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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,012.00857 | StructFormer: Joint Unsupervised Induction of Dependency and
Constituency Structure from Masked Language Modeling | ['Yikang Shen', 'Yi Tay', 'Che Zheng', 'Dara Bahri', 'Donald Metzler', 'Aaron Courville'] | ['cs.CL', 'cs.AI', 'cs.LG'] | There are two major classes of natural language grammar -- the dependency
grammar that models one-to-one correspondences between words and the
constituency grammar that models the assembly of one or several corresponded
words. While previous unsupervised parsing methods mostly focus on only
inducing one class of gramma... | 2020-12-01T21:54:51Z | Published as a conference paper at ACL 2021 | null | null | StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling | ['Yikang Shen', 'Yi Tay', 'Che Zheng', 'Dara Bahri', 'Donald Metzler', 'Aaron C. Courville'] | 2,020 | Annual Meeting of the Association for Computational Linguistics | 41 | 44 | ['Computer Science'] |
2,012.01477 | The Third DIHARD Diarization Challenge | ['Neville Ryant', 'Prachi Singh', 'Venkat Krishnamohan', 'Rajat Varma', 'Kenneth Church', 'Christopher Cieri', 'Jun Du', 'Sriram Ganapathy', 'Mark Liberman'] | ['eess.AS', 'cs.SD'] | DIHARD III was the third in a series of speaker diarization challenges
intended to improve the robustness of diarization systems to variability in
recording equipment, noise conditions, and conversational domain. Speaker
diarization was evaluated under two speech activity conditions (diarization
from a reference speech... | 2020-12-02T19:33:44Z | arXiv admin note: text overlap with arXiv:1906.07839 | null | null | The Third DIHARD Diarization Challenge | ['Neville Ryant', 'Prachi Singh', 'Venkat Krishnamohan', 'Rajat Varma', 'Kenneth Ward Church', 'C. Cieri', 'Jun Du', 'Sriram Ganapathy', 'M. Liberman'] | 2,020 | Interspeech | 135 | 43 | ['Engineering', 'Computer Science'] |
2,012.01873 | Saying No is An Art: Contextualized Fallback Responses for Unanswerable
Dialogue Queries | ['Ashish Shrivastava', 'Kaustubh Dhole', 'Abhinav Bhatt', 'Sharvani Raghunath'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Despite end-to-end neural systems making significant progress in the last
decade for task-oriented as well as chit-chat based dialogue systems, most
dialogue systems rely on hybrid approaches which use a combination of
rule-based, retrieval and generative approaches for generating a set of ranked
responses. Such dialog... | 2020-12-03T12:34:22Z | ACL-IJCNLP 2021 | null | null | null | null | null | null | null | null | null |
2,012.0211 | GottBERT: a pure German Language Model | ['Raphael Scheible', 'Fabian Thomczyk', 'Patric Tippmann', 'Victor Jaravine', 'Martin Boeker'] | ['cs.CL', 'cs.LG'] | Lately, pre-trained language models advanced the field of natural language
processing (NLP). The introduction of Bidirectional Encoders for Transformers
(BERT) and its optimized version RoBERTa have had significant impact and
increased the relevance of pre-trained models. First, research in this field
mainly started on... | 2020-12-03T17:45:03Z | null | null | 10.18653/v1/2024.emnlp-main.1183 | GottBERT: a pure German Language Model | ['Raphael Scheible', 'Fabian Thomczyk', 'P. Tippmann', 'V. Jaravine', 'M. Boeker'] | 2,020 | Conference on Empirical Methods in Natural Language Processing | 81 | 35 | ['Computer Science'] |
2,012.02613 | FinnSentiment -- A Finnish Social Media Corpus for Sentiment Polarity
Annotation | ['Krister Lindén', 'Tommi Jauhiainen', 'Sam Hardwick'] | ['cs.CL'] | Sentiment analysis and opinion mining is an important task with obvious
application areas in social media, e.g. when indicating hate speech and fake
news. In our survey of previous work, we note that there is no large-scale
social media data set with sentiment polarity annotations for Finnish. This
publications aims to... | 2020-12-04T14:17:46Z | null | null | null | null | null | null | null | null | null | null |
2,012.02951 | FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene
Understanding | ['Maryam Rahnemoonfar', 'Tashnim Chowdhury', 'Argho Sarkar', 'Debvrat Varshney', 'Masoud Yari', 'Robin Murphy'] | ['cs.CV', '68T45', 'I.4.6'] | Visual scene understanding is the core task in making any crucial decision in
any computer vision system. Although popular computer vision datasets like
Cityscapes, MS-COCO, PASCAL provide good benchmarks for several tasks (e.g.
image classification, segmentation, object detection), these datasets are
hardly suitable f... | 2020-12-05T05:15:36Z | 11 pages | null | null | null | null | null | null | null | null | null |
2,012.03308 | TediGAN: Text-Guided Diverse Face Image Generation and Manipulation | ['Weihao Xia', 'Yujiu Yang', 'Jing-Hao Xue', 'Baoyuan Wu'] | ['cs.CV', 'cs.AI', 'cs.MM'] | In this work, we propose TediGAN, a novel framework for multi-modal image
generation and manipulation with textual descriptions. The proposed method
consists of three components: StyleGAN inversion module, visual-linguistic
similarity learning, and instance-level optimization. The inversion module maps
real images to t... | 2020-12-06T16:20:19Z | CVPR 2021. Code: https://github.com/weihaox/TediGAN Data:
https://github.com/weihaox/Multi-Modal-CelebA-HQ Video:
https://youtu.be/L8Na2f5viAM | null | null | TediGAN: Text-Guided Diverse Image Generation and Manipulation | ['Weihao Xia', 'Yujiu Yang', 'Jing-Hao Xue', 'Baoyuan Wu'] | 2,020 | arXiv.org | 23 | 64 | ['Computer Science'] |
2,012.03411 | MLS: A Large-Scale Multilingual Dataset for Speech Research | ['Vineel Pratap', 'Qiantong Xu', 'Anuroop Sriram', 'Gabriel Synnaeve', 'Ronan Collobert'] | ['eess.AS', 'cs.CL', 'cs.SD'] | This paper introduces Multilingual LibriSpeech (MLS) dataset, a large
multilingual corpus suitable for speech research. The dataset is derived from
read audiobooks from LibriVox and consists of 8 languages, including about
44.5K hours of English and a total of about 6K hours for other languages.
Additionally, we provid... | 2020-12-07T01:53:45Z | null | Interspeech 2020 | 10.21437/Interspeech.2020-2826 | null | null | null | null | null | null | null |
2,012.03619 | Structural Text Segmentation of Legal Documents | ['Dennis Aumiller', 'Satya Almasian', 'Sebastian Lackner', 'Michael Gertz'] | ['cs.CL'] | The growing complexity of legal cases has lead to an increasing interest in
legal information retrieval systems that can effectively satisfy user-specific
information needs. However, such downstream systems typically require documents
to be properly formatted and segmented, which is often done with relatively
simple pr... | 2020-12-07T12:09:37Z | null | null | 10.1145/3462757.3466085 | null | null | null | null | null | null | null |
2,012.04584 | Distilling Knowledge from Reader to Retriever for Question Answering | ['Gautier Izacard', 'Edouard Grave'] | ['cs.CL', 'cs.LG'] | The task of information retrieval is an important component of many natural
language processing systems, such as open domain question answering. While
traditional methods were based on hand-crafted features, continuous
representations based on neural networks recently obtained competitive results.
A challenge of using ... | 2020-12-08T17:36:34Z | null | null | null | Distilling Knowledge from Reader to Retriever for Question Answering | ['Gautier Izacard', 'Edouard Grave'] | 2,020 | International Conference on Learning Representations | 267 | 41 | ['Computer Science'] |
2,012.05483 | Specialization maps for Scholze's category of diamonds | ['Ian Gleason'] | ['math.AG', 'math.NT'] | We introduce the specialization map in Scholzes theory of diamonds. We
consider v-sheaves that behave like formal schemes and call them kimberlites.
We attach to them: a reduced special fiber, an analytic locus, a specialization
map, a Zariski site, and an etale site. When the kimberlite comes from a formal
scheme, our... | 2020-12-10T07:00:21Z | The material of specialization maps for moduli spaces of p-adic
shtukas can now be found in arXiv:2107.03579 | null | null | null | null | null | null | null | null | null |
2,012.05628 | As Good as New. How to Successfully Recycle English GPT-2 to Make Models
for Other Languages | ['Wietse de Vries', 'Malvina Nissim'] | ['cs.CL'] | Large generative language models have been very successful for English, but
other languages lag behind, in part due to data and computational limitations.
We propose a method that may overcome these problems by adapting existing
pre-trained models to new languages. Specifically, we describe the adaptation
of English GP... | 2020-12-10T12:27:16Z | Findings of ACL 2021 Camera Ready | Findings of the Association for Computational Linguistics:
ACL-IJCNLP 2021 | 10.18653/v1/2021.findings-acl.74 | As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages | ['Wietse de Vries', 'M. Nissim'] | 2,020 | Findings | 78 | 42 | ['Computer Science'] |
2,012.06785 | DETR for Crowd Pedestrian Detection | ['Matthieu Lin', 'Chuming Li', 'Xingyuan Bu', 'Ming Sun', 'Chen Lin', 'Junjie Yan', 'Wanli Ouyang', 'Zhidong Deng'] | ['cs.CV'] | Pedestrian detection in crowd scenes poses a challenging problem due to the
heuristic defined mapping from anchors to pedestrians and the conflict between
NMS and highly overlapped pedestrians. The recently proposed end-to-end
detectors(ED), DETR and deformable DETR, replace hand designed components such
as NMS and anc... | 2020-12-12T11:02:05Z | null | null | null | null | null | null | null | null | null | null |
2,012.07436 | Informer: Beyond Efficient Transformer for Long Sequence Time-Series
Forecasting | ['Haoyi Zhou', 'Shanghang Zhang', 'Jieqi Peng', 'Shuai Zhang', 'Jianxin Li', 'Hui Xiong', 'Wancai Zhang'] | ['cs.LG', 'cs.AI', 'cs.IR'] | Many real-world applications require the prediction of long sequence
time-series, such as electricity consumption planning. Long sequence
time-series forecasting (LSTF) demands a high prediction capacity of the model,
which is the ability to capture precise long-range dependency coupling between
output and input effici... | 2020-12-14T11:43:09Z | 8 pages (main), 5 pages (appendix) and to be appeared in AAAI2021 | null | null | Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting | ['Haoyi Zhou', 'Shanghang Zhang', 'Jieqi Peng', 'Shuai Zhang', 'Jianxin Li', 'Hui Xiong', 'Wan Zhang'] | 2,020 | AAAI Conference on Artificial Intelligence | 4,298 | 57 | ['Computer Science'] |
2,012.07791 | img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation | ['Vítor Albiero', 'Xingyu Chen', 'Xi Yin', 'Guan Pang', 'Tal Hassner'] | ['cs.CV'] | We propose real-time, six degrees of freedom (6DoF), 3D face pose estimation
without face detection or landmark localization. We observe that estimating the
6DoF rigid transformation of a face is a simpler problem than facial landmark
detection, often used for 3D face alignment. In addition, 6DoF offers more
informatio... | 2020-12-14T18:26:20Z | To appear in CVPR 2021. Joint first authorship: V\'itor Albiero and
Xingyu Chen | null | null | null | null | null | null | null | null | null |
2,012.09841 | Taming Transformers for High-Resolution Image Synthesis | ['Patrick Esser', 'Robin Rombach', 'Björn Ommer'] | ['cs.CV'] | Designed to learn long-range interactions on sequential data, transformers
continue to show state-of-the-art results on a wide variety of tasks. In
contrast to CNNs, they contain no inductive bias that prioritizes local
interactions. This makes them expressive, but also computationally infeasible
for long sequences, su... | 2020-12-17T18:57:28Z | Changelog can be found in the supplementary | null | null | Taming Transformers for High-Resolution Image Synthesis | ['Patrick Esser', 'Robin Rombach', 'B. Ommer'] | 2,020 | Computer Vision and Pattern Recognition | 3,016 | 82 | ['Computer Science'] |
2,012.10289 | HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection | ['Binny Mathew', 'Punyajoy Saha', 'Seid Muhie Yimam', 'Chris Biemann', 'Pawan Goyal', 'Animesh Mukherjee'] | ['cs.CL', 'cs.AI', 'cs.SI'] | Hate speech is a challenging issue plaguing the online social media. While
better models for hate speech detection are continuously being developed, there
is little research on the bias and interpretability aspects of hate speech. In
this paper, we introduce HateXplain, the first benchmark hate speech dataset
covering ... | 2020-12-18T15:12:14Z | 12 pages, 7 figues, 8 tables. Accepted at AAAI 2021 | null | null | HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection | ['Binny Mathew', 'Punyajoy Saha', 'Seid Muhie Yimam', 'Chris Biemann', 'Pawan Goyal', 'Animesh Mukherjee'] | 2,020 | AAAI Conference on Artificial Intelligence | 582 | 60 | ['Computer Science'] |
2,012.10309 | Learning Contextual Representations for Semantic Parsing with
Generation-Augmented Pre-Training | ['Peng Shi', 'Patrick Ng', 'Zhiguo Wang', 'Henghui Zhu', 'Alexander Hanbo Li', 'Jun Wang', 'Cicero Nogueira dos Santos', 'Bing Xiang'] | ['cs.CL'] | Most recently, there has been significant interest in learning contextual
representations for various NLP tasks, by leveraging large scale text corpora
to train large neural language models with self-supervised learning objectives,
such as Masked Language Model (MLM). However, based on a pilot study, we
observe three i... | 2020-12-18T15:53:50Z | Accepted to AAAI 2021 | null | null | Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training | ['Peng Shi', 'Patrick Ng', 'Zhiguo Wang', 'Henghui Zhu', 'Alexander Hanbo Li', 'Jun Wang', 'C. D. Santos', 'Bing Xiang'] | 2,020 | AAAI Conference on Artificial Intelligence | 117 | 48 | ['Computer Science'] |
2,012.1182 | Recognizing Emotion Cause in Conversations | ['Soujanya Poria', 'Navonil Majumder', 'Devamanyu Hazarika', 'Deepanway Ghosal', 'Rishabh Bhardwaj', 'Samson Yu Bai Jian', 'Pengfei Hong', 'Romila Ghosh', 'Abhinaba Roy', 'Niyati Chhaya', 'Alexander Gelbukh', 'Rada Mihalcea'] | ['cs.CL'] | We address the problem of recognizing emotion cause in conversations, define
two novel sub-tasks of this problem, and provide a corresponding dialogue-level
dataset, along with strong Transformer-based baselines. The dataset is
available at https://github.com/declare-lab/RECCON.
Introduction: Recognizing the cause be... | 2020-12-22T03:51:35Z | https://github.com/declare-lab/RECCON, Accepted at Cognitive
Computation | null | null | Recognizing Emotion Cause in Conversations | ['Soujanya Poria', 'Navonil Majumder', 'Devamanyu Hazarika', 'Deepanway Ghosal', 'Rishabh Bhardwaj', 'Samson Yu', 'Pengfei Hong', 'Romila Ghosh', 'Niyati Chhaya', 'A. Gelbukh', 'Rada Mihalcea'] | 2,020 | Cognitive Computation | 129 | 45 | ['Computer Science'] |
2,012.12624 | Learning Dense Representations of Phrases at Scale | ['Jinhyuk Lee', 'Mujeen Sung', 'Jaewoo Kang', 'Danqi Chen'] | ['cs.CL'] | Open-domain question answering can be reformulated as a phrase retrieval
problem, without the need for processing documents on-demand during inference
(Seo et al., 2019). However, current phrase retrieval models heavily depend on
sparse representations and still underperform retriever-reader approaches. In
this work, w... | 2020-12-23T12:28:17Z | ACL 2021. Code available at
https://github.com/princeton-nlp/DensePhrases | null | null | Learning Dense Representations of Phrases at Scale | ['Jinhyuk Lee', 'Mujeen Sung', 'Jaewoo Kang', 'Danqi Chen'] | 2,020 | Annual Meeting of the Association for Computational Linguistics | 122 | 52 | ['Computer Science'] |
2,012.12877 | Training data-efficient image transformers & distillation through
attention | ['Hugo Touvron', 'Matthieu Cord', 'Matthijs Douze', 'Francisco Massa', 'Alexandre Sablayrolles', 'Hervé Jégou'] | ['cs.CV'] | Recently, neural networks purely based on attention were shown to address
image understanding tasks such as image classification. However, these visual
transformers are pre-trained with hundreds of millions of images using an
expensive infrastructure, thereby limiting their adoption.
