DE eng

Search in the Catalogues and Directories

Hits 1 – 13 of 13

1
Estimating the Entropy of Linguistic Distributions ...
BASE
Show details
2
On Homophony and Rényi Entropy ...
BASE
Show details
3
On Homophony and Rényi Entropy ...
BASE
Show details
4
Revisiting the Uniform Information Density Hypothesis ...
BASE
Show details
5
Revisiting the Uniform Information Density Hypothesis ...
BASE
Show details
6
Conditional Poisson Stochastic Beams ...
BASE
Show details
7
Language Model Evaluation Beyond Perplexity ...
BASE
Show details
8
A surprisal--duration trade-off across and within the world's languages ...
BASE
Show details
9
Determinantal Beam Search ...
BASE
Show details
10
Is Sparse Attention more Interpretable? ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.17 Abstract: Sparse attention has been claimed to increase model interpretability under the assumption that it highlights influential inputs. Yet the attention distribution is typically over representations internal to the model rather than the inputs themselves, suggesting this assumption may not have merit. We build on the recent work exploring the interpretability of attention; we design a set of experiments to help us understand how sparsity affects our ability to use attention as an explainability tool. On three text classification tasks, we verify that only a weak relationship between inputs and co-indexed intermediate representations exists—under sparse attention and otherwise. Further, we do not find any plausible mappings from sparse attention distributions to a sparse set of influential inputs through other avenues. Rather, we observe in this setting that inducing sparsity may make it less plausible that attention can be used as a tool for ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25435-is-sparse-attention-more-interpretablequestion
https://dx.doi.org/10.48448/90jh-y922
BASE
Hide details
11
A Cognitive Regularizer for Language Modeling ...
BASE
Show details
12
A Cognitive Regularizer for Language Modeling ...
BASE
Show details
13
If beam search is the answer, what was the question?
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
BASE
Show details

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
13
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern