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1
Priorless Recurrent Networks Learn Curiously ...
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Composing Byte-Pair Encodings for Morphological Sequence Classification ...
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3
Variation in Universal Dependencies annotation: A token based typological case study on adpossessive constructions ...
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4
Corpus evidence for word order freezing in Russian and German ...
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5
An analysis of language models for metaphor recognition ...
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6
Noise Isn't Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models ...
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7
Exhaustive Entity Recognition for Coptic - Challenges and Solutions ...
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8
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games ...
Abstract: In visual guessing games, a Guesser has to identify a target object in a scene by asking questions to an Oracle. An effective strategy for the players is to learn conceptual representations of objects that are both discriminative and expressive enough to ask questions and guess correctly. However, as shown by Suglia et al. (2020), existing models fail to learn truly multi-modal representations, relying instead on gold category labels for objects in the scene both at training and inference time. This provides an unnatural performance advantage when categories at inference time match those at training time, and it causes models to fail in more realistic "zero-shot" scenarios where out-of-domain object categories are involved. To overcome this issue, we introduce a novel "imagination" module based on Regularized Auto-Encoders, that learns context-aware and category-aware latent embeddings without relying on category labels at inference time. Our imagination module outperforms state-of-the-art competitors by ...
Keyword: Computer and Information Science; Information and Knowledge Engineering; Intelligent System; Natural Language Processing; Neural Network
URL: https://dx.doi.org/10.48448/z403-y144
https://underline.io/lecture/6661-imagining-grounded-conceptual-representations-from-perceptual-information-in-situated-guessing-games
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9
Attentively Embracing Noise for Robust Latent Representation in BERT ...
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10
Catching Attention with Automatic Pull Quote Selection ...
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11
Classifier Probes May Just Learn from Linear Context Features ...
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12
Seeing the world through text: Evaluating image descriptions for commonsense reasoning in machine reading comprehension ...
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13
Part 6 - Cross-linguistic Studies ...
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14
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information ...
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15
HMSid and HMSid2 at PARSEME Shared Task 2020: Computational Corpus Linguistics and unseen-in-training MWEs ...
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16
Autoencoding Improves Pre-trained Word Embeddings ...
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Exploring End-to-End Differentiable Natural Logic Modeling ...
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18
AutoMeTS: The Autocomplete for Medical Text Simplification. ...
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19
A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English ...
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20
SemEval Task 6: DeftEval ...
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