In this work, we produce a compet... | 2020-12-23T18:42:10Z | null | null | null | null | null | null | null | null | null | null |
2,012.13577 | LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification | ['Jiangjie Chen', 'Qiaoben Bao', 'Changzhi Sun', 'Xinbo Zhang', 'Jiaze Chen', 'Hao Zhou', 'Yanghua Xiao', 'Lei Li'] | ['cs.CL', 'cs.AI'] | Given a natural language statement, how to verify its veracity against a
large-scale textual knowledge source like Wikipedia? Most existing neural
models make predictions without giving clues about which part of a false claim
goes wrong. In this paper, we propose LOREN, an approach for interpretable fact
verification. ... | 2020-12-25T13:57:04Z | Accepted to AAAI 2022 | null | 10.1609/aaai.v36i10.21291 | null | null | null | null | null | null | null |
2,012.1421 | The Curse of Dense Low-Dimensional Information Retrieval for Large Index
Sizes | ['Nils Reimers', 'Iryna Gurevych'] | ['cs.IR', 'cs.CL'] | Information Retrieval using dense low-dimensional representations recently
became popular and showed out-performance to traditional sparse-representations
like BM25. However, no previous work investigated how dense representations
perform with large index sizes. We show theoretically and empirically that the
performanc... | 2020-12-28T12:25:25Z | Published at ACL 2021 | null | null | null | null | null | null | null | null | null |
2,012.14353 | DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced
Bengali Language | ['Md. Rezaul Karim', 'Sumon Kanti Dey', 'Tanhim Islam', 'Sagor Sarker', 'Mehadi Hasan Menon', 'Kabir Hossain', 'Bharathi Raja Chakravarthi', 'Md. Azam Hossain', 'Stefan Decker'] | ['cs.CL', 'cs.LG'] | The exponential growths of social media and micro-blogging sites not only
provide platforms for empowering freedom of expressions and individual voices,
but also enables people to express anti-social behaviour like online
harassment, cyberbullying, and hate speech. Numerous works have been proposed
to utilize textual d... | 2020-12-28T16:46:03Z | Proceeding of IEEE International Conference on Data Science and
Advanced Analytics (DSAA'2021), October 6-9, 2021, Porto, Portugal | null | null | DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali Language | ['Md. Rezaul Karim', 'Sumon Dey', 'Bharathi Raja Chakravarthi'] | 2,020 | International Conference on Data Science and Advanced Analytics | 85 | 36 | ['Computer Science'] |
2,012.1474 | LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document
Understanding | ['Yang Xu', 'Yiheng Xu', 'Tengchao Lv', 'Lei Cui', 'Furu Wei', 'Guoxin Wang', 'Yijuan Lu', 'Dinei Florencio', 'Cha Zhang', 'Wanxiang Che', 'Min Zhang', 'Lidong Zhou'] | ['cs.CL'] | Pre-training of text and layout has proved effective in a variety of
visually-rich document understanding tasks due to its effective model
architecture and the advantage of large-scale unlabeled scanned/digital-born
documents. We propose LayoutLMv2 architecture with new pre-training tasks to
model the interaction among... | 2020-12-29T13:01:52Z | ACL 2021 main conference | null | null | null | null | null | null | null | null | null |
2,012.15349 | DynaSent: A Dynamic Benchmark for Sentiment Analysis | ['Christopher Potts', 'Zhengxuan Wu', 'Atticus Geiger', 'Douwe Kiela'] | ['cs.CL'] | We introduce DynaSent ('Dynamic Sentiment'), a new English-language benchmark
task for ternary (positive/negative/neutral) sentiment analysis. DynaSent
combines naturally occurring sentences with sentences created using the
open-source Dynabench Platform, which facilities human-and-model-in-the-loop
dataset creation. D... | 2020-12-30T22:38:21Z | null | null | null | null | null | null | null | null | null | null |
2,012.15516 | AraELECTRA: Pre-Training Text Discriminators for Arabic Language
Understanding | ['Wissam Antoun', 'Fady Baly', 'Hazem Hajj'] | ['cs.CL'] | Advances in English language representation enabled a more sample-efficient
pre-training task by Efficiently Learning an Encoder that Classifies Token
Replacements Accurately (ELECTRA). Which, instead of training a model to
recover masked tokens, it trains a discriminator model to distinguish true
input tokens from cor... | 2020-12-31T09:35:39Z | null | null | null | null | null | null | null | null | null | null |
2,012.1552 | AraGPT2: Pre-Trained Transformer for Arabic Language Generation | ['Wissam Antoun', 'Fady Baly', 'Hazem Hajj'] | ['cs.CL'] | Recently, pre-trained transformer-based architectures have proven to be very
efficient at language modeling and understanding, given that they are trained
on a large enough corpus. Applications in language generation for Arabic are
still lagging in comparison to other NLP advances primarily due to the lack of
advanced ... | 2020-12-31T09:48:05Z | null | null | null | null | null | null | null | null | null | null |
2,012.15562 | UNKs Everywhere: Adapting Multilingual Language Models to New Scripts | ['Jonas Pfeiffer', 'Ivan Vulić', 'Iryna Gurevych', 'Sebastian Ruder'] | ['cs.CL'] | Massively multilingual language models such as multilingual BERT offer
state-of-the-art cross-lingual transfer performance on a range of NLP tasks.
However, due to limited capacity and large differences in pretraining data
sizes, there is a profound performance gap between resource-rich and
resource-poor target languag... | 2020-12-31T11:37:28Z | EMNLP 2021 | null | null | null | null | null | null | null | null | null |
2,012.15613 | How Good is Your Tokenizer? On the Monolingual Performance of
Multilingual Language Models | ['Phillip Rust', 'Jonas Pfeiffer', 'Ivan Vulić', 'Sebastian Ruder', 'Iryna Gurevych'] | ['cs.CL'] | In this work, we provide a systematic and comprehensive empirical comparison
of pretrained multilingual language models versus their monolingual
counterparts with regard to their monolingual task performance. We study a set
of nine typologically diverse languages with readily available pretrained
monolingual models on ... | 2020-12-31T14:11:00Z | ACL 2021 | null | null | null | null | null | null | null | null | null |
2,012.15674 | ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual
Semantics with Monolingual Corpora | ['Xuan Ouyang', 'Shuohuan Wang', 'Chao Pang', 'Yu Sun', 'Hao Tian', 'Hua Wu', 'Haifeng Wang'] | ['cs.CL'] | Recent studies have demonstrated that pre-trained cross-lingual models
achieve impressive performance in downstream cross-lingual tasks. This
improvement benefits from learning a large amount of monolingual and parallel
corpora. Although it is generally acknowledged that parallel corpora are
critical for improving the ... | 2020-12-31T15:52:27Z | Accepted by EMNLP 2021 (main conference, long paper) | null | null | null | null | null | null | null | null | null |
2,012.15761 | Learning from the Worst: Dynamically Generated Datasets to Improve
Online Hate Detection | ['Bertie Vidgen', 'Tristan Thrush', 'Zeerak Waseem', 'Douwe Kiela'] | ['cs.CL', 'cs.LG'] | We present a human-and-model-in-the-loop process for dynamically generating
datasets and training better performing and more robust hate detection models.
We provide a new dataset of ~40,000 entries, generated and labelled by trained
annotators over four rounds of dynamic data creation. It includes ~15,000
challenging ... | 2020-12-31T17:36:48Z | null | null | null | null | null | null | null | null | null | null |
2,012.15828 | MiniLMv2: Multi-Head Self-Attention Relation Distillation for
Compressing Pretrained Transformers | ['Wenhui Wang', 'Hangbo Bao', 'Shaohan Huang', 'Li Dong', 'Furu Wei'] | ['cs.CL'] | We generalize deep self-attention distillation in MiniLM (Wang et al., 2020)
by only using self-attention relation distillation for task-agnostic
compression of pretrained Transformers. In particular, we define multi-head
self-attention relations as scaled dot-product between the pairs of query, key,
and value vectors ... | 2020-12-31T18:51:26Z | Monolingual and multilingual distilled models:
https://github.com/microsoft/unilm/tree/master/minilm | null | null | null | null | null | null | null | null | null |
2,012.1584 | Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
with Transformers | ['Sixiao Zheng', 'Jiachen Lu', 'Hengshuang Zhao', 'Xiatian Zhu', 'Zekun Luo', 'Yabiao Wang', 'Yanwei Fu', 'Jianfeng Feng', 'Tao Xiang', 'Philip H. S. Torr', 'Li Zhang'] | ['cs.CV'] | Most recent semantic segmentation methods adopt a fully-convolutional network
(FCN) with an encoder-decoder architecture. The encoder progressively reduces
the spatial resolution and learns more abstract/semantic visual concepts with
larger receptive fields. Since context modeling is critical for segmentation,
the late... | 2020-12-31T18:55:57Z | CVPR 2021. Project page at https://fudan-zvg.github.io/SETR/ | null | null | Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers | ['Sixiao Zheng', 'Jiachen Lu', 'Hengshuang Zhao', 'Xiatian Zhu', 'Zekun Luo', 'Yabiao Wang', 'Yanwei Fu', 'Jianfeng Feng', 'T. Xiang', 'Philip H. S. Torr', 'Li Zhang'] | 2,020 | Computer Vision and Pattern Recognition | 2,928 | 63 | ['Computer Science'] |
2,023.12345 | null | [] | [''] | null | null | null | null | null | null | null | null | null | null | null | null |
2,101.00027 | The Pile: An 800GB Dataset of Diverse Text for Language Modeling | ['Leo Gao', 'Stella Biderman', 'Sid Black', 'Laurence Golding', 'Travis Hoppe', 'Charles Foster', 'Jason Phang', 'Horace He', 'Anish Thite', 'Noa Nabeshima', 'Shawn Presser', 'Connor Leahy'] | ['cs.CL'] | Recent work has demonstrated that increased training dataset diversity
improves general cross-domain knowledge and downstream generalization
capability for large-scale language models. With this in mind, we present
\textit{the Pile}: an 825 GiB English text corpus targeted at training
large-scale language models. The P... | 2020-12-31T19:00:10Z | null | null | null | null | null | null | null | null | null | null |
2,101.0019 | Prefix-Tuning: Optimizing Continuous Prompts for Generation | ['Xiang Lisa Li', 'Percy Liang'] | ['cs.CL'] | Fine-tuning is the de facto way to leverage large pretrained language models
to perform downstream tasks. However, it modifies all the language model
parameters and therefore necessitates storing a full copy for each task. In
