguous sequences of n tokens. Furthermore,
ERNIE-Gram employs a generator model to sample plausible n-gram identities as
optional n-gram masks and predict them in both coarse-grained and fine-grained
manners to enable comprehensive n-gram prediction and relation modeling. We
pre-train ERNIE-Gram on English and Chinese text corpora and fine-tune on 19
downstream tasks. Experimental results show that ERNIE-Gram outperforms
previous pre-training models like XLNet and RoBERTa by a large margin, and
achieves comparable results with state-of-the-art methods. The source codes and
pre-trained models have been released at https://github.com/PaddlePaddle/ERNIE. | 2020-10-23T03:42:20Z | Accepted by NAACL-HLT 2021. Codes will be released at
https://github.com/PaddlePaddle/ERNIE | null | null | null | null | null | null | null | null | null |
2,010.12321 | BARThez: a Skilled Pretrained French Sequence-to-Sequence Model | ['Moussa Kamal Eddine', 'Antoine J. -P. Tixier', 'Michalis Vazirgiannis'] | ['cs.CL'] | Inductive transfer learning has taken the entire NLP field by storm, with
models such as BERT and BART setting new state of the art on countless NLU
tasks. However, most of the available models and research have been conducted
for English. In this work, we introduce BARThez, the first large-scale
pretrained seq2seq model for French. Being based on BART, BARThez is
particularly well-suited for generative tasks. We evaluate BARThez on five
discriminative tasks from the FLUE benchmark and two generative tasks from a
novel summarization dataset, OrangeSum, that we created for this research. We
show BARThez to be very competitive with state-of-the-art BERT-based French
language models such as CamemBERT and FlauBERT. We also continue the
pretraining of a multilingual BART on BARThez' corpus, and show our resulting
model, mBARThez, to significantly boost BARThez' generative performance. Code,
data and models are publicly available. | 2020-10-23T11:57:33Z | More experiments and results, human evaluation, reorganization of
paper | null | null | null | null | null | null | null | null | null |
2,010.12421 | TweetEval: Unified Benchmark and Comparative Evaluation for Tweet
Classification | ['Francesco Barbieri', 'Jose Camacho-Collados', 'Leonardo Neves', 'Luis Espinosa-Anke'] | ['cs.CL', 'cs.SI'] | The experimental landscape in natural language processing for social media is
too fragmented. Each year, new shared tasks and datasets are proposed, ranging
from classics like sentiment analysis to irony detection or emoji prediction.
Therefore, it is unclear what the current state of the art is, as there is no
standardized evaluation protocol, neither a strong set of baselines trained on
such domain-specific data. In this paper, we propose a new evaluation framework
(TweetEval) consisting of seven heterogeneous Twitter-specific classification
tasks. We also provide a strong set of baselines as starting point, and compare
different language modeling pre-training strategies. Our initial experiments
show the effectiveness of starting off with existing pre-trained generic
language models, and continue training them on Twitter corpora. | 2020-10-23T14:11:04Z | Findings of EMNLP 2020. TweetEval benchmark available at
https://github.com/cardiffnlp/tweeteval | null | null | null | null | null | null | null | null | null |
2,010.12725 | Compositional Generalization and Natural Language Variation: Can a
Semantic Parsing Approach Handle Both? | ['Peter Shaw', 'Ming-Wei Chang', 'Panupong Pasupat', 'Kristina Toutanova'] | ['cs.CL'] | Sequence-to-sequence models excel at handling natural language variation, but
have been shown to struggle with out-of-distribution compositional
generalization. This has motivated new specialized architectures with stronger
compositional biases, but most of these approaches have only been evaluated on
synthetically-generated datasets, which are not representative of natural
language variation. In this work we ask: can we develop a semantic parsing
approach that handles both natural language variation and compositional
generalization? To better assess this capability, we propose new train and test
splits of non-synthetic datasets. We demonstrate that strong existing
approaches do not perform well across a broad set of evaluations. We also
propose NQG-T5, a hybrid model that combines a high-precision grammar-based
approach with a pre-trained sequence-to-sequence model. It outperforms existing
approaches across several compositional generalization challenges on
non-synthetic data, while also being competitive with the state-of-the-art on
standard evaluations. While still far from solving this problem, our study
highlights the importance of diverse evaluations and the open challenge of
handling both compositional generalization and natural language variation in
semantic parsing. | 2020-10-24T00:38:27Z | ACL 2021 | null | null | null | null | null | null | null | null | null |
2,010.12821 | Rethinking embedding coupling in pre-trained language models | ['Hyung Won Chung', 'Thibault Févry', 'Henry Tsai', 'Melvin Johnson', 'Sebastian Ruder'] | ['cs.CL', 'cs.LG'] | We re-evaluate the standard practice of sharing weights between input and
