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1Graphadapter: Tuning vision-language models with dual knowledge graph
X. Li, D. Lian, Z. Lu, J. Bai, Z. Chen, and X. Wang. Advances in Neural Information Processing Systems, (2024)
2 years ago by @jannaom
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adapter
language
model
knowledge
graph
vision
adapterlanguagemodelknowledgegraphvision
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8Lora: Low-rank adaptation of large language models
E. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen. arXiv preprint arXiv:2106.09685, (2021)
2 years ago by @jannaom
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adapter
large
language
adaptation
model
adapterlargelanguageadaptationmodel
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3Large Language Models and Knowledge Graphs: Opportunities and Challenges
J. Pan, S. Razniewski, J. Kalo, S. Singhania, J. Chen, S. Dietze, H. Jabeen, J. Omeliyanenko, W. Zhang, M. Lissandrini and 6 other author(s). Transactions on Graph Data and Knowledge, 1 (1): 2:1--2:38 (2023)
2 years ago by @jannaom
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author:omeliyanenko
myown
app_nlp
large
language
research_knowledge
research_llm
llm
graph
model
kg
knowledge
selected
author:omeliyanenkomyownapp_nlplargelanguageresearch_knowledgeresearch_llmllmgraphmodelkgknowledgeselected
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2Optimizing Medical Service Request Processes through Language Modeling and Semantic Search
D. Schlör, J. Pfister, and A. Hotho. 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), page 136–141. New York, NY, USA, Association for Computing Machinery, (2023)
2 years ago by @janpf
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myown
semantic
modeling
medical
optimization
language
author:schloer
search
author:pfister
myownsemanticmodelingmedicaloptimizationlanguageauthor:schloersearchauthor:pfister
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3Compacter: Efficient Low-Rank Hypercomplex Adapter Layers
R. Karimi Mahabadi, J. Henderson, and S. Ruder. Advances in Neural Information Processing Systems, 34, page 1022--1035. Curran Associates, Inc., (2021)
2 years ago by @jannaom
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lm
nlp
adapter
language
compacter
model
llm
lmnlpadapterlanguagecompactermodelllm
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2Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning
Z. Ke, B. Liu, N. Ma, H. Xu, and L. Shu. Advances in Neural Information Processing Systems, (2021)
3 years ago by @jannaom
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LM
models
routing
capsule
forgetting
language
LMmodelsroutingcapsuleforgettinglanguage
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1It’s not Rocket Science: Interpreting Figurative Language in Narratives
T. Chakrabarty, Y. Choi, and V. Shwartz. Transactions of the Association for Computational Linguistics, (2022)
4 years ago by @jannaom
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language
idioms
model
expressions
knowledge
multiword
languageidiomsmodelexpressionsknowledgemultiword
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2All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality
W. Timkey, and M. van Schijndel. (2021)
4 years ago by @jannaom
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lm
rogue
language
embeddings
similarity
contextualised
model
word
dimensions
lmroguelanguageembeddingssimilaritycontextualisedmodelworddimensions
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2MultiVitaminBooster and MultiVitaminRegressor at PARSEME Shared Task 2020: Combining Window-and Dependency-Based Features with Multilingual Contextualized Word Embeddings for Detecting Verbal Multiword Expressions
S. Gombert, and S. Bartsch. Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons, page 149--155. (2020)
4 years ago by @jannaom
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FG
LM
Roberta
language
PIE
expression
expressions
forschungsgruppe
idiomatic
MWE
model
multiword
potential
FGLMRobertalanguagePIEexpressionexpressionsforschungsgruppeidiomaticMWEmodelmultiwordpotential
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3MTLB-STRUCT@ PARSEME 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models
S. Taslimipoor, S. Bahaadini, and E. Kochmar. arXiv preprint arXiv:2011.02541, (2020)
4 years ago by @jannaom
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FG
LM
language
PIE
expression
expressions
forschungsgruppe
idiomatic
CRF
MWE
model
multiword
potential
FGLMlanguagePIEexpressionexpressionsforschungsgruppeidiomaticCRFMWEmodelmultiwordpotential
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2TRAVIS at PARSEME Shared Task 2020: How good is (m) BERT at seeing the unseen?
M. Kurfali. International Conference on Computational Linguistics (COLING), Barcelona, Spain (Online), December 13, 2020, page 136--141. (2020)
4 years ago by @jannaom
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FG
LM