this paper, we propose prefix-tuning, a lightweight alternative to fine-tuning
for natural lan... | 2021-01-01T08:00:36Z | null | null | null | Prefix-Tuning: Optimizing Continuous Prompts for Generation | ['Xiang Lisa Li', 'Percy Liang'] | 2,021 | Annual Meeting of the Association for Computational Linguistics | 4,340 | 55 | ['Computer Science'] |
2,101.00204 | BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource
Language Understanding Evaluation in Bangla | ['Abhik Bhattacharjee', 'Tahmid Hasan', 'Wasi Uddin Ahmad', 'Kazi Samin', 'Md Saiful Islam', 'Anindya Iqbal', 'M. Sohel Rahman', 'Rifat Shahriyar'] | ['cs.CL'] | In this work, we introduce BanglaBERT, a BERT-based Natural Language
Understanding (NLU) model pretrained in Bangla, a widely spoken yet
low-resource language in the NLP literature. To pretrain BanglaBERT, we collect
27.5 GB of Bangla pretraining data (dubbed `Bangla2B+') by crawling 110 popular
Bangla sites. We introd... | 2021-01-01T09:28:45Z | Findings of North American Chapter of the Association for
Computational Linguistics, NAACL 2022 (camera-ready) | null | null | BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla | ['Abhik Bhattacharjee', 'Tahmid Hasan', 'Kazi Samin Mubasshir', 'Md. Saiful Islam', 'Wasi Uddin Ahmad', 'Anindya Iqbal', 'M. Rahman', 'Rifat Shahriyar'] | 2,021 | NAACL-HLT | 180 | 58 | ['Computer Science'] |
2,101.0039 | VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation | ['Changhan Wang', 'Morgane Rivière', 'Ann Lee', 'Anne Wu', 'Chaitanya Talnikar', 'Daniel Haziza', 'Mary Williamson', 'Juan Pino', 'Emmanuel Dupoux'] | ['cs.CL', 'eess.AS'] | We introduce VoxPopuli, a large-scale multilingual corpus providing 100K
hours of unlabelled speech data in 23 languages. It is the largest open data to
date for unsupervised representation learning as well as semi-supervised
learning. VoxPopuli also contains 1.8K hours of transcribed speeches in 16
languages and their... | 2021-01-02T07:24:21Z | Accepted to ACL 2021 (long paper) | null | null | null | null | null | null | null | null | null |
2,101.00406 | CDLM: Cross-Document Language Modeling | ['Avi Caciularu', 'Arman Cohan', 'Iz Beltagy', 'Matthew E. Peters', 'Arie Cattan', 'Ido Dagan'] | ['cs.CL'] | We introduce a new pretraining approach geared for multi-document language
modeling, incorporating two key ideas into the masked language modeling
self-supervised objective. First, instead of considering documents in
isolation, we pretrain over sets of multiple related documents, encouraging the
model to learn cross-do... | 2021-01-02T09:01:39Z | EMNLP 2021, findings | null | null | null | null | null | null | null | null | null |
2,101.00416 | Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting | ['Wangchunshu Zhou', 'Tao Ge', 'Canwen Xu', 'Ke Xu', 'Furu Wei'] | ['cs.CL'] | In this paper, we generalize text infilling (e.g., masked language models) by
proposing Sequence Span Rewriting (SSR) as a self-supervised
sequence-to-sequence (seq2seq) pre-training objective. SSR provides more
fine-grained learning signals for text representations by supervising the model
to rewrite imperfect spans t... | 2021-01-02T10:27:11Z | null | null | null | null | null | null | null | null | null | null |
2,101.00434 | Coreference Resolution without Span Representations | ['Yuval Kirstain', 'Ori Ram', 'Omer Levy'] | ['cs.CL'] | The introduction of pretrained language models has reduced many complex
task-specific NLP models to simple lightweight layers. An exception to this
trend is coreference resolution, where a sophisticated task-specific model is
appended to a pretrained transformer encoder. While highly effective, the model
has a very lar... | 2021-01-02T11:46:51Z | Accepted to ACL 2021 | null | null | Coreference Resolution without Span Representations | ['Yuval Kirstain', 'Ori Ram', 'Omer Levy'] | 2,021 | Annual Meeting of the Association for Computational Linguistics | 72 | 18 | ['Computer Science'] |
2,101.00436 | Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval | ['Omar Khattab', 'Christopher Potts', 'Matei Zaharia'] | ['cs.CL', 'cs.IR'] | Multi-hop reasoning (i.e., reasoning across two or more documents) is a key
ingredient for NLP models that leverage large corpora to exhibit broad
knowledge. To retrieve evidence passages, multi-hop models must contend with a
fast-growing search space across the hops, represent complex queries that
combine multiple inf... | 2021-01-02T11:52:20Z | NeurIPS 2021 (Spotlight) | null | null | Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval | ['O. Khattab', 'Christopher Potts', 'M. Zaharia'] | 2,021 | Neural Information Processing Systems | 58 | 32 | ['Computer Science'] |
2,101.00438 | Few-Shot Question Answering by Pretraining Span Selection | ['Ori Ram', 'Yuval Kirstain', 'Jonathan Berant', 'Amir Globerson', 'Omer Levy'] | ['cs.CL'] | In several question answering benchmarks, pretrained models have reached
human parity through fine-tuning on an order of 100,000 annotated questions and
answers. We explore the more realistic few-shot setting, where only a few
hundred training examples are available, and observe that standard models
perform poorly, hig... | 2021-01-02T11:58:44Z | Accepted to ACL 2021 | null | null | null | null | null | null | null | null | null |
2,101.01039 | Improving reference mining in patents with BERT | ['Ken Voskuil', 'Suzan Verberne'] | ['cs.IR', 'cs.CL', 'H.3.1; I.2.7'] | In this paper we address the challenge of extracting scientific references
from patents. We approach the problem as a sequence labelling task and
investigate the merits of BERT models to the extraction of these long
sequences. References in patents to scientific literature are relevant to study
the connection between s... | 2021-01-04T15:56:21Z | 10 pages, 3 figures | Published in the 11th International Workshop on
Bibliometric-enhanced Information Retrieval (BIR 2021) | null | null | null | null | null | null | null | null |
2,101.01213 | Improving Portuguese Semantic Role Labeling with Transformers and
Transfer Learning | ['Sofia Oliveira', 'Daniel Loureiro', 'Alípio Jorge'] | ['cs.CL'] | The Natural Language Processing task of determining "Who did what to whom" is
called Semantic Role Labeling. For English, recent methods based on Transformer
models have allowed for major improvements in this task over the previous state
of the art. However, for low resource languages, like Portuguese, currently
availa... | 2021-01-04T19:56:01Z | 30 pages, 3 figures; Fixed broken links in References | 2021 IEEE 8th International Conference on Data Science and
Advanced Analytics (DSAA), 2021, pp. 1-9 | 10.1109/DSAA53316.2021.9564238 | null | null | null | null | null | null | null |
2,101.01321 | I-BERT: Integer-only BERT Quantization | ['Sehoon Kim', 'Amir Gholami', 'Zhewei Yao', 'Michael W. Mahoney', 'Kurt Keutzer'] | ['cs.CL'] | Transformer based models, like BERT and RoBERTa, have achieved
state-of-the-art results in many Natural Language Processing tasks. However,
their memory footprint, inference latency, and power consumption are
prohibitive efficient inference at the edge, and even at the data center. While
quantization can be a viable so... | 2021-01-05T02:42:58Z | null | ICML 2021 (Oral) | null | null | null | null | null | null | null | null |
2,101.02235 | Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit
Reasoning Strategies | ['Mor Geva', 'Daniel Khashabi', 'Elad Segal', 'Tushar Khot', 'Dan Roth', 'Jonathan Berant'] | ['cs.CL'] | A key limitation in current datasets for multi-hop reasoning is that the
required steps for answering the question are mentioned in it explicitly. In
this work, we introduce StrategyQA, a question answering (QA) benchmark where
the required reasoning steps are implicit in the question, and should be
inferred using a st... | 2021-01-06T19:14:23Z | Accepted for publication in Transactions of the Association for
Computational Linguistics (TACL), 2021. Author's final version | null | null | null | null | null | null | null | null | null |
2,101.02477 | GAN-Control: Explicitly Controllable GANs | ['Alon Shoshan', 'Nadav Bhonker', 'Igor Kviatkovsky', 'Gerard Medioni'] | ['cs.CV'] | We present a framework for training GANs with explicit control over generated
images. We are able to control the generated image by settings exact attributes
such as age, pose, expression, etc. Most approaches for editing GAN-generated
images achieve partial control by leveraging the latent space disentanglement
proper... | 2021-01-07T10:54:17Z | null | null | null | null | null | null | null | null | null | null |
2,101.03697 | RepVGG: Making VGG-style ConvNets Great Again | ['Xiaohan Ding', 'Xiangyu Zhang', 'Ningning Ma', 'Jungong Han', 'Guiguang Ding', 'Jian Sun'] | ['cs.CV', 'cs.AI', 'cs.LG'] | We present a simple but powerful architecture of convolutional neural
network, which has a VGG-like inference-time body composed of nothing but a
stack of 3x3 convolution and ReLU, while the training-time model has a
multi-branch topology. Such decoupling of the training-time and inference-time
architecture is realized... | 2021-01-11T04:46:11Z | CVPR 2021 | null | null | null | null | null | null | null | null | null |
2,101.03961 | Switch Transformers: Scaling to Trillion Parameter Models with Simple
and Efficient Sparsity | ['William Fedus', 'Barret Zoph', 'Noam Shazeer'] | ['cs.LG', 'cs.AI'] | In deep learning, models typically reuse the same parameters for all inputs.
Mixture of Experts (MoE) defies this and instead selects different parameters
for each incoming example. The result is a sparsely-activated model -- with
outrageous numbers of parameters -- but a constant computational cost. However,
despite s... | 2021-01-11T16:11:52Z | JMLR | null | null | Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity | ['W. Fedus', 'Barret Zoph', 'Noam M. Shazeer'] | 2,021 | Journal of machine learning research | 2,249 | 65 | ['Computer Science'] |
2,101.04061 | Towards Real-World Blind Face Restoration with Generative Facial Prior | ['Xintao Wang', 'Yu Li', 'Honglun Zhang', 'Ying Shan'] | ['cs.CV'] | Blind face restoration usually relies on facial priors, such as facial
geometry prior or reference prior, to restore realistic and faithful details.