output embeddings in state-of-the-art pre-trained language models. We show that
decoupled embeddings provide increased modeling flexibility, allowing us to
significantly improve the efficiency of parameter allocation in the input
embedding of multilingual models. By reallocating the input embedding
parameters in the Transformer layers, we achieve dramatically better
performance on standard natural language understanding tasks with the same
number of parameters during fine-tuning. We also show that allocating
additional capacity to the output embedding provides benefits to the model that
persist through the fine-tuning stage even though the output embedding is
discarded after pre-training. Our analysis shows that larger output embeddings
prevent the model's last layers from overspecializing to the pre-training task
and encourage Transformer representations to be more general and more
transferable to other tasks and languages. Harnessing these findings, we are
able to train models that achieve strong performance on the XTREME benchmark
without increasing the number of parameters at the fine-tuning stage. | 2020-10-24T07:43:00Z | null | null | null | Rethinking embedding coupling in pre-trained language models | ['Hyung Won Chung', 'Thibault Févry', 'Henry Tsai', 'Melvin Johnson', 'Sebastian Ruder'] | 2,020 | International Conference on Learning Representations | 143 | 73 | ['Computer Science'] |
2,010.12871 | Large Scale Legal Text Classification Using Transformer Models | ['Zein Shaheen', 'Gerhard Wohlgenannt', 'Erwin Filtz'] | ['cs.CL', 'cs.AI'] | Large multi-label text classification is a challenging Natural Language
Processing (NLP) problem that is concerned with text classification for
datasets with thousands of labels. We tackle this problem in the legal domain,
where datasets, such as JRC-Acquis and EURLEX57K labeled with the EuroVoc
vocabulary were created within the legal information systems of the European
Union. The EuroVoc taxonomy includes around 7000 concepts. In this work, we
study the performance of various recent transformer-based models in combination
with strategies such as generative pretraining, gradual unfreezing and
discriminative learning rates in order to reach competitive classification
performance, and present new state-of-the-art results of 0.661 (F1) for
JRC-Acquis and 0.754 for EURLEX57K. Furthermore, we quantify the impact of
individual steps, such as language model fine-tuning or gradual unfreezing in
an ablation study, and provide reference dataset splits created with an
iterative stratification algorithm. | 2020-10-24T11:03:01Z | null | null | null | null | null | null | null | null | null | null |
2,010.13002 | Pre-trained Summarization Distillation | ['Sam Shleifer', 'Alexander M. Rush'] | ['cs.CL', 'cs.AI'] | Recent state-of-the-art approaches to summarization utilize large pre-trained
Transformer models. Distilling these models to smaller student models has
become critically important for practical use; however there are many different
distillation methods proposed by the NLP literature. Recent work on distilling
BERT for classification and regression tasks shows strong performance using
direct knowledge distillation. Alternatively, machine translation practitioners
distill using pseudo-labeling, where a small model is trained on the
translations of a larger model. A third, simpler approach is to 'shrink and
fine-tune' (SFT), which avoids any explicit distillation by copying parameters
to a smaller student model and then fine-tuning. We compare these three
approaches for distillation of Pegasus and BART, the current and former state
of the art, pre-trained summarization models, and find that SFT outperforms
knowledge distillation and pseudo-labeling on the CNN/DailyMail dataset, but
under-performs pseudo-labeling on the more abstractive XSUM dataset. PyTorch
Code and checkpoints of different sizes are available through Hugging Face
transformers here http://tiny.cc/4iy0tz. | 2020-10-24T23:15:43Z | null | null | null | Pre-trained Summarization Distillation | ['Sam Shleifer', 'Alexander M. Rush'] | 2,020 | arXiv.org | 103 | 32 | ['Computer Science'] |
2,010.13154 | Attention is All You Need in Speech Separation | ['Cem Subakan', 'Mirco Ravanelli', 'Samuele Cornell', 'Mirko Bronzi', 'Jianyuan Zhong'] | ['eess.AS', 'cs.LG', 'cs.SD', 'eess.SP'] | Recurrent Neural Networks (RNNs) have long been the dominant architecture in
sequence-to-sequence learning. RNNs, however, are inherently sequential models
that do not allow parallelization of their computations. Transformers are
emerging as a natural alternative to standard RNNs, replacing recurrent
computations with a multi-head attention mechanism. In this paper, we propose
the SepFormer, a novel RNN-free Transformer-based neural network for speech
separation. The SepFormer learns short and long-term dependencies with a
multi-scale approach that employs transformers. The proposed model achieves
state-of-the-art (SOTA) performance on the standard WSJ0-2/3mix datasets. It
reaches an SI-SNRi of 22.3 dB on WSJ0-2mix and an SI-SNRi of 19.5 dB on