language
PIE
expression
expressions
forschungsgruppe
idiomatic
BERT
MWE
model
multiword
potential
FGLMlanguagePIEexpressionexpressionsforschungsgruppeidiomaticBERTMWEmodelmultiwordpotential
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1Disambiguation of Potentially Idiomatic Expressions with Contextual Embeddings
M. Kurfali, and R. Östling. Joint Workshop on Multiword Expressions and Electronic Lexicons, Barcelona, Spain (Online), December 13, 2020, page 85--94. (2020)
4 years ago by @jannaom
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FG
LM
language
PIE
expression
expressions
forschungsgruppe
idiomatic
BERT
MWE
model
multiword
potential
FGLMlanguagePIEexpressionexpressionsforschungsgruppeidiomaticBERTMWEmodelmultiwordpotential
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3Commonsense Knowledge Base Completion with Structural and Semantic Context
C. Malaviya, C. Bhagavatula, A. Bosselut, and Y. Choi. Proceedings of AAAI, (2020)
4 years ago by @jannaom
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language
model
kg
knowledge
graph
languagemodelkgknowledgegraph
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3Cognitive Graph for Multi-Hop Reading Comprehension at Scale.
M. Ding, C. Zhou, Q. Chen, H. Yang, and J. Tang. CoRR, (2019)
4 years ago by @jannaom
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answering
language
quaternion
bert
model
kg
knowledge
graph
answeringlanguagequaternionbertmodelkgknowledgegraph
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4ConvBERT: Improving BERT with Span-based Dynamic Convolution
Z. Jiang, W. Yu, D. Zhou, Y. Chen, J. Feng, and S. Yan. (2020)cite arxiv:2008.02496Comment: 17 pages.
5 years ago by @jannaom
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language
bert
model
languagebertmodel
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3JAKET: Joint Pre-training of Knowledge Graph and Language Understanding.
D. Yu, C. Zhu, Y. Yang, and M. Zeng. CoRR, (2020)
5 years ago by @jannaom
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language
embedding
model
knowledge
graph
languageembeddingmodelknowledgegraph
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4KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation.
X. Wang, T. Gao, Z. Zhu, Z. Liu, J. Li, and J. Tang. CoRR, (2019)
5 years ago by @jannaom
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lm
thema:ba
language
thema
embedding
thema:kg_and_lm
model
kg
knowledge
graph
lmthema:balanguagethemaembeddingthema:kg_and_lmmodelkgknowledgegraph
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2Recurrent neural network based language model.
T. Mikolov. Interspeech, 2, page 3. (2010)
8 years ago by @tobias.koopmann
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rnn
ma
language
dmir
rnnmalanguagedmir
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4Exploring the Limits of Language Modeling
R. Jozefowicz, O. Vinyals, M. Schuster, N. Shazeer, and Y. Wu. (2016)cite arxiv:1602.02410.
8 years ago by @daschloer
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LM
RNN
language
LMRNNlanguage
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4Adversarial Generation of Natural Language
S. Subramanian, S. Rajeswar, F. Dutil, C. Pal, and A. Courville. ACL 2017, (2017)
8 years ago by @daschloer
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GAN
language
GANlanguage
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publications (hide)25
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1Graphadapter: Tuning vision-language models with dual knowledge graph

8Lora: Low-rank adaptation of large language models

3Large Language Models and Knowledge Graphs: Opportunities and Challenges

2Optimizing Medical Service Request Processes through Language Modeling and Semantic Search

3Compacter: Efficient Low-Rank Hypercomplex Adapter Layers

2Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning

1It’s not Rocket Science: Interpreting Figurative Language in Narratives

2All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

2MultiVitaminBooster and MultiVitaminRegressor at PARSEME Shared Task 2020: Combining Window-and Dependency-Based Features with Multilingual Contextualized Word Embeddings for Detecting Verbal Multiword Expressions

3MTLB-STRUCT@ PARSEME 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models

2TRAVIS at PARSEME Shared Task 2020: How good is (m) BERT at seeing the unseen?

1Disambiguation of Potentially Idiomatic Expressions with Contextual Embeddings

3Commonsense Knowledge Base Completion with Structural and Semantic Context

3Cognitive Graph for Multi-Hop Reading Comprehension at Scale.

4ConvBERT: Improving BERT with Span-based Dynamic Convolution

3JAKET: Joint Pre-training of Knowledge Graph and Language Understanding.

4KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation.

2Recurrent neural network based language model.

4Exploring the Limits of Language Modeling

4Adversarial Generation of Natural Language

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