However, very low-quality inputs cannot offer accurate geometric prior while
high-quality references are inaccessible, limiting the applicability in
real-world scenarios. ... | 2021-01-11T17:54:38Z | CVPR 2021. Codes: https://github.com/TencentARC/GFPGAN | null | null | null | null | null | null | null | null | null |
2,101.04615 | Toward Effective Automated Content Analysis via Crowdsourcing | ['Jiele Wu', 'Chau-Wai Wong', 'Xinyan Zhao', 'Xianpeng Liu'] | ['cs.CL', 'cs.IR', 'cs.LG'] | Many computer scientists use the aggregated answers of online workers to
represent ground truth. Prior work has shown that aggregation methods such as
majority voting are effective for measuring relatively objective features. For
subjective features such as semantic connotation, online workers, known for
optimizing the... | 2021-01-12T17:14:18Z | Corrected minor typos. Camera-ready version for the 2021 IEEE
International Conference on Multimedia and Expo (ICME) | null | null | Toward Effective Automated Content Analysis via Crowdsourcing | ['Jiele Wu', 'Chau-Wai Wong', 'Xinyan Zhao', 'Xianpeng Liu'] | 2,021 | IEEE International Conference on Multimedia and Expo | 4 | 19 | ['Computer Science'] |
2,101.04704 | Boundary-Aware Segmentation Network for Mobile and Web Applications | ['Xuebin Qin', 'Deng-Ping Fan', 'Chenyang Huang', 'Cyril Diagne', 'Zichen Zhang', "Adrià Cabeza Sant'Anna", 'Albert Suàrez', 'Martin Jagersand', 'Ling Shao'] | ['cs.CV'] | Although deep models have greatly improved the accuracy and robustness of
image segmentation, obtaining segmentation results with highly accurate
boundaries and fine structures is still a challenging problem. In this paper,
we propose a simple yet powerful Boundary-Aware Segmentation Network (BASNet),
which comprises a... | 2021-01-12T19:20:26Z | 18 pages, 16 figures, submitted to TPAMI | null | null | Boundary-Aware Segmentation Network for Mobile and Web Applications | ['Xuebin Qin', 'Deng-Ping Fan', 'Chenyang Huang', 'Cyril Diagne', 'Zichen Zhang', "Adria Cabeza Sant'Anna", 'Albert Suàrez', 'Martin Jägersand', 'Ling Shao'] | 2,021 | arXiv.org | 81 | 149 | ['Computer Science'] |
2,101.04775 | Towards Faster and Stabilized GAN Training for High-fidelity Few-shot
Image Synthesis | ['Bingchen Liu', 'Yizhe Zhu', 'Kunpeng Song', 'Ahmed Elgammal'] | ['cs.CV', 'cs.AI'] | Training Generative Adversarial Networks (GAN) on high-fidelity images
usually requires large-scale GPU-clusters and a vast number of training images.
In this paper, we study the few-shot image synthesis task for GAN with minimum
computing cost. We propose a light-weight GAN structure that gains superior
quality on 102... | 2021-01-12T22:02:54Z | ICLR-2021 | null | null | null | null | null | null | null | null | null |
2,101.05667 | The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained
Sequence-to-Sequence Models | ['Ronak Pradeep', 'Rodrigo Nogueira', 'Jimmy Lin'] | ['cs.IR', 'cs.CL'] | We propose a design pattern for tackling text ranking problems, dubbed
"Expando-Mono-Duo", that has been empirically validated for a number of ad hoc
retrieval tasks in different domains. At the core, our design relies on
pretrained sequence-to-sequence models within a standard multi-stage ranking
architecture. "Expand... | 2021-01-14T15:29:54Z | null | null | null | null | null | null | null | null | null | null |
2,101.05716 | SICKNL: A Dataset for Dutch Natural Language Inference | ['Gijs Wijnholds', 'Michael Moortgat'] | ['cs.CL'] | We present SICK-NL (read: signal), a dataset targeting Natural Language
Inference in Dutch. SICK-NL is obtained by translating the SICK dataset of
Marelli et al. (2014)from English into Dutch. Having a parallel inference
dataset allows us to compare both monolingual and multilingual NLP models for
English and Dutch on ... | 2021-01-14T16:42:57Z | To appear at EACL 2021 | null | null | SICK-NL: A Dataset for Dutch Natural Language Inference | ['G. Wijnholds', 'M. Moortgat'] | 2,021 | Conference of the European Chapter of the Association for Computational Linguistics | 26 | 21 | ['Computer Science'] |
2,101.06085 | Deep Dual-resolution Networks for Real-time and Accurate Semantic
Segmentation of Road Scenes | ['Yuanduo Hong', 'Huihui Pan', 'Weichao Sun', 'Yisong Jia'] | ['cs.CV'] | Semantic segmentation is a key technology for autonomous vehicles to
understand the surrounding scenes. The appealing performances of contemporary
models usually come at the expense of heavy computations and lengthy inference
time, which is intolerable for self-driving. Using light-weight architectures
(encoder-decoder... | 2021-01-15T12:56:18Z | 12 pages, 7 figures. This work has been submitted to the IEEE for
possible publication | null | null | null | null | null | null | null | null | null |
2,101.0684 | ZeRO-Offload: Democratizing Billion-Scale Model Training | ['Jie Ren', 'Samyam Rajbhandari', 'Reza Yazdani Aminabadi', 'Olatunji Ruwase', 'Shuangyan Yang', 'Minjia Zhang', 'Dong Li', 'Yuxiong He'] | ['cs.DC', 'cs.LG'] | Large-scale model training has been a playing ground for a limited few
requiring complex model refactoring and access to prohibitively expensive GPU
clusters. ZeRO-Offload changes the large model training landscape by making
large model training accessible to nearly everyone. It can train models with
over 13 billion pa... | 2021-01-18T02:11:25Z | null | null | null | null | null | null | null | null | null | null |
2,101.06983 | Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup | ['Luyu Gao', 'Yunyi Zhang', 'Jiawei Han', 'Jamie Callan'] | ['cs.LG', 'cs.CL', 'cs.IR'] | Contrastive learning has been applied successfully to learn vector
representations of text. Previous research demonstrated that learning
high-quality representations benefits from batch-wise contrastive loss with a
large number of negatives. In practice, the technique of in-batch negative is
used, where for each exampl... | 2021-01-18T10:42:34Z | RepL4NLP 2021 | null | null | null | null | null | null | null | null | null |
2,101.07138 | Teach me how to Label: Labeling Functions from Natural Language with
Text-to-text Transformers | ['Yannis Papanikolaou'] | ['cs.CL', 'cs.LG'] | Annotated data has become the most important bottleneck in training accurate
machine learning models, especially for areas that require domain expertise. A
recent approach to deal with the above issue proposes using natural language
explanations instead of labeling individual data points, thereby increasing
human annot... | 2021-01-18T16:04:15Z | null | null | null | null | null | null | null | null | null | null |
2,101.07597 | UniSpeech: Unified Speech Representation Learning with Labeled and
Unlabeled Data | ['Chengyi Wang', 'Yu Wu', 'Yao Qian', 'Kenichi Kumatani', 'Shujie Liu', 'Furu Wei', 'Michael Zeng', 'Xuedong Huang'] | ['cs.CL', 'cs.LG', 'cs.SD', 'eess.AS'] | In this paper, we propose a unified pre-training approach called UniSpeech to
learn speech representations with both unlabeled and labeled data, in which
supervised phonetic CTC learning and phonetically-aware contrastive
self-supervised learning are conducted in a multi-task learning manner. The
resultant representati... | 2021-01-19T12:53:43Z | accepted by ICML2021 | null | null | UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data | ['Chengyi Wang', 'Yuehua Wu', 'Yu Wu', 'Yao Qian', 'K. Kumatani', 'Shujie Liu', 'Furu Wei', 'Michael Zeng', 'Xuedong Huang'] | 2,021 | International Conference on Machine Learning | 115 | 44 | ['Computer Science', 'Engineering'] |
2,101.08231 | Word Alignment by Fine-tuning Embeddings on Parallel Corpora | ['Zi-Yi Dou', 'Graham Neubig'] | ['cs.CL'] | Word alignment over parallel corpora has a wide variety of applications,
including learning translation lexicons, cross-lingual transfer of language
processing tools, and automatic evaluation or analysis of translation outputs.
The great majority of past work on word alignment has worked by performing
unsupervised lear... | 2021-01-20T17:54:47Z | EACL 2021 | null | null | null | null | null | null | null | null | null |
2,101.08674 | DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset
For Anime Character Recognition | ['Edwin Arkel Rios', 'Wen-Huang Cheng', 'Bo-Cheng Lai'] | ['cs.CV', 'I.2; I.4'] | In this work we tackle the challenging problem of anime character
recognition. Anime, referring to animation produced within Japan and work
derived or inspired from it. For this purpose we present DAF:re
(DanbooruAnimeFaces:revamped), a large-scale, crowd-sourced, long-tailed
dataset with almost 500 K images spread acr... | 2021-01-21T15:40:45Z | 5 pages, 3 figures, 4 tables | null | null | DAF: re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition | ['Edwin Arkel Rios', 'Wen-Huang Cheng', 'B. Lai'] | 2,021 | arXiv.org | 12 | 22 | ['Computer Science'] |
2,101.08692 | Characterizing signal propagation to close the performance gap in
unnormalized ResNets | ['Andrew Brock', 'Soham De', 'Samuel L. Smith'] | ['cs.LG', 'cs.CV', 'stat.ML'] | Batch Normalization is a key component in almost all state-of-the-art image
classifiers, but it also introduces practical challenges: it breaks the
independence between training examples within a batch, can incur compute and
memory overhead, and often results in unexpected bugs. Building on recent
theoretical analyses ... | 2021-01-21T16:07:06Z | Published as a conference paper at ICLR 2021 | null | null | Characterizing signal propagation to close the performance gap in unnormalized ResNets | ['Andrew Brock', 'Soham De', 'Samuel L. Smith'] | 2,021 | International Conference on Learning Representations | 124 | 81 | ['Computer Science', 'Mathematics'] |
2,101.09635 | WangchanBERTa: Pretraining transformer-based Thai Language Models | ['Lalita Lowphansirikul', 'Charin Polpanumas', 'Nawat Jantrakulchai', 'Sarana Nutanong'] | ['cs.CL'] | Transformer-based language models, more specifically BERT-based architectures
have achieved state-of-the-art performance in many downstream tasks. However,
for a relatively low-resource language such as Thai, the choices of models are
limited to training a BERT-based model based on a much smaller dataset or
finetuning ... | 2021-01-24T03:06:34Z | 24 pages, edited the citation of the syllable-level tokenizer from
[Chormai et al., 2020] to [Phatthiyaphaibun et al., 2020] as the authors used
the syllable-level tokenizer from PyThaiNLP [Phatthiyaphaibun et al., 2020]
in the experiments | null | null | WangchanBERTa: Pretraining transformer-based Thai Language Models | ['Lalita Lowphansirikul', 'Charin Polpanumas', 'Nawat Jantrakulchai', 'Sarana Nutanong'] | 2,021 | arXiv.org | 77 | 42 | ['Computer Science'] |
2,101.10804 | CPTR: Full Transformer Network for Image Captioning | ['Wei Liu', 'Sihan Chen', 'Longteng Guo', 'Xinxin Zhu', 'Jing Liu'] | ['cs.CV'] | In this paper, we consider the image captioning task from a new
sequence-to-sequence prediction perspective and propose CaPtion TransformeR
(CPTR) which takes the sequentialized raw images as the input to Transformer.