WSJ0-3mix. The SepFormer inherits the parallelization advantages of
Transformers and achieves a competitive performance even when downsampling the
encoded representation by a factor of 8. It is thus significantly faster and it
is less memory-demanding than the latest speech separation systems with
comparable performance. | 2020-10-25T16:28:54Z | Accepted to ICASSP 2021 | null | null | null | null | null | null | null | null | null |
2,010.13652 | Dutch Humor Detection by Generating Negative Examples | ['Thomas Winters', 'Pieter Delobelle'] | ['cs.CL', 'cs.AI', '68T50', 'I.2.7; I.2.6'] | Detecting if a text is humorous is a hard task to do computationally, as it
usually requires linguistic and common sense insights. In machine learning,
humor detection is usually modeled as a binary classification task, trained to
predict if the given text is a joke or another type of text. Rather than using
completely different non-humorous texts, we propose using text generation
algorithms for imitating the original joke dataset to increase the difficulty
for the learning algorithm. We constructed several different joke and non-joke
datasets to test the humor detection abilities of different language
technologies. In particular, we compare the humor detection capabilities of
classic neural network approaches with the state-of-the-art Dutch language
model RobBERT. In doing so, we create and compare the first Dutch humor
detection systems. We found that while other language models perform well when
the non-jokes came from completely different domains, RobBERT was the only one
that was able to distinguish jokes from generated negative examples. This
performance illustrates the usefulness of using text generation to create
negative datasets for humor recognition, and also shows that transformer models
are a large step forward in humor detection. | 2020-10-26T15:15:10Z | Accepted at the Proceedings of the 32st Benelux Conference on
Artificial Intelligence (BNAIC 2020) and the 29th Belgian Dutch Conference on
Machine Learning (Benelearn 2020) | null | null | Dutch Humor Detection by Generating Negative Examples | ['Thomas Winters', 'Pieter Delobelle'] | 2,020 | arXiv.org | 11 | 39 | ['Computer Science'] |
2,010.13886 | MarbleNet: Deep 1D Time-Channel Separable Convolutional Neural Network
for Voice Activity Detection | ['Fei Jia', 'Somshubra Majumdar', 'Boris Ginsburg'] | ['eess.AS', 'cs.SD'] | We present MarbleNet, an end-to-end neural network for Voice Activity
Detection (VAD). MarbleNet is a deep residual network composed from blocks of
1D time-channel separable convolution, batch-normalization, ReLU and dropout
layers. When compared to a state-of-the-art VAD model, MarbleNet is able to
achieve similar performance with roughly 1/10-th the parameter cost. We further
conduct extensive ablation studies on different training methods and choices of
parameters in order to study the robustness of MarbleNet in real-world VAD
tasks. | 2020-10-26T20:26:05Z | Accepted to ICASSP 2021 | null | null | null | null | null | null | null | null | null |
2,010.13956 | Recent Developments on ESPnet Toolkit Boosted by Conformer | ['Pengcheng Guo', 'Florian Boyer', 'Xuankai Chang', 'Tomoki Hayashi', 'Yosuke Higuchi', 'Hirofumi Inaguma', 'Naoyuki Kamo', 'Chenda Li', 'Daniel Garcia-Romero', 'Jiatong Shi', 'Jing Shi', 'Shinji Watanabe', 'Kun Wei', 'Wangyou Zhang', 'Yuekai Zhang'] | ['eess.AS', 'cs.SD'] | In this study, we present recent developments on ESPnet: End-to-End Speech
Processing toolkit, which mainly involves a recently proposed architecture
called Conformer, Convolution-augmented Transformer. This paper shows the
results for a wide range of end-to-end speech processing applications, such as
automatic speech recognition (ASR), speech translations (ST), speech separation
(SS) and text-to-speech (TTS). Our experiments reveal various training tips and
significant performance benefits obtained with the Conformer on different
tasks. These results are competitive or even outperform the current
state-of-art Transformer models. We are preparing to release all-in-one recipes
using open source and publicly available corpora for all the above tasks with
pre-trained models. Our aim for this work is to contribute to our research
community by reducing the burden of preparing state-of-the-art research
environments usually requiring high resources. | 2020-10-26T23:49:23Z | null | null | null | null | null | null | null | null | null | null |
2,010.14235 | Multi-XScience: A Large-scale Dataset for Extreme Multi-document
Summarization of Scientific Articles | ['Yao Lu', 'Yue Dong', 'Laurent Charlin'] | ['cs.CL', 'cs.AI'] | Multi-document summarization is a challenging task for which there exists
little large-scale datasets. We propose Multi-XScience, a large-scale
multi-document summarization dataset created from scientific articles.
Multi-XScience introduces a challenging multi-document summarization task:
writing the related-work section of a paper based on its abstract and the
articles it references. Our work is inspired by extreme summarization, a
dataset construction protocol that favours abstractive modeling approaches.
Descriptive statistics and empirical results---using several state-of-the-art
models trained on the Multi-XScience dataset---reveal that Multi-XScience is
well suited for abstractive models. | 2020-10-27T12:10:19Z | EMNLP 2020 | null | null | Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles | ['Yao Lu', 'Yue Dong', 'Laurent Charlin'] | 2,020 | Conference on Empirical Methods in Natural Language Processing | 120 | 31 | ['Computer Science'] |
2,010.14568 | Strongly Incremental Constituency Parsing with Graph Neural Networks | ['Kaiyu Yang', 'Jia Deng'] | ['cs.CL'] | Parsing sentences into syntax trees can benefit downstream applications in
NLP. Transition-based parsers build trees by executing actions in a state
transition system. They are computationally efficient, and can leverage machine
learning to predict actions based on partial trees. However, existing
transition-based parsers are predominantly based on the shift-reduce transition
system, which does not align with how humans are known to parse sentences.
Psycholinguistic research suggests that human parsing is strongly incremental:
humans grow a single parse tree by adding exactly one token at each step. In
this paper, we propose a novel transition system called attach-juxtapose. It is
strongly incremental; it represents a partial sentence using a single tree;
each action adds exactly one token into the partial tree. Based on our
transition system, we develop a strongly incremental parser. At each step, it
encodes the partial tree using a graph neural network and predicts an action.
We evaluate our parser on Penn Treebank (PTB) and Chinese Treebank (CTB). On
PTB, it outperforms existing parsers trained with only constituency trees; and
it performs on par with state-of-the-art parsers that use dependency trees as
additional training data. On CTB, our parser establishes a new state of the
art. Code is available at
https://github.com/princeton-vl/attach-juxtapose-parser. | 2020-10-27T19:19:38Z | Accepted to NeurIPS 2020 | null | null | null | null | null | null | null | null | null |
2,010.14819 | Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets | ['Kai Han', 'Yunhe Wang', 'Qiulin Zhang', 'Wei Zhang', 'Chunjing Xu', 'Tong Zhang'] | ['cs.CV'] | To obtain excellent deep neural architectures, a series of techniques are
carefully designed in EfficientNets. The giant formula for simultaneously
enlarging the resolution, depth and width provides us a Rubik's cube for neural
networks. So that we can find networks with high efficiency and excellent
performance by twisting the three dimensions. This paper aims to explore the
twisting rules for obtaining deep neural networks with minimum model sizes and
computational costs. Different from the network enlarging, we observe that
resolution and depth are more important than width for tiny networks.
Therefore, the original method, i.e., the compound scaling in EfficientNet is
no longer suitable. To this end, we summarize a tiny formula for downsizing
neural architectures through a series of smaller models derived from the
EfficientNet-B0 with the FLOPs constraint. Experimental results on the ImageNet
benchmark illustrate that our TinyNet performs much better than the smaller
version of EfficientNets using the inversed giant formula. For instance, our
TinyNet-E achieves a 59.9% Top-1 accuracy with only 24M FLOPs, which is about
1.9% higher than that of the previous best MobileNetV3 with similar
computational cost. Code will be available at
https://github.com/huawei-noah/ghostnet/tree/master/tinynet_pytorch, and
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/tinynet. | 2020-10-28T08:49:45Z | NeurIPS 2020 | null | null | null | null | null | null | null | null | null |
2,010.15052 | Image Representations Learned With Unsupervised Pre-Training Contain
Human-like Biases | ['Ryan Steed', 'Aylin Caliskan'] | ['cs.CY', 'cs.CV'] | Recent advances in machine learning leverage massive datasets of unlabeled
images from the web to learn general-purpose image representations for tasks
from image classification to face recognition. But do unsupervised computer
vision models automatically learn implicit patterns and embed social biases
that could have harmful downstream effects? We develop a novel method for
quantifying biased associations between representations of social concepts and
attributes in images. We find that state-of-the-art unsupervised models trained
on ImageNet, a popular benchmark image dataset curated from internet images,
automatically learn racial, gender, and intersectional biases. We replicate 8
previously documented human biases from social psychology, from the innocuous,
as with insects and flowers, to the potentially harmful, as with race and
gender. Our results closely match three hypotheses about intersectional bias
from social psychology. For the first time in unsupervised computer vision, we
also quantify implicit human biases about weight, disabilities, and several
ethnicities. When compared with statistical patterns in online image datasets,
our findings suggest that machine learning models can automatically learn bias
from the way people are stereotypically portrayed on the web. | 2020-10-28T15:55:49Z | 10 pages, 3 figures. Replaced example image completions of real
