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The 2021 Conference on Empirical Methods in Natural Language Processing 2021 (252)
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Hits 1 – 20 of 254
1
Le modèle Transformer: un « couteau suisse » pour le traitement automatique des langues
Yvon, François
In: Techniques de l'Ingenieur ; https://hal.archives-ouvertes.fr/hal-03619077 ; Techniques de l'Ingenieur, Techniques de l'ingénieur, 2022, ⟨10.51257/a-v1-in195⟩ ; https://www.techniques-ingenieur.fr/base-documentaire/innovation-th10/innovations-en-electronique-et-tic-42257210/transformer-des-reseaux-de-neurones-pour-le-traitement-automatique-des-langues-in195/ (2022)
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2
Automatic Error Type Annotation for Arabic ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Belkebir, Riadh
;
Habash, Nizar
. - : Underline Science Inc., 2021
BASE
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3
Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Chen, Yuanzhi
;
Hasan, Mohammad
. - : Underline Science Inc., 2021
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4
HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Chen, Sanxing
;
Gao, Jianfeng
. - : Underline Science Inc., 2021
BASE
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5
Detecting Gender Bias using Explainability ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Krithika
;
Gupta, Gauri
. - : Underline Science Inc., 2021
BASE
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6
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
He, Lifang
;
LIU, YE
. - : Underline Science Inc., 2021
BASE
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7
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Ong, Desmond
;
Suresh, Varsha
. - : Underline Science Inc., 2021
BASE
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8
Contrastive Code Representation Learning ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Jain, Ajay
;
Jain, Paras
. - : Underline Science Inc., 2021
BASE
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9
Unsupervised Multi-View Post-OCR Error Correction With Language Models ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Brenner, Eliot
;
Broscheit, Samuel
. - : Underline Science Inc., 2021
BASE
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10
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Ding, Haoran
;
Luo, Xiao
. - : Underline Science Inc., 2021
BASE
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11
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Park, Joonsuk
;
Sathe, Aalok
. - : Underline Science Inc., 2021
BASE
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12
Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Kumbale, Sinchana
;
Singh, Smriti
. - : Underline Science Inc., 2021
BASE
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13
Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Wang, Gengyu
. - : Underline Science Inc., 2021
BASE
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14
Pre-train or Annotate? Domain Adaptation with a Constrained Budget ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bai, Fan
;
Ritter, Alan
;
Xu, Wei
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.409/ Abstract: Recent work has demonstrated that pre-training in-domain language models can boost performance when adapting to a new domain. However, the costs associated with pre-training raise an important question: given a fixed budget, what steps should an NLP practitioner take to maximize performance? In this paper, we study domain adaptation under budget constraints, and approach it as a customer choice problem between data annotation and pre-training. Specifically, we measure the annotation cost of three procedural text datasets and the pre-training cost of three in-domain language models. Then we evaluate the utility of different combinations of pre-training and data annotation under varying budget constraints to assess which combination strategy works best. We find that, for small budgets, spending all funds on annotation leads to the best performance; once the budget becomes large enough, a combination of data annotation and in-domain ...
Keyword:
Computational Linguistics
;
Language Models
;
Machine Learning
;
Machine Learning and Data Mining
;
Natural Language Processing
URL:
https://underline.io/lecture/37963-pre-train-or-annotatequestion-domain-adaptation-with-a-constrained-budget
https://dx.doi.org/10.48448/z1gf-n855
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15
Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Choenni, Rochelle
;
Shutova, Ekaterina
. - : Underline Science Inc., 2021
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16
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Cao, Shuyang
;
Wang, Lu
. - : Underline Science Inc., 2021
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17
Automatic Text Evaluation through the Lens of Wasserstein Barycenters ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Clavel, Chloe
;
Colombo, Pierre
. - : Underline Science Inc., 2021
BASE
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18
Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Funkquist, Martin
. - : Underline Science Inc., 2021
BASE
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19
Meta Distant Transfer Learning for Pre-trained Language Models ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Wang, Chengyu
. - : Underline Science Inc., 2021
BASE
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20
How to Train BERT with an Academic Budget ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Berchansky, Moshe
;
Izsak, Peter
. - : Underline Science Inc., 2021
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