Compared to the "CNN+Transformer" design paradigm, our model can model global
context at every encoder... | 2021-01-26T14:29:52Z | null | null | null | null | null | null | null | null | null | null |
2,101.11038 | Muppet: Massive Multi-task Representations with Pre-Finetuning | ['Armen Aghajanyan', 'Anchit Gupta', 'Akshat Shrivastava', 'Xilun Chen', 'Luke Zettlemoyer', 'Sonal Gupta'] | ['cs.CL', 'cs.LG'] | We propose pre-finetuning, an additional large-scale learning stage between
language model pre-training and fine-tuning. Pre-finetuning is massively
multi-task learning (around 50 datasets, over 4.8 million total labeled
examples), and is designed to encourage learning of representations that
generalize better to many ... | 2021-01-26T19:18:27Z | null | null | null | Muppet: Massive Multi-task Representations with Pre-Finetuning | ['Armen Aghajanyan', 'Anchit Gupta', 'Akshat Shrivastava', 'Xilun Chen', 'Luke Zettlemoyer', 'Sonal Gupta'] | 2,021 | Conference on Empirical Methods in Natural Language Processing | 270 | 75 | ['Computer Science'] |
2,101.11075 | Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged
Gradient Method for Stochastic Optimization | ['Aaron Defazio', 'Samy Jelassi'] | ['cs.LG', 'cs.AI', 'math.OC'] | We introduce MADGRAD, a novel optimization method in the family of AdaGrad
adaptive gradient methods. MADGRAD shows excellent performance on deep learning
optimization problems from multiple fields, including classification and
image-to-image tasks in vision, and recurrent and bidirectionally-masked models
in natural l... | 2021-01-26T20:38:26Z | null | null | null | Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization | ['Aaron Defazio', 'Samy Jelassi'] | 2,021 | arXiv.org | 70 | 38 | ['Computer Science', 'Mathematics'] |
2,101.11605 | Bottleneck Transformers for Visual Recognition | ['Aravind Srinivas', 'Tsung-Yi Lin', 'Niki Parmar', 'Jonathon Shlens', 'Pieter Abbeel', 'Ashish Vaswani'] | ['cs.CV', 'cs.AI', 'cs.LG'] | We present BoTNet, a conceptually simple yet powerful backbone architecture
that incorporates self-attention for multiple computer vision tasks including
image classification, object detection and instance segmentation. By just
replacing the spatial convolutions with global self-attention in the final
three bottleneck ... | 2021-01-27T18:55:27Z | Technical Report, 20 pages, 13 figures, 19 tables | null | null | null | null | null | null | null | null | null |
2,101.11718 | BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language
Generation | ['Jwala Dhamala', 'Tony Sun', 'Varun Kumar', 'Satyapriya Krishna', 'Yada Pruksachatkun', 'Kai-Wei Chang', 'Rahul Gupta'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Recent advances in deep learning techniques have enabled machines to generate
cohesive open-ended text when prompted with a sequence of words as context.
While these models now empower many downstream applications from conversation
bots to automatic storytelling, they have been shown to generate texts that
exhibit soci... | 2021-01-27T22:07:03Z | null | null | 10.1145/3442188.3445924 | BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation | ['J. Dhamala', 'Tony Sun', 'Varun Kumar', 'Satyapriya Krishna', 'Yada Pruksachatkun', 'Kai-Wei Chang', 'Rahul Gupta'] | 2,021 | Conference on Fairness, Accountability and Transparency | 403 | 47 | ['Computer Science'] |
2,102.00086 | Challenges in Automated Debiasing for Toxic Language Detection | ['Xuhui Zhou', 'Maarten Sap', 'Swabha Swayamdipta', 'Noah A. Smith', 'Yejin Choi'] | ['cs.CL'] | Biased associations have been a challenge in the development of classifiers
for detecting toxic language, hindering both fairness and accuracy. As
potential solutions, we investigate recently introduced debiasing methods for
text classification datasets and models, as applied to toxic language
detection. Our focus is o... | 2021-01-29T22:03:17Z | EACL 2021 | null | null | Challenges in Automated Debiasing for Toxic Language Detection | ['Xuhui Zhou', 'Maarten Sap', 'Swabha Swayamdipta', 'Noah A. Smith', 'Yejin Choi'] | 2,021 | Conference of the European Chapter of the Association for Computational Linguistics | 142 | 49 | ['Computer Science'] |
2,102.01192 | Generative Spoken Language Modeling from Raw Audio | ['Kushal Lakhotia', 'Evgeny Kharitonov', 'Wei-Ning Hsu', 'Yossi Adi', 'Adam Polyak', 'Benjamin Bolte', 'Tu-Anh Nguyen', 'Jade Copet', 'Alexei Baevski', 'Adelrahman Mohamed', 'Emmanuel Dupoux'] | ['cs.CL'] | We introduce Generative Spoken Language Modeling, the task of learning the
acoustic and linguistic characteristics of a language from raw audio (no text,
no labels), and a set of metrics to automatically evaluate the learned
representations at acoustic and linguistic levels for both encoding and
generation. We set up b... | 2021-02-01T21:41:40Z | null | null | null | On Generative Spoken Language Modeling from Raw Audio | ['Kushal Lakhotia', 'Evgeny Kharitonov', 'Wei-Ning Hsu', 'Yossi Adi', 'Adam Polyak', 'Benjamin Bolte', 'Tu Nguyen', 'Jade Copet', 'Alexei Baevski', 'A. Mohamed', 'Emmanuel Dupoux'] | 2,021 | Transactions of the Association for Computational Linguistics | 366 | 80 | ['Computer Science'] |
2,102.01547 | WeNet: Production oriented Streaming and Non-streaming End-to-End Speech
Recognition Toolkit | ['Zhuoyuan Yao', 'Di Wu', 'Xiong Wang', 'Binbin Zhang', 'Fan Yu', 'Chao Yang', 'Zhendong Peng', 'Xiaoyu Chen', 'Lei Xie', 'Xin Lei'] | ['cs.SD', 'cs.CL', 'eess.AS'] | In this paper, we propose an open source, production first, and production
ready speech recognition toolkit called WeNet in which a new two-pass approach
is implemented to unify streaming and non-streaming end-to-end (E2E) speech
recognition in a single model. The main motivation of WeNet is to close the gap
between th... | 2021-02-02T15:19:41Z | 5 pages, 2 figures, 4 tables | null | null | WeNet: Production Oriented Streaming and Non-Streaming End-to-End Speech Recognition Toolkit | ['Zhuoyuan Yao', 'Di Wu', 'Xiong Wang', 'Binbin Zhang', 'Fan Yu', 'Chao Yang', 'Zhendong Peng', 'Xiaoyu Chen', 'Lei Xie', 'X. Lei'] | 2,021 | Interspeech | 268 | 32 | ['Computer Science', 'Engineering'] |
2,102.01909 | HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis
and Emotion Recognition | ['Avihay Chriqui', 'Inbal Yahav'] | ['cs.CL'] | This paper introduces HeBERT and HebEMO. HeBERT is a Transformer-based model
for modern Hebrew text, which relies on a BERT (Bidirectional Encoder
Representations for Transformers) architecture. BERT has been shown to
outperform alternative architectures in sentiment analysis, and is suggested to
be particularly approp... | 2021-02-03T06:59:59Z | null | null | 10.1287/ijds.2022.0016 | HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition | ['Avihay Chriqui', 'I. Yahav'] | 2,021 | INFORMS Journal on Data Science | 37 | 82 | ['Computer Science'] |
2,102.02611 | CKConv: Continuous Kernel Convolution For Sequential Data | ['David W. Romero', 'Anna Kuzina', 'Erik J. Bekkers', 'Jakub M. Tomczak', 'Mark Hoogendoorn'] | ['cs.LG'] | Conventional neural architectures for sequential data present important
limitations. Recurrent networks suffer from exploding and vanishing gradients,
small effective memory horizons, and must be trained sequentially.