people with completions of artificial people | null | 10.1145/3442188.3445932 | null | null | null | null | null | null | null |
2,011.00677 | IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model
for Indonesian NLP | ['Fajri Koto', 'Afshin Rahimi', 'Jey Han Lau', 'Timothy Baldwin'] | ['cs.CL'] | Although the Indonesian language is spoken by almost 200 million people and
the 10th most spoken language in the world, it is under-represented in NLP
research. Previous work on Indonesian has been hampered by a lack of annotated
datasets, a sparsity of language resources, and a lack of resource
standardization. In this work, we release the IndoLEM dataset comprising seven
tasks for the Indonesian language, spanning morpho-syntax, semantics, and
discourse. We additionally release IndoBERT, a new pre-trained language model
for Indonesian, and evaluate it over IndoLEM, in addition to benchmarking it
against existing resources. Our experiments show that IndoBERT achieves
state-of-the-art performance over most of the tasks in IndoLEM. | 2020-11-02T01:54:56Z | Accepted at COLING 2020 - The 28th International Conference on
Computational Linguistics | null | null | IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP | ['Fajri Koto', 'Afshin Rahimi', 'Jey Han Lau', 'Timothy Baldwin'] | 2,020 | International Conference on Computational Linguistics | 263 | 66 | ['Computer Science'] |
2,011.01513 | CharBERT: Character-aware Pre-trained Language Model | ['Wentao Ma', 'Yiming Cui', 'Chenglei Si', 'Ting Liu', 'Shijin Wang', 'Guoping Hu'] | ['cs.CL'] | Most pre-trained language models (PLMs) construct word representations at
subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV
(out-of-vocab) words are almost avoidable. However, those methods split a word
into subword units and make the representation incomplete and fragile. In this
paper, we propose a character-aware pre-trained language model named CharBERT
improving on the previous methods (such as BERT, RoBERTa) to tackle these
problems. We first construct the contextual word embedding for each token from
the sequential character representations, then fuse the representations of
characters and the subword representations by a novel heterogeneous interaction
module. We also propose a new pre-training task named NLM (Noisy LM) for
unsupervised character representation learning. We evaluate our method on
question answering, sequence labeling, and text classification tasks, both on
the original datasets and adversarial misspelling test sets. The experimental
results show that our method can significantly improve the performance and
robustness of PLMs simultaneously. Pretrained models, evaluation sets, and code
are available at https://github.com/wtma/CharBERT | 2020-11-03T07:13:06Z | 12 pages, to appear at COLING 2020 | null | 10.18653/v1/2020.coling-main.4 | null | null | null | null | null | null | null |
2,011.03706 | ESPnet-se: end-to-end speech enhancement and separation toolkit designed
for asr integration | ['Chenda Li', 'Jing Shi', 'Wangyou Zhang', 'Aswin Shanmugam Subramanian', 'Xuankai Chang', 'Naoyuki Kamo', 'Moto Hira', 'Tomoki Hayashi', 'Christoph Boeddeker', 'Zhuo Chen', 'Shinji Watanabe'] | ['eess.AS', 'cs.SD'] | We present ESPnet-SE, which is designed for the quick development of speech
enhancement and speech separation systems in a single framework, along with the
optional downstream speech recognition module. ESPnet-SE is a new project which
integrates rich automatic speech recognition related models, resources and
systems to support and validate the proposed front-end implementation (i.e.
speech enhancement and separation).It is capable of processing both
single-channel and multi-channel data, with various functionalities including
dereverberation, denoising and source separation. We provide all-in-one recipes
including data pre-processing, feature extraction, training and evaluation
pipelines for a wide range of benchmark datasets. This paper describes the
design of the toolkit, several important functionalities, especially the speech
recognition integration, which differentiates ESPnet-SE from other open source
toolkits, and experimental results with major benchmark datasets. | 2020-11-07T06:14:18Z | Accepted by SLT 2021 | null | 10.1109/SLT48900.2021.9383615 | ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for ASR Integration | ['Chenda Li', 'Jing Shi', 'Wangyou Zhang', 'A. Subramanian', 'Xuankai Chang', 'Naoyuki Kamo', 'Moto Hira', 'Tomoki Hayashi', 'Christoph Boeddeker', 'Zhuo Chen', 'Shinji Watanabe'] | 2,020 | Spoken Language Technology Workshop | 82 | 54 | ['Computer Science', 'Engineering'] |
2,011.04784 | EstBERT: A Pretrained Language-Specific BERT for Estonian | ['Hasan Tanvir', 'Claudia Kittask', 'Sandra Eiche', 'Kairit Sirts'] | ['cs.CL'] | This paper presents EstBERT, a large pretrained transformer-based
language-specific BERT model for Estonian. Recent work has evaluated
multilingual BERT models on Estonian tasks and found them to outperform the
baselines. Still, based on existing studies on other languages, a
language-specific BERT model is expected to improve over the multilingual ones.
We first describe the EstBERT pretraining process and then present the results
of the models based on finetuned EstBERT for multiple NLP tasks, including POS
and morphological tagging, named entity recognition and text classification.
