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1
Priorless Recurrent Networks Learn Curiously ...
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2
Character Alignment in Morphologically Complex Translation Sets for Related Languages ...
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3
Composing Byte-Pair Encodings for Morphological Sequence Classification ...
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4
Variation in Universal Dependencies annotation: A token based typological case study on adpossessive constructions ...
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5
Corpus evidence for word order freezing in Russian and German ...
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6
An analysis of language models for metaphor recognition ...
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7
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 ...
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9
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games ...
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10
Attentively Embracing Noise for Robust Latent Representation in BERT ...
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11
Catching Attention with Automatic Pull Quote Selection ...
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12
Opening Ceremony ...
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13
Classifier Probes May Just Learn from Linear Context Features ...
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14
Seeing the world through text: Evaluating image descriptions for commonsense reasoning in machine reading comprehension ...
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15
Part 6 - Cross-linguistic Studies ...
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16
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information ...
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17
HMSid and HMSid2 at PARSEME Shared Task 2020: Computational Corpus Linguistics and unseen-in-training MWEs ...
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18
Multi-dialect Arabic BERT for Country-level Dialect Identification ...
Abstract: Arabic dialect identification is a complex problem for a number of inherent properties of the language itself. In this paper, we present the experiments conducted, and the models developed by our competing team, Mawdoo3 AI, along the way to achieving our winning solution to subtask 1 of the Nuanced Arabic Dialect Identification (NADI) shared task. The dialect identification subtask provides 21,000 country-level labeled tweets covering all 21 Arab countries. An unlabeled corpus of 10M tweets from the same domain is also presented by the competition organizers for optional use. Our winning solution itself came in the form of an ensemble of different training iterations of our pre-trained BERT model, which achieved a micro-averaged F1-score of 26.78% on the subtask at hand. We publicly release the pre-trained language model component of our winning solution under the name of Multi-dialect-Arabic-BERT model, for any interested researcher out there. ...
Keyword: Natural Language Processing
URL: https://underline.io/lecture/6530-multi-dialect-arabic-bert-for-country-level-dialect-identification
https://dx.doi.org/10.48448/xm5v-rh49
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19
Autoencoding Improves Pre-trained Word Embeddings ...
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20
Exploring End-to-End Differentiable Natural Logic Modeling ...
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