Convolutional networks are unable to handle sequences of unknown size and their
memory horizon must be... | 2021-02-04T13:51:19Z | null | null | null | null | null | null | null | null | null | null |
2,102.02766 | Designing an Encoder for StyleGAN Image Manipulation | ['Omer Tov', 'Yuval Alaluf', 'Yotam Nitzan', 'Or Patashnik', 'Daniel Cohen-Or'] | ['cs.CV'] | Recently, there has been a surge of diverse methods for performing image
editing by employing pre-trained unconditional generators. Applying these
methods on real images, however, remains a challenge, as it necessarily
requires the inversion of the images into their latent space. To successfully
invert a real image, on... | 2021-02-04T17:52:38Z | null | null | null | null | null | null | null | null | null | null |
2,102.02779 | Unifying Vision-and-Language Tasks via Text Generation | ['Jaemin Cho', 'Jie Lei', 'Hao Tan', 'Mohit Bansal'] | ['cs.CL', 'cs.AI', 'cs.CV', 'cs.LG'] | Existing methods for vision-and-language learning typically require designing
task-specific architectures and objectives for each task. For example, a
multi-label answer classifier for visual question answering, a region scorer
for referring expression comprehension, and a language decoder for image
captioning, etc. To... | 2021-02-04T17:59:30Z | ICML 2021 (15 pages, 4 figures, 14 tables) | null | null | Unifying Vision-and-Language Tasks via Text Generation | ['Jaemin Cho', 'Jie Lei', 'Hao Tan', 'Mohit Bansal'] | 2,021 | International Conference on Machine Learning | 547 | 86 | ['Computer Science'] |
2,102.03334 | ViLT: Vision-and-Language Transformer Without Convolution or Region
Supervision | ['Wonjae Kim', 'Bokyung Son', 'Ildoo Kim'] | ['stat.ML', 'cs.LG'] | Vision-and-Language Pre-training (VLP) has improved performance on various
joint vision-and-language downstream tasks. Current approaches to VLP heavily
rely on image feature extraction processes, most of which involve region
supervision (e.g., object detection) and the convolutional architecture (e.g.,
ResNet). Althou... | 2021-02-05T18:36:11Z | ICML 2021 Long Presentation | null | null | ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision | ['Wonjae Kim', 'Bokyung Son', 'Ildoo Kim'] | 2,021 | International Conference on Machine Learning | 1,775 | 65 | ['Mathematics', 'Computer Science'] |
2,102.03902 | Nyströmformer: A Nyström-Based Algorithm for Approximating
Self-Attention | ['Yunyang Xiong', 'Zhanpeng Zeng', 'Rudrasis Chakraborty', 'Mingxing Tan', 'Glenn Fung', 'Yin Li', 'Vikas Singh'] | ['cs.CL', 'cs.LG'] | Transformers have emerged as a powerful tool for a broad range of natural
language processing tasks. A key component that drives the impressive
performance of Transformers is the self-attention mechanism that encodes the
influence or dependence of other tokens on each specific token. While
beneficial, the quadratic com... | 2021-02-07T20:06:59Z | AAAI 2021; Code and supplement available at
https://github.com/mlpen/Nystromformer | null | null | Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention | ['Yunyang Xiong', 'Zhanpeng Zeng', 'Rudrasis Chakraborty', 'Mingxing Tan', 'G. Fung', 'Yin Li', 'Vikas Singh'] | 2,021 | AAAI Conference on Artificial Intelligence | 526 | 65 | ['Computer Science', 'Medicine'] |
2,102.0404 | LightSpeech: Lightweight and Fast Text to Speech with Neural
Architecture Search | ['Renqian Luo', 'Xu Tan', 'Rui Wang', 'Tao Qin', 'Jinzhu Li', 'Sheng Zhao', 'Enhong Chen', 'Tie-Yan Liu'] | ['cs.SD', 'cs.AI', 'cs.LG', 'eess.AS'] | Text to speech (TTS) has been broadly used to synthesize natural and
intelligible speech in different scenarios. Deploying TTS in various end
devices such as mobile phones or embedded devices requires extremely small
memory usage and inference latency. While non-autoregressive TTS models such as
FastSpeech have achieve... | 2021-02-08T07:45:06Z | Accepted to ICASSP 21 | null | null | null | null | null | null | null | null | null |
2,102.04411 | Traceability Transformed: Generating more Accurate Links with
Pre-Trained BERT Models | ['Jinfeng Lin', 'Yalin Liu', 'Qingkai Zeng', 'Meng Jiang', 'Jane Cleland-Huang'] | ['cs.SE'] | Software traceability establishes and leverages associations between diverse
development artifacts. Researchers have proposed the use of deep learning trace
models to link natural language artifacts, such as requirements and issue
descriptions, to source code; however, their effectiveness has been restricted
by availab... | 2021-02-08T18:18:07Z | null | null | null | Traceability Transformed: Generating More Accurate Links with Pre-Trained BERT Models | ['Jinfeng Lin', 'Yalin Liu', 'Qingkai Zeng', 'Meng Jiang', 'J. Cleland-Huang'] | 2,021 | International Conference on Software Engineering | 117 | 43 | ['Computer Science'] |
2,102.04664 | CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding
and Generation | ['Shuai Lu', 'Daya Guo', 'Shuo Ren', 'Junjie Huang', 'Alexey Svyatkovskiy', 'Ambrosio Blanco', 'Colin Clement', 'Dawn Drain', 'Daxin Jiang', 'Duyu Tang', 'Ge Li', 'Lidong Zhou', 'Linjun Shou', 'Long Zhou', 'Michele Tufano', 'Ming Gong', 'Ming Zhou', 'Nan Duan', 'Neel Sundaresan', 'Shao Kun Deng', 'Shengyu Fu', 'Shujie ... | ['cs.SE', 'cs.CL'] | Benchmark datasets have a significant impact on accelerating research in
programming language tasks. In this paper, we introduce CodeXGLUE, a benchmark
dataset to foster machine learning research for program understanding and
generation. CodeXGLUE includes a collection of 10 tasks across 14 datasets and
a platform for ... | 2021-02-09T06:16:25Z | 14 pages; Revise CodeBLEU scores for all models on text-to-code task | null | null | CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation | ['Shuai Lu', 'Daya Guo', 'Shuo Ren', 'Junjie Huang', 'Alexey Svyatkovskiy', 'Ambrosio Blanco', 'Colin B. Clement', 'Dawn Drain', 'Daxin Jiang', 'Duyu Tang', 'Ge Li', 'Lidong Zhou', 'Linjun Shou', 'Long Zhou', 'Michele Tufano', 'Ming Gong', 'Ming Zhou', 'Nan Duan', 'Neel Sundaresan', 'Shao Kun Deng', 'Shengyu Fu', 'Shuj... | 2,021 | NeurIPS Datasets and Benchmarks | 1,166 | 117 | ['Computer Science'] |
2,102.05095 | Is Space-Time Attention All You Need for Video Understanding? | ['Gedas Bertasius', 'Heng Wang', 'Lorenzo Torresani'] | ['cs.CV'] | We present a convolution-free approach to video classification built
exclusively on self-attention over space and time. Our method, named
"TimeSformer," adapts the standard Transformer architecture to video by
enabling spatiotemporal feature learning directly from a sequence of
frame-level patches. Our experimental stu... | 2021-02-09T19:49:33Z | Accepted to ICML 2021 | null | null | Is Space-Time Attention All You Need for Video Understanding? | ['Gedas Bertasius', 'Heng Wang', 'L. Torresani'] | 2,021 | International Conference on Machine Learning | 2,080 | 73 | ['Computer Science'] |
2,102.05918 | Scaling Up Visual and Vision-Language Representation Learning With Noisy
Text Supervision | ['Chao Jia', 'Yinfei Yang', 'Ye Xia', 'Yi-Ting Chen', 'Zarana Parekh', 'Hieu Pham', 'Quoc V. Le', 'Yunhsuan Sung', 'Zhen Li', 'Tom Duerig'] | ['cs.CV', 'cs.CL', 'cs.LG'] | Pre-trained representations are becoming crucial for many NLP and perception
tasks. While representation learning in NLP has transitioned to training on raw
text without human annotations, visual and vision-language representations
still rely heavily on curated training datasets that are expensive or require
expert kno... | 2021-02-11T10:08:12Z | ICML 2021 | International Conference on Machine Learning 2021 | null | null | null | null | null | null | null | null |
2,102.06171 | High-Performance Large-Scale Image Recognition Without Normalization | ['Andrew Brock', 'Soham De', 'Samuel L. Smith', 'Karen Simonyan'] | ['cs.CV', 'cs.LG', 'stat.ML'] | Batch normalization is a key component of most image classification models,
but it has many undesirable properties stemming from its dependence on the
batch size and interactions between examples. Although recent work has
succeeded in training deep ResNets without normalization layers, these models
do not match the tes... | 2021-02-11T18:23:20Z | null | null | null | null | null | null | null | null | null | null |
2,102.06203 | Proof Artifact Co-training for Theorem Proving with Language Models | ['Jesse Michael Han', 'Jason Rute', 'Yuhuai Wu', 'Edward W. Ayers', 'Stanislas Polu'] | ['cs.AI', 'cs.LG', 'cs.LO'] | Labeled data for imitation learning of theorem proving in large libraries of
formalized mathematics is scarce as such libraries require years of
concentrated effort by human specialists to be built. This is particularly
challenging when applying large Transformer language models to tactic
prediction, because the scalin... | 2021-02-11T18:59:24Z | null | null | null | Proof Artifact Co-training for Theorem Proving with Language Models | ['Jesse Michael Han', 'Jason M. Rute', 'Yuhuai Wu', 'Edward W. Ayers', 'Stanislas Polu'] | 2,021 | International Conference on Learning Representations | 127 | 94 | ['Computer Science'] |
2,102.06867 | CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation | ['Shengcong Chen', 'Changxing Ding', 'Minfeng Liu', 'Jun Cheng', 'Dacheng Tao'] | ['cs.CV'] | Nucleus segmentation is a challenging task due to the crowded distribution
and blurry boundaries of nuclei. Recent approaches represent nuclei by means of
polygons to differentiate between touching and overlapping nuclei and have
accordingly achieved promising performance. Each polygon is represented by a
set of centro... | 2021-02-13T05:59:52Z | Accepted Version to IEEE Transactions on Image Processing | null | 10.1109/TIP.2023.3237013 | null | null | null | null | null | null | null |
2,102.07033 | PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them | ['Patrick Lewis', 'Yuxiang Wu', 'Linqing Liu', 'Pasquale Minervini', 'Heinrich Küttler', 'Aleksandra Piktus', 'Pontus Stenetorp', 'Sebastian Riedel'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Open-domain Question Answering models which directly leverage question-answer
(QA) pairs, such as closed-book QA (CBQA) models and QA-pair retrievers, show
promise in terms of speed and memory compared to conventional models which
retrieve and read from text corpora. QA-pair retrievers also offer
interpretable answers,... | 2021-02-13T23:43:45Z | null | null | null | null | null | null | null | null | null | null |
2,102.08473 | COCO-LM: Correcting and Contrasting Text Sequences for Language Model
Pretraining | ['Yu Meng', 'Chenyan Xiong', 'Payal Bajaj', 'Saurabh Tiwary', 'Paul Bennett', 'Jiawei Han', 'Xia Song'] | ['cs.CL', 'cs.LG'] | We present a self-supervised learning framework, COCO-LM, that pretrains
Language Models by COrrecting and COntrasting corrupted text sequences.