The evaluation results show that the models based on EstBERT outperform
multilingual BERT models on five tasks out of six, providing further evidence
towards a view that training language-specific BERT models are still useful,
even when multilingual models are available. | 2020-11-09T21:33:53Z | NoDaLiDa 2021 | null | null | EstBERT: A Pretrained Language-Specific BERT for Estonian | ['Hasan Tanvir', 'Claudia Kittask', 'Kairit Sirts'] | 2,020 | Nordic Conference of Computational Linguistics | 37 | 26 | ['Computer Science'] |
2,011.06294 | Real-Time Intermediate Flow Estimation for Video Frame Interpolation | ['Zhewei Huang', 'Tianyuan Zhang', 'Wen Heng', 'Boxin Shi', 'Shuchang Zhou'] | ['cs.CV', 'cs.LG'] | Real-time video frame interpolation (VFI) is very useful in video processing,
media players, and display devices. We propose RIFE, a Real-time Intermediate
Flow Estimation algorithm for VFI. To realize a high-quality flow-based VFI
method, RIFE uses a neural network named IFNet that can estimate the
intermediate flows end-to-end with much faster speed. A privileged distillation
scheme is designed for stable IFNet training and improve the overall
performance. RIFE does not rely on pre-trained optical flow models and can
support arbitrary-timestep frame interpolation with the temporal encoding
input. Experiments demonstrate that RIFE achieves state-of-the-art performance
on several public benchmarks. Compared with the popular SuperSlomo and DAIN
methods, RIFE is 4--27 times faster and produces better results. Furthermore,
RIFE can be extended to wider applications thanks to temporal encoding. The
code is available at https://github.com/megvii-research/ECCV2022-RIFE. | 2020-11-12T10:12:06Z | Accepted to ECCV 2022 | null | null | null | null | null | null | null | null | null |
2,011.06993 | FLERT: Document-Level Features for Named Entity Recognition | ['Stefan Schweter', 'Alan Akbik'] | ['cs.CL'] | Current state-of-the-art approaches for named entity recognition (NER)
typically consider text at the sentence-level and thus do not model information
that crosses sentence boundaries. However, the use of transformer-based models
for NER offers natural options for capturing document-level features. In this
paper, we perform a comparative evaluation of document-level features in the
two standard NER architectures commonly considered in the literature, namely
"fine-tuning" and "feature-based LSTM-CRF". We evaluate different
hyperparameters for document-level features such as context window size and
enforcing document-locality. We present experiments from which we derive
recommendations for how to model document context and present new
state-of-the-art scores on several CoNLL-03 benchmark datasets. Our approach is
integrated into the Flair framework to facilitate reproduction of our
experiments. | 2020-11-13T16:13:59Z | null | null | null | null | null | null | null | null | null | null |
2,011.09127 | 3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics | ['Huan Fu', 'Bowen Cai', 'Lin Gao', 'Lingxiao Zhang', 'Jiaming Wang Cao Li', 'Zengqi Xun', 'Chengyue Sun', 'Rongfei Jia', 'Binqiang Zhao', 'Hao Zhang'] | ['cs.CV'] | We introduce 3D-FRONT (3D Furnished Rooms with layOuts and semaNTics), a new,
large-scale, and comprehensive repository of synthetic indoor scenes
highlighted by professionally designed layouts and a large number of rooms
populated by high-quality textured 3D models with style compatibility. From
layout semantics down to texture details of individual objects, our dataset is
freely available to the academic community and beyond. Currently, 3D-FRONT
contains 18,968 rooms diversely furnished by 3D objects, far surpassing all
publicly available scene datasets. In addition, the 13,151 furniture objects
all come with high-quality textures. While the floorplans and layout designs
are directly sourced from professional creations, the interior designs in terms
of furniture styles, color, and textures have been carefully curated based on a
recommender system we develop to attain consistent styles as expert designs.
