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The 2021 Conference on Empirical Methods in Natural Language Processing 2021 (110)
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Hits 1 – 20 of 110
1
On Homophony and Rényi Entropy ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Cotterell, Ryan
;
Meister, Clara
. - : Underline Science Inc., 2021
BASE
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2
Backtranslation in Neural Morphological Inflection ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Hulden, Mans
;
Liu, Ling
. - : Underline Science Inc., 2021
BASE
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3
Rule-based Morphological Inflection Improves Neural Terminology Translation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Carpuat, Marine
;
Xu, Weijia
. - : Underline Science Inc., 2021
BASE
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4
Translating Headers of Tabular Data: A Pilot Study of Schema Translation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Gao, Yan
;
Guo, Jiaqi
. - : Underline Science Inc., 2021
BASE
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5
An Information-Theoretic Characterization of Morphological Fusion ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Futrell, Richard
;
Hahn, Michael
. - : Underline Science Inc., 2021
BASE
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6
Analyzing the Surprising Variability in Word Embedding Stability Across Languages ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Burdick, Laura
;
Kummerfeld, Jonathan
. - : Underline Science Inc., 2021
BASE
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7
Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Peng, Wei
. - : Underline Science Inc., 2021
BASE
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8
STaCK: Sentence Ordering with Temporal Commonsense Knowledge ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Ghosal, Deepanway
;
Majumder, Navonil
. - : Underline Science Inc., 2021
BASE
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9
Wikily Supervised Neural Translation Tailored to Cross-Lingual Tasks ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Callison-Burch, Chris
;
Rasooli, Mohammad Sadegh
. - : Underline Science Inc., 2021
BASE
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10
Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Gheini, Mozhdeh
;
May, Jonathan
. - : Underline Science Inc., 2021
BASE
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11
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Esplà-Gomis, Miquel
;
Pérez-Ortiz, Juan Antonio
. - : Underline Science Inc., 2021
BASE
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12
Sequence Length is a Domain: Length-based Overfitting in Transformer Models ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bojar, Ondřej
;
Variš, Dušan
. - : Underline Science Inc., 2021
BASE
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13
Speechformer: Reducing Information Loss in Direct Speech Translation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Gaido, Marco
;
Negri, Matteo
. - : Underline Science Inc., 2021
BASE
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14
Data and Parameter Scaling Laws for Neural Machine Translation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Duh, Kevin
;
Gordon, Mitchell
. - : Underline Science Inc., 2021
BASE
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15
A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
De Raedt, Maarten
. - : Underline Science Inc., 2021
BASE
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16
Universal Simultaneous Machine Translation with Mixture-of-Experts Wait-k Policy ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Feng, Yang
;
Zhang, Shaolei
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.581/ Abstract: Simultaneous machine translation (SiMT) generates translation before reading the entire source sentence and hence it has to trade off between translation quality and latency. To fulfill the requirements of different translation quality and latency in practical applications, the previous methods usually need to train multiple SiMT models for different latency levels, resulting in large computational costs. In this paper, we propose a universal SiMT model with Mixture-of-Experts Wait-k Policy to achieve the best translation quality under arbitrary latency with only one trained model. Specifically, our method employs multi-head attention to accomplish the mixture of experts where each head is treated as a wait-k expert with its own waiting words number, and given a test latency and source inputs, the weights of the experts are accordingly adjusted to produce the best translation. Experiments on three datasets show that our method ...
Keyword:
Computational Linguistics
;
Machine Learning
;
Machine Learning and Data Mining
;
Machine translation
;
Natural Language Processing
URL:
https://underline.io/lecture/37449-universal-simultaneous-machine-translation-with-mixture-of-experts-wait-k-policy
https://dx.doi.org/10.48448/sg3t-st28
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17
Learning to Rewrite for Non-Autoregressive Neural Machine Translation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Feng, Xiaocheng
;
Geng, Xinwei
. - : Underline Science Inc., 2021
BASE
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18
Towards Making the Most of Dialogue Characteristics for Neural Chat Translation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Fandong
;
Chen, Yufeng
. - : Underline Science Inc., 2021
BASE
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19
Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Byrne, Bill
;
Domhan, Tobias
. - : Underline Science Inc., 2021
BASE
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20
Sometimes We Want Ungrammatical Translations ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Parthasarathi, Prasanna
. - : Underline Science Inc., 2021
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