Following ELECTRA-style pretraining, COCO-LM employs an auxiliary language
model to corrupt text sequences, upon which it constructs two new tasks for
pretraining the main mod... | 2021-02-16T22:24:29Z | NeurIPS 2021. (Code and Models: https://github.com/microsoft/COCO-LM) | null | null | null | null | null | null | null | null | null |
2,102.08602 | LambdaNetworks: Modeling Long-Range Interactions Without Attention | ['Irwan Bello'] | ['cs.CV', 'cs.LG'] | We present lambda layers -- an alternative framework to self-attention -- for
capturing long-range interactions between an input and structured contextual
information (e.g. a pixel surrounded by other pixels). Lambda layers capture
such interactions by transforming available contexts into linear functions,
termed lambd... | 2021-02-17T06:33:47Z | Accepted for publication at the International Conference in Learning
Representations 2021 (Spotlight) | null | null | LambdaNetworks: Modeling Long-Range Interactions Without Attention | ['Irwan Bello'] | 2,021 | International Conference on Learning Representations | 181 | 88 | ['Computer Science'] |
2,102.08981 | Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize
Long-Tail Visual Concepts | ['Soravit Changpinyo', 'Piyush Sharma', 'Nan Ding', 'Radu Soricut'] | ['cs.CV', 'cs.CL'] | The availability of large-scale image captioning and visual question
answering datasets has contributed significantly to recent successes in
vision-and-language pre-training. However, these datasets are often collected
with overrestrictive requirements inherited from their original target tasks
(e.g., image caption gen... | 2021-02-17T19:15:53Z | IEEE Conference on Computer Vision and Pattern Recognition (CVPR
2021). Our dataset is available at
https://github.com/google-research-datasets/conceptual-12m | null | null | Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts | ['Soravit Changpinyo', 'P. Sharma', 'Nan Ding', 'Radu Soricut'] | 2,021 | Computer Vision and Pattern Recognition | 1,143 | 100 | ['Computer Science'] |
2,102.09206 | Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using
a Weak Decoder | ['Shuqi Lu', 'Di He', 'Chenyan Xiong', 'Guolin Ke', 'Waleed Malik', 'Zhicheng Dou', 'Paul Bennett', 'Tieyan Liu', 'Arnold Overwijk'] | ['cs.LG'] | Dense retrieval requires high-quality text sequence embeddings to support
effective search in the representation space. Autoencoder-based language models
are appealing in dense retrieval as they train the encoder to output
high-quality embedding that can reconstruct the input texts. However, in this
paper, we provide t... | 2021-02-18T08:08:17Z | null | null | null | null | null | null | null | null | null | null |
2,102.09542 | SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical
Visual Question Answering | ['Bo Liu', 'Li-Ming Zhan', 'Li Xu', 'Lin Ma', 'Yan Yang', 'Xiao-Ming Wu'] | ['cs.CV', 'cs.AI', 'cs.CL'] | Medical visual question answering (Med-VQA) has tremendous potential in
healthcare. However, the development of this technology is hindered by the
lacking of publicly-available and high-quality labeled datasets for training
and evaluation. In this paper, we present a large bilingual dataset, SLAKE,
with comprehensive s... | 2021-02-18T18:44:50Z | ISBI 2021 | null | null | Slake: A Semantically-Labeled Knowledge-Enhanced Dataset For Medical Visual Question Answering | ['Bo Liu', 'Li-Ming Zhan', 'Li Xu', 'Lin Ma', 'Y. Yang', 'Xiao-Ming Wu'] | 2,021 | IEEE International Symposium on Biomedical Imaging | 274 | 15 | ['Computer Science'] |
2,102.09665 | MUDES: Multilingual Detection of Offensive Spans | ['Tharindu Ranasinghe', 'Marcos Zampieri'] | ['cs.CL', 'cs.AI', 'cs.LG'] | The interest in offensive content identification in social media has grown
substantially in recent years. Previous work has dealt mostly with post level
annotations. However, identifying offensive spans is useful in many ways. To
help coping with this important challenge, we present MUDES, a multilingual
system to dete... | 2021-02-18T23:19:00Z | Accepted to NAACL-HLT 2021 | null | null | MUDES: Multilingual Detection of Offensive Spans | ['Tharindu Ranasinghe', 'Marcos Zampieri'] | 2,021 | North American Chapter of the Association for Computational Linguistics | 41 | 51 | ['Computer Science'] |
2,102.09672 | Improved Denoising Diffusion Probabilistic Models | ['Alex Nichol', 'Prafulla Dhariwal'] | ['cs.LG', 'cs.AI', 'stat.ML'] | Denoising diffusion probabilistic models (DDPM) are a class of generative
models which have recently been shown to produce excellent samples. We show
that with a few simple modifications, DDPMs can also achieve competitive
log-likelihoods while maintaining high sample quality. Additionally, we find
that learning varian... | 2021-02-18T23:44:17Z | null | null | null | null | null | null | null | null | null | null |
2,102.10684 | Pre-Training BERT on Arabic Tweets: Practical Considerations | ['Ahmed Abdelali', 'Sabit Hassan', 'Hamdy Mubarak', 'Kareem Darwish', 'Younes Samih'] | ['cs.CL', 'cs.AI'] | Pretraining Bidirectional Encoder Representations from Transformers (BERT)
for downstream NLP tasks is a non-trival task. We pretrained 5 BERT models that
differ in the size of their training sets, mixture of formal and informal
Arabic, and linguistic preprocessing. All are intended to support Arabic
dialects and socia... | 2021-02-21T20:51:33Z | 6 pages, 5 figures | null | null | Pre-Training BERT on Arabic Tweets: Practical Considerations | ['Ahmed Abdelali', 'Sabit Hassan', 'Hamdy Mubarak', 'Kareem Darwish', 'Younes Samih'] | 2,021 | arXiv.org | 102 | 30 | ['Computer Science'] |
2,102.11646 | HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search | ['Niv Nayman', 'Yonathan Aflalo', 'Asaf Noy', 'Lihi Zelnik-Manor'] | ['cs.LG', 'cs.AI', 'cs.CV', 'math.OC', 'stat.ML', '68T09, 68T45', 'G.1.6; G.3; I.2.8; I.2.10; I.5.1'] | Realistic use of neural networks often requires adhering to multiple
constraints on latency, energy and memory among others. A popular approach to
find fitting networks is through constrained Neural Architecture Search (NAS),
however, previous methods enforce the constraint only softly. Therefore, the
resulting network... | 2021-02-23T11:56:30Z | Niv Nayman and Yonathan Aflalo contributed equally. An implementation
of HardCoRe-NAS is available at: https://github.com/Alibaba-MIIL/HardCoReNAS | null | null | null | null | null | null | null | null | null |
2,102.11972 | Do Transformer Modifications Transfer Across Implementations and
Applications? | ['Sharan Narang', 'Hyung Won Chung', 'Yi Tay', 'William Fedus', 'Thibault Fevry', 'Michael Matena', 'Karishma Malkan', 'Noah Fiedel', 'Noam Shazeer', 'Zhenzhong Lan', 'Yanqi Zhou', 'Wei Li', 'Nan Ding', 'Jake Marcus', 'Adam Roberts', 'Colin Raffel'] | ['cs.LG', 'cs.CL'] | The research community has proposed copious modifications to the Transformer
architecture since it was introduced over three years ago, relatively few of
which have seen widespread adoption. In this paper, we comprehensively evaluate
many of these modifications in a shared experimental setting that covers most
of the c... | 2021-02-23T22:44:54Z | To appear at EMNLP 2021 as a conference paper | null | null | null | null | null | null | null | null | null |
2,102.12092 | Zero-Shot Text-to-Image Generation | ['Aditya Ramesh', 'Mikhail Pavlov', 'Gabriel Goh', 'Scott Gray', 'Chelsea Voss', 'Alec Radford', 'Mark Chen', 'Ilya Sutskever'] | ['cs.CV', 'cs.LG'] | Text-to-image generation has traditionally focused on finding better modeling
assumptions for training on a fixed dataset. These assumptions might involve
complex architectures, auxiliary losses, or side information such as object
part labels or segmentation masks supplied during training. We describe a
simple approach... | 2021-02-24T06:42:31Z | null | null | null | null | null | null | null | null | null | null |
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