Who Cited It

HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case

2019 · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 17,489 citations · 25 from inside this corpus

Mandar Joshi low, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov low

Given a combinatorial optimisation problem, there are typically multiple ways of modelling it for presentation to an automated solver. Choosing the right combination of model and target solver can have a significant impact on the effectiveness of the solving process. The best combination of model and solver can also be instance-dependent: there may not exist a single combination that works best for all instances of the same problem. We consider the task of building machine learning models to automatically select the best combination for a problem instance. Critical to the learning process is to define instance features, which serve as input to the selection model. Our contribution is the automatic learning of instance features directly from the high-level representation of a problem instance using a transformer encoder. We evaluate the performance of our approach using the Essence modelling language via a case study of three problem classes.

HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanit… (2019)HISTORIAE, History of Socio-C…[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics… (2015)[No title in the source recor…BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019)BERT: Pre-training of Deep Bi…Automatic differentiation in PyTorch (2017)Automatic differentiation in …[No title in the source record — Edinburgh Research Explorer (University of Edinburgh)][No title in the source recor…Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank (2013)Recursive Deep Models for Sem…SQuAD: 100,000+ Questions for Machine Comprehension of Text (2016)SQuAD: 100,000+ Questions for…Gaussian Error Linear Units (GELUs) (2016)Gaussian Error Linear Units (…fairseq: A Fast, Extensible Toolkit for Sequence Modeling (2019)fairseq: A Fast, Extensible T…Know What You Don’t Know: Unanswerable Questions for SQuAD (2018)Know What You Don’t Know: Una…GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding (2018)GLUE: A Multi-Task Benchmark …The PASCAL Recognising Textual Entailment Challenge (2006)The PASCAL Recognising Textua…Cross-lingual Language Model Pretraining (2019)Cross-lingual Language Model …Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019)Sentence-BERT: Sentence Embed…Transformers: State-of-the-Art Natural Language Processing (2020)Transformers: State-of-the-Ar…DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter (2019)DistilBERT, a distilled versi…ALBERT: A Lite BERT for Self-supervised Learning of Language\n Representations (2019)ALBERT: A Lite BERT for Self-…Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Langu… (2022)Pre-train, Prompt, and Predic…Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (2019)Exploring the Limits of Trans…HuggingFace's Transformers: State-of-the-art Natural Language Processing (2019)HuggingFace's Transformers: S…Language Models are Few-Shot Learners (2020)Language Models are Few-Shot …SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021)SimCSE: Simple Contrastive Le…CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020)CodeBERT: A Pre-Trained Model…LoRA Fine-Tuning of a 3B Code LLM for Algorithmic Efficiency (2021)LoRA Fine-Tuning of a 3B Code…In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication (2020)In-Kernel Aggregation and Bro…Prefix-Tuning: Optimizing Continuous Prompts for Generation (2021)Prefix-Tuning: Optimizing Con…On the Opportunities and Risks of Foundation Models (2021)Longformer: The Long-Document Transformer (2020)Longformer: The Long-Document…Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing (2021)Domain-Specific Language Mode…BERTScore: Evaluating Text Generation with BERT (2019)BERTScore: Evaluating Text Ge…XLNet: Generalized Autoregressive Pretraining for Language Understanding (2019)XLNet: Generalized Autoregres…TinyBERT: Distilling BERT for Natural Language Understanding (2020)TinyBERT: Distilling BERT for…mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer (2021)mT5: A Massively Multilingual…RoFormer: Enhanced transformer with Rotary Position Embedding (2023)RoFormer: Enhanced transforme…Scaling Laws for Neural Language Models (2020)Scaling Laws for Neural Langu…Deep Learning--based Text Classification (2021)Deep Learning--based Text Cla…Pre-trained models for natural language processing: A survey (2020)
36 of 37 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…201550,396
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding201933,416
Automatic differentiation in PyTorch201711,099
[No title in the source record — Edinburgh Research Explorer (University of Edinburgh)]7,291
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank20136,849
SQuAD: 100,000+ Questions for Machine Comprehension of Text20166,435
Gaussian Error Linear Units (GELUs)20163,197
fairseq: A Fast, Extensible Toolkit for Sequence Modeling20192,511
Know What You Don’t Know: Unanswerable Questions for SQuAD20182,196
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding20181,861
The PASCAL Recognising Textual Entailment Challenge20061,672
Cross-lingual Language Model Pretraining20191,624

What cites it, inside the corpus

PaperYearCited
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks201911,789
Transformers: State-of-the-Art Natural Language Processing20208,295
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter20194,600
ALBERT: A Lite BERT for Self-supervised Learning of Language\n Representations20194,076
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Langu…20223,836
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer20193,698
HuggingFace's Transformers: State-of-the-art Natural Language Processing20193,147
Language Models are Few-Shot Learners20203,020
SimCSE: Simple Contrastive Learning of Sentence Embeddings20212,645
CodeBERT: A Pre-Trained Model for Programming and Natural Languages20202,630
LoRA Fine-Tuning of a 3B Code LLM for Algorithmic Efficiency20212,531
In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication20202,452
Prefix-Tuning: Optimizing Continuous Prompts for Generation20212,322
On the Opportunities and Risks of Foundation Models20212,265
Longformer: The Long-Document Transformer20202,206
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing20212,146
BERTScore: Evaluating Text Generation with BERT20192,065
XLNet: Generalized Autoregressive Pretraining for Language Understanding20191,854
TinyBERT: Distilling BERT for Natural Language Understanding20201,706
mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer20211,627
RoFormer: Enhanced transformer with Rotary Position Embedding20231,577
Scaling Laws for Neural Language Models20201,537
Deep Learning--based Text Classification20211,525
Pre-trained models for natural language processing: A survey20201,521

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Topics

Topic ModelingComputer Science
Natural Language Processing TechniquesComputer Science
Multimodal Machine Learning ApplicationsComputer Science

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  • supports6 author record(s) attached.
  • supports45 reference(s) recorded.
  • weakensThe DOI names 2025 but the record dates this to 2,019. One of the two is about a different paper.
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