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Priorless Recurrent Networks Learn Curiously ...
Abstract: Recently, domain-general recurrent neural networks, without explicit linguistic inductive biases, have been shown to successfully reproduce a range of human language behaviours, such as accurately predicting number agreement between nouns and verbs. We show that such networks will also learn number agreement within unnatural sentence structures, i.e. structures that are not found within any natural languages and which humans struggle to process. These results suggest that the models are learning from their input in a manner that is substantially different from human language acquisition, and we undertake an analysis of how the learned knowledge is stored in the weights of the network. We find that while the model has an effective understanding of singular versus plural for individual sentences, there is a lack of a unified concept of number agreement connecting these processes across the full range of inputs. Moreover, the weights handling natural and unnatural structures overlap substantially, in a way that ...
Keyword: Computer and Information Science; Natural Language Processing; Neural Network
URL: https://underline.io/lecture/6295-priorless-recurrent-networks-learn-curiously
https://dx.doi.org/10.48448/x5ax-0680
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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 ...
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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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