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The 28th International Conference on Computational Linguistics 2020 (566)
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Hits 1 – 20 of 566
1
Priorless Recurrent Networks Learn Curiously ...
The 28th International Conference on Computational Linguistics 2020
;
Bowers, Jeff
;
Mitchell, Jeff
. - : Underline Science Inc., 2020
BASE
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2
Character Alignment in Morphologically Complex Translation Sets for Related Languages ...
The 28th International Conference on Computational Linguistics 2020
;
Ephrem, Binyam
;
Gasser, Michael
. - : Underline Science Inc., 2020
BASE
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3
Composing Byte-Pair Encodings for Morphological Sequence Classification ...
The 28th International Conference on Computational Linguistics 2020
;
Bernardy, Jean-Philipe
;
Ek, Adam
. - : Underline Science Inc., 2020
BASE
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4
Variation in Universal Dependencies annotation: A token based typological case study on adpossessive constructions ...
The 28th International Conference on Computational Linguistics 2020
;
Haakama, Viljami
;
Sinnemäki, Kaius
. - : Underline Science Inc., 2020
BASE
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5
Corpus evidence for word order freezing in Russian and German ...
The 28th International Conference on Computational Linguistics 2020
;
Berdicevskis, Aleksandrs
;
Piperski, Alexander
. - : Underline Science Inc., 2020
BASE
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6
An analysis of language models for metaphor recognition ...
The 28th International Conference on Computational Linguistics 2020
;
Markert, Katja
;
Neidlein, Arthur
. - : Underline Science Inc., 2020
BASE
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7
Noise Isn't Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models ...
The 28th International Conference on Computational Linguistics 2020
;
Nicolai, Garrett
;
Silfverberg, Miikka
. - : Underline Science Inc., 2020
Abstract:
Morphological inflection, like many sequence-to-sequence tasks, sees great performance from recurrent neural architectures when data is plentiful, but performance falls off sharply in lower-data settings. We investigate one aspect of neural seq2seq models that we hypothesize contributes to overfitting - teacher forcing. By creating different training and test conditions, exposure bias increases the likelihood that a system too closely models its training data. Experiments show that teacher-forced models struggle to recover when they enter unknown territory. However, a simple modification to the training algorithm to more closely mimic test conditions creates models that are better able to generalize to unseen environments. ...
Keyword:
Computer and Information Science
;
Natural Language Processing
;
Neural Network
URL:
https://dx.doi.org/10.48448/3zab-w963
https://underline.io/lecture/6227-noise-isn't-always-negative-countering-exposure-bias-in-sequence-to-sequence-inflection-models
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8
Exhaustive Entity Recognition for Coptic - Challenges and Solutions ...
The 28th International Conference on Computational Linguistics 2020
;
Martin, Lance
;
Tu, Sichang
. - : Underline Science Inc., 2020
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9
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games ...
The 28th International Conference on Computational Linguistics 2020
;
Bastianelli, Emanuele
;
Bisk, Yonatan
. - : Underline Science Inc., 2020
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10
Attentively Embracing Noise for Robust Latent Representation in BERT ...
The 28th International Conference on Computational Linguistics 2020
;
Cunha Sergio, Gwenaelle
;
Lee, Minho
. - : Underline Science Inc., 2020
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11
Catching Attention with Automatic Pull Quote Selection ...
The 28th International Conference on Computational Linguistics 2020
;
Bohn, Tanner
. - : Underline Science Inc., 2020
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12
Classifier Probes May Just Learn from Linear Context Features ...
The 28th International Conference on Computational Linguistics 2020
;
Kuhlmann, Marco
;
Kunz, Jenny
. - : Underline Science Inc., 2020
BASE
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13
Seeing the world through text: Evaluating image descriptions for commonsense reasoning in machine reading comprehension ...
The 28th International Conference on Computational Linguistics 2020
;
Galvan-Sosa, Diana
;
Inui, Kentaro
. - : Underline Science Inc., 2020
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14
Part 6 - Cross-linguistic Studies ...
The 28th International Conference on Computational Linguistics 2020
;
., Omri
. - : Underline Science Inc., 2020
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15
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information ...
The 28th International Conference on Computational Linguistics 2020
;
Chen, Jinjun
;
Li , Lin
. - : Underline Science Inc., 2020
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16
HMSid and HMSid2 at PARSEME Shared Task 2020: Computational Corpus Linguistics and unseen-in-training MWEs ...
The 28th International Conference on Computational Linguistics 2020
;
Colson, Jean-Pierre
. - : Underline Science Inc., 2020
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17
Multi-dialect Arabic BERT for Country-level Dialect Identification ...
The 28th International Conference on Computational Linguistics 2020
;
Ali, Mohammad
;
Al-Natsheh, Hussein
. - : Underline Science Inc., 2020
BASE
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18
Autoencoding Improves Pre-trained Word Embeddings ...
The 28th International Conference on Computational Linguistics 2020
;
Bollegala, Danushka
;
Kaneko, Masahiro
. - : Underline Science Inc., 2020
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19
Exploring End-to-End Differentiable Natural Logic Modeling ...
The 28th International Conference on Computational Linguistics 2020
;
Feng, Yufei
;
Greenspan, Michael
. - : Underline Science Inc., 2020
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20
AutoMeTS: The Autocomplete for Medical Text Simplification. ...
The 28th International Conference on Computational Linguistics 2020
. - : Underline Science Inc., 2020
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