1 |
Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
ANLIzing the Adversarial Natural Language Inference Dataset
|
|
|
|
In: Proceedings of the Society for Computation in Linguistics (2022)
|
|
BASE
|
|
Show details
|
|
3 |
Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization
|
|
|
|
In: Association for Computational Linguistics (2021)
|
|
BASE
|
|
Show details
|
|
4 |
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Compositional Neural Machine Translation by Removing the Lexicon from Syntax ...
|
|
|
|
Abstract:
The meaning of a natural language utterance is largely determined from its syntax and words. Additionally, there is evidence that humans process an utterance by separating knowledge about the lexicon from syntax knowledge. Theories from semantics and neuroscience claim that complete word meanings are not encoded in the representation of syntax. In this paper, we propose neural units that can enforce this constraint over an LSTM encoder and decoder. We demonstrate that our model achieves competitive performance across a variety of domains including semantic parsing, syntactic parsing, and English to Mandarin Chinese translation. In these cases, our model outperforms the standard LSTM encoder and decoder architecture on many or all of our metrics. To demonstrate that our model achieves the desired separation between the lexicon and syntax, we analyze its weights and explore its behavior when different neural modules are damaged. When damaged, we find that the model displays the knowledge distortions that ... : natural language processing; adversarial neural networks; machine translation; aphasia; neural attention ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://arxiv.org/abs/2002.08899 https://dx.doi.org/10.48550/arxiv.2002.08899
|
|
BASE
|
|
Hide details
|
|
7 |
SAL : a Self-Aware Learning system ; Self-Aware Learning system
|
|
|
|
BASE
|
|
Show details
|
|
|
|