Home
Catalogue search
Refine your search:
Keyword
Creator / Publisher:
Linzen, Tal (8)
McCoy, R. Thomas (8)
Frank, Robert (3)
Smolensky, Paul (3)
Lepori, Michael A. (2)
Mccoy, R. Thomas (2)
Pavlick, Ellie (2)
Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS) (1)
Apprentissage machine et développement cognitif (CoML) (1)
Bowman, Samuel R. (1)
more
Year
Medium:
Online (11)
Type:
Miscellaneous (8)
Article (3)
BLLDB-Access
Search in the Catalogues and Directories
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
Sort by
creator [A → Z]
'
creator [Z → A]
'
publishing year ↑ (asc)
'
publishing year ↓ (desc)
'
title [A → Z]
'
title [Z → A]
'
Simple Search
Hits 1 – 11 of 11
1
How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN ...
McCoy, R. Thomas
;
Smolensky, Paul
;
Linzen, Tal
. - : arXiv, 2021
BASE
Show details
2
Picking BERT's Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis ...
Lepori, Michael A.
;
McCoy, R. Thomas
. - : arXiv, 2020
BASE
Show details
3
Universal linguistic inductive biases via meta-learning ...
McCoy, R. Thomas
;
Grant, Erin
;
Smolensky, Paul
. - : arXiv, 2020
BASE
Show details
4
Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMs ...
Lepori, Michael A.
;
Linzen, Tal
;
McCoy, R. Thomas
. - : arXiv, 2020
BASE
Show details
5
Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks
McCoy, R. Thomas
;
Frank, Robert
;
Linzen, Tal
In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 125-140 (2020) (2020)
BASE
Show details
6
RNNs Implicitly Implement Tensor Product Representations
Mccoy, R. Thomas
;
Linzen, Tal
;
Dunbar, Ewan
...
In: International Conference on Learning Representations ; ICLR 2019 - International Conference on Learning Representations ; https://hal.archives-ouvertes.fr/hal-02274498 ; ICLR 2019 - International Conference on Learning Representations, May 2019, New Orleans, United States (2019)
BASE
Show details
7
What do you learn from context? Probing for sentence structure in contextualized word representations ...
Tenney, Ian
;
Xia, Patrick
;
Chen, Berlin
. - : arXiv, 2019
BASE
Show details
8
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference ...
McCoy, R. Thomas
;
Pavlick, Ellie
;
Linzen, Tal
. - : arXiv, 2019
BASE
Show details
9
BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance ...
McCoy, R. Thomas
;
Min, Junghyun
;
Linzen, Tal
. - : arXiv, 2019
BASE
Show details
10
Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks ...
McCoy, R. Thomas
;
Frank, Robert
;
Linzen, Tal
. - : arXiv, 2018
BASE
Show details
11
TAG Parsing with Neural Networks and Vector Representations of Supertags
Kasai, Jungo
;
Frank, Robert
;
Mccoy, R. Thomas
;
Rambow, Owen
;
Nasr, Alexis
In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, ; Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-01771494 ; Conference on Empirical Methods in Natural Language Processing, Sep 2017, Copenhague, Denmark. pp.1712 - 1722 (2017)
Abstract:
International audience ; We present supertagging-based models for Tree Adjoining Grammar parsing that use neural network architectures and dense vector representation of supertags (elementary trees) to achieve state-of-the-art performance in unlabeled and labeled attachment scores. The shift-reduce parsing model eschews lexical information entirely , and uses only the 1-best supertags to parse a sentence, providing further support for the claim that supertagging is " almost parsing. " We demonstrate that the embedding vector representations the parser induces for supertags possess linguistically interpretable structure, supporting analogies between grammatical structures like those familiar from recent work in distri-butional semantics. This dense representation of supertags overcomes the drawbacks for statistical models of TAG as compared to CCG parsing, raising the possibility that TAG is a viable alternative for NLP tasks that require the assignment of richer structural descriptions to sentences.
Keyword:
[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
URL:
https://hal.archives-ouvertes.fr/hal-01771494/file/D17-1180.pdf
https://hal.archives-ouvertes.fr/hal-01771494/document
https://hal.archives-ouvertes.fr/hal-01771494
BASE
Hide details
Mobile view
All
Catalogues
UB Frankfurt Linguistik
0
IDS Mannheim
0
OLC Linguistik
0
UB Frankfurt Retrokatalog
0
DNB Subject Category Language
0
Institut für Empirische Sprachwissenschaft
0
Leibniz-Centre General Linguistics (ZAS)
0
Bibliographies
BLLDB
0
BDSL
0
IDS Bibliografie zur deutschen Grammatik
0
IDS Bibliografie zur Gesprächsforschung
0
IDS Konnektoren im Deutschen
0
IDS Präpositionen im Deutschen
0
IDS OBELEX meta
0
MPI-SHH Linguistics Collection
0
MPI for Psycholinguistics
0
Linked Open Data catalogues
Annohub
0
Online resources
Link directory
0
Journal directory
0
Database directory
0
Dictionary directory
0
Open access documents
BASE
11
Linguistik-Repository
0
IDS Publikationsserver
0
Online dissertations
0
Language Description Heritage
0
© 2013 - 2024 Lin|gu|is|tik
|
Imprint
|
Privacy Policy
|
Datenschutzeinstellungen ändern