Furthermore, we release Trescope, a light-weight rendering tool, to support
benchmark rendering of 2D images and annotations from 3D-FRONT. We demonstrate
two applications, interior scene synthesis and texture synthesis, that are
especially tailored to the strengths of our new dataset. The project page is
at: https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset. | 2020-11-18T07:14:55Z | Project page:
https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset | null | null | 3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics | ['Huan Fu', 'Bowen Cai', 'Lin Gao', 'Ling-Xiao Zhang', 'Cao Li', 'Zengqi Xun', 'Chengyue Sun', 'Yiyun Fei', 'Yu-qiong Zheng', 'Ying Li', 'Yi Liu', 'Peng Liu', 'Lin Ma', 'Le Weng', 'Xiaohang Hu', 'Xin Ma', 'Qian Qian', 'Rongfei Jia', 'Binqiang Zhao', 'H. Zhang'] | 2,020 | IEEE International Conference on Computer Vision | 276 | 50 | ['Computer Science'] |
2,011.09468 | Gradient Starvation: A Learning Proclivity in Neural Networks | ['Mohammad Pezeshki', 'Sékou-Oumar Kaba', 'Yoshua Bengio', 'Aaron Courville', 'Doina Precup', 'Guillaume Lajoie'] | ['cs.LG', 'math.DS', 'stat.ML'] | We identify and formalize a fundamental gradient descent phenomenon resulting
in a learning proclivity in over-parameterized neural networks. Gradient
Starvation arises when cross-entropy loss is minimized by capturing only a
subset of features relevant for the task, despite the presence of other
predictive features that fail to be discovered. This work provides a
theoretical explanation for the emergence of such feature imbalance in neural
networks. Using tools from Dynamical Systems theory, we identify simple
properties of learning dynamics during gradient descent that lead to this
imbalance, and prove that such a situation can be expected given certain
statistical structure in training data. Based on our proposed formalism, we
develop guarantees for a novel regularization method aimed at decoupling
feature learning dynamics, improving accuracy and robustness in cases hindered
by gradient starvation. We illustrate our findings with simple and real-world
out-of-distribution (OOD) generalization experiments. | 2020-11-18T18:52:08Z | Proceeding of NeurIPS 2021 | null | null | Gradient Starvation: A Learning Proclivity in Neural Networks | ['M. Pezeshki', 'S. Kaba', 'Y. Bengio', 'Aaron C. Courville', 'Doina Precup', 'Guillaume Lajoie'] | 2,020 | Neural Information Processing Systems | 269 | 117 | ['Computer Science', 'Mathematics'] |
2,011.1245 | Sparse R-CNN: End-to-End Object Detection with Learnable Proposals | ['Peize Sun', 'Rufeng Zhang', 'Yi Jiang', 'Tao Kong', 'Chenfeng Xu', 'Wei Zhan', 'Masayoshi Tomizuka', 'Lei Li', 'Zehuan Yuan', 'Changhu Wang', 'Ping Luo'] | ['cs.CV'] | We present Sparse R-CNN, a purely sparse method for object detection in
images. Existing works on object detection heavily rely on dense object
candidates, such as $k$ anchor boxes pre-defined on all grids of image feature
map of size $H\times W$. In our method, however, a fixed sparse set of learned
object proposals, total length of $N$, are provided to object recognition head
to perform classification and location. By eliminating $HWk$ (up to hundreds of
thousands) hand-designed object candidates to $N$ (e.g. 100) learnable
proposals, Sparse R-CNN completely avoids all efforts related to object
candidates design and many-to-one label assignment. More importantly, final
predictions are directly output without non-maximum suppression post-procedure.
Sparse R-CNN demonstrates accuracy, run-time and training convergence
performance on par with the well-established detector baselines on the
challenging COCO dataset, e.g., achieving 45.0 AP in standard $3\times$
training schedule and running at 22 fps using ResNet-50 FPN model. We hope our
work could inspire re-thinking the convention of dense prior in object
detectors. The code is available at: https://github.com/PeizeSun/SparseR-CNN. | 2020-11-25T00:01:28Z | add test-dev; add crowdhuman | null | null | null | null | null | null | null | null | null |
2,011.13205 | SLURP: A Spoken Language Understanding Resource Package | ['Emanuele Bastianelli', 'Andrea Vanzo', 'Pawel Swietojanski', 'Verena Rieser'] | ['cs.CL', 'cs.LG'] | Spoken Language Understanding infers semantic meaning directly from audio
data, and thus promises to reduce error propagation and misunderstandings in
end-user applications. However, publicly available SLU resources are limited.
In this paper, we release SLURP, a new SLU package containing the following:
(1) A new challenging dataset in English spanning 18 domains, which is
substantially bigger and linguistically more diverse than existing datasets;
(2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A
new transparent metric for entity labelling which enables a detailed error
analysis for identifying potential areas of improvement. SLURP is available at
https: //github.com/pswietojanski/slurp. | 2020-11-26T09:58:20Z | Published at the 2020 Conference on Empirical Methods in Natural
Language Processing (EMNLP-2020) | null | null | null | null | null | null | null | null | null |
2,011.13456 | Score-Based Generative Modeling through Stochastic Differential
Equations | ['Yang Song', 'Jascha Sohl-Dickstein', 'Diederik P. Kingma', 'Abhishek Kumar', 'Stefano Ermon', 'Ben Poole'] | ['cs.LG', 'stat.ML'] | Creating noise from data is easy; creating data from noise is generative
modeling. We present a stochastic differential equation (SDE) that smoothly
transforms a complex data distribution to a known prior distribution by slowly
injecting noise, and a corresponding reverse-time SDE that transforms the prior
distribution back into the data distribution by slowly removing the noise.
Crucially, the reverse-time SDE depends only on the time-dependent gradient
field (\aka, score) of the perturbed data distribution. By leveraging advances
in score-based generative modeling, we can accurately estimate these scores
with neural networks, and use numerical SDE solvers to generate samples. We
show that this framework encapsulates previous approaches in score-based
generative modeling and diffusion probabilistic modeling, allowing for new
sampling procedures and new modeling capabilities. In particular, we introduce
a predictor-corrector framework to correct errors in the evolution of the
discretized reverse-time SDE. We also derive an equivalent neural ODE that
samples from the same distribution as the SDE, but additionally enables exact
likelihood computation, and improved sampling efficiency. In addition, we
provide a new way to solve inverse problems with score-based models, as
demonstrated with experiments on class-conditional generation, image
inpainting, and colorization. Combined with multiple architectural
improvements, we achieve record-breaking performance for unconditional image
generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a
competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity
generation of 1024 x 1024 images for the first time from a score-based
generative model. | 2020-11-26T19:39:10Z | ICLR 2021 (Oral) | null | null | null | null | null | null | null | null | null |
2,012.00413 | CPM: A Large-scale Generative Chinese Pre-trained Language Model | ['Zhengyan Zhang', 'Xu Han', 'Hao Zhou', 'Pei Ke', 'Yuxian Gu', 'Deming Ye', 'Yujia Qin', 'Yusheng Su', 'Haozhe Ji', 'Jian Guan', 'Fanchao Qi', 'Xiaozhi Wang', 'Yanan Zheng', 'Guoyang Zeng', 'Huanqi Cao', 'Shengqi Chen', 'Daixuan Li', 'Zhenbo Sun', 'Zhiyuan Liu', 'Minlie Huang', 'Wentao Han', 'Jie Tang', 'Juanzi Li', 'Xiaoyan Zhu', 'Maosong Sun'] | ['cs.CL'] | Pre-trained Language Models (PLMs) have proven to be beneficial for various
downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570GB
training data, drew a lot of attention due to the capacity of few-shot (even
zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is
still challenging, as the training corpus of GPT-3 is primarily English, and
the parameters are not publicly available. In this technical report, we release
the Chinese Pre-trained Language Model (CPM) with generative pre-training on
large-scale Chinese training data. To the best of our knowledge, CPM, with 2.6
billion parameters and 100GB Chinese training data, is the largest Chinese
pre-trained language model, which could facilitate several downstream Chinese
NLP tasks, such as conversation, essay generation, cloze test, and language
understanding. Extensive experiments demonstrate that CPM achieves strong
performance on many NLP tasks in the settings of few-shot (even zero-shot)
learning. The code and parameters are available at
https://github.com/TsinghuaAI/CPM-Generate. | 2020-12-01T11:32:56Z | null | null | null | CPM: A Large-scale Generative Chinese Pre-trained Language Model | ['Zhengyan Zhang', 'Xu Han', 'Hao Zhou', 'Pei Ke', 'Yuxian Gu', 'Deming Ye', 'Yujia Qin', 'Yusheng Su', 'Haozhe Ji', 'Jian Guan', 'Fanchao Qi', 'Xiaozhi Wang', 'Yanan Zheng', 'Guoyang Zeng', 'Huanqi Cao', 'S. Chen', 'Daixuan Li', 'Zhenbo Sun', 'Zhiyuan Liu', 'Minlie Huang', 'Wentao Han', 'Jie Tang', 'Juan-Zi Li', 'Xiaoyan Zhu', 'Maosong Sun'] | 2,020 | AI Open | 119 | 42 | ['Computer Science'] |
2,012.00483 | ClimaText: A Dataset for Climate Change Topic Detection | ['Francesco S. Varini', 'Jordan Boyd-Graber', 'Massimiliano Ciaramita', 'Markus Leippold'] | ['cs.CL', 'cs.AI'] | Climate change communication in the mass media and other textual sources may
affect and shape public perception. Extracting climate change information from
these sources is an important task, e.g., for filtering content and
e-discovery, sentiment analysis, automatic summarization, question-answering,
and fact-checking. However, automating this process is a challenge, as climate
change is a complex, fast-moving, and often ambiguous topic with scarce
resources for popular text-based AI tasks. In this paper, we introduce
\textsc{ClimaText}, a dataset for sentence-based climate change topic
detection, which we make publicly available. We explore different approaches to
identify the climate change topic in various text sources. We find that popular
keyword-based models are not adequate for such a complex and evolving task.
Context-based algorithms like BERT \cite{devlin2018bert} can detect, in
addition to many trivial cases, a variety of complex and implicit topic
patterns. Nevertheless, our analysis reveals a great potential for improvement
in several directions, such as, e.g., capturing the discussion on indirect
effects of climate change. Hence, we hope this work can serve as a good
starting point for further research on this topic. | 2020-12-01T13:42:37Z | Accepted for the Tackling Climate Change with Machine Learning
Workshop at NeurIPS 2020 | null | null | null | null | null | null | null | null | null |
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