Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-24 | 593 |
| 2026-07-25 | 593 |
| 2026-07-28 | 593 |
| 2026-07-30 | 593 |
| 2026-08-06 | 593 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# NLP Paper  natural language processing paper list ## Contents * [Bert Series](#Bert-Series) * [Transformer Series](#Transformer-Series) * [Transfer Learning](#Transfer-Learning) * [Text Summarization](#Text-Summarization) * [Sentiment Analysis](#Sentiment-Analysis) * [Question Answering](#Question-Answering) * [Machine Translation](#Machine-Translation) * [Surver paper](#survey-paper) * [Downstream task](#downstream-task) * [QA MC Dialogue](#QA-MC-Dialogue) * [Slot filling](#Slot-filling) * [Analysis](#Analysis) * [Word segmentation parsing NER](#Word-segmentation-parsing-NER) * [Pronoun coreference resolution](#Pronoun-coreference-resolution) * [Word sense disambiguation](#Word-sense-disambiguation) * [Sentiment analysis](#Sentiment-analysis) * [Relation extraction](#Relation-extraction) * [Knowledge base](#Knowledge-base) * [Text classification](#Text-classification) * [WSC WNLI NLI](#WSC-WNLI-NLI) * [Commonsense](#Commonsense) * [Extractive summarization](#Extractive-summarization) * [IR](#IR) * [Generation](#generation) * [Quality evaluator](#quality-evaluator) * [Modification (multi-task, masking strategy, etc.)](#modification-multi-task-masking-strategy-etc) * [Probe](#probe) * [Multi-lingual](#multi-lingual) * [Other than English models](#other-than-english-models) * [Domain specific](#domain-specific) * [Multi-modal](#multi-modal) * [Model compression](#model-compression) * [LLM](#LLM) * [Misc](#misc) ### Bert Series * [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - NAACL 2019)](https://arxiv.org/abs/1810.04805) * [ERNIE 2.0: A Continual Pre-training Framework for Language Understanding - arXiv 2019)](https://arxiv.org/abs/1907.12412) * [StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding - arXiv 2019)](https://arxiv.org/abs/1908.04577) * [RoBERTa: A Robustly Optimized BERT Pretraining Approach - arXiv 2019)](https://arxiv.org/abs/1907.11692) * [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations - arXiv 2019)](https://arxiv.org/abs/1909.11942) * [Multi-Task Deep Neural Networks for Natural Language Understanding - arXiv 2019)](https://arxiv.org/abs/1901.11504) * [What does BERT learn about the structure of language?](https://hal.inria.fr/hal-02131630/document) (ACL2019) * [Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned](https://arxiv.org/abs/1905.09418) (ACL2019) [[github](https://github.com/lena-voita/the-story-of-heads)] * [Open Sesame: Getting Inside BERT's Linguistic Knowledge](https://arxiv.org/abs/1906.01698) (ACL2019 WS) * [Analyzing the Structure of Attention in a Transformer Language Model](https://arxiv.org/abs/1906.04284) (ACL2019 WS) * [What Does BERT Look At? An Analysis of BERT's Attention](https://arxiv.org/abs/1906.04341) (ACL2019 WS) * [Do Attention Heads in BERT Track Syntactic Dependencies?](https://arxiv.org/abs/1911.12246) * [Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains](https://arxiv.org/abs/1906.01539) (ACL2019 WS) * [Inducing Syntactic Trees from BERT Representations](https://arxiv.org/abs/1906.11511) (ACL2019 WS) * [A Multiscale Visualization of Attention in the Transformer Model](https://arxiv.org/abs/1906.05714) (ACL2019 Demo) * [Visualizing and Measuring the Geometry of BERT](https://arxiv.org/abs/1906.02715) * [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://arxiv.org/abs/1909.00512) (EMNLP2019) * [Are Sixteen Heads Really Better than One?](https://arxiv.org/abs/1905.10650) (NeurIPS2019) * [On the Validity of Self-Attention as Explanation in Transformer Models
Excerpt of 65,899 characters
Read on GitHub
54
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8bb3eff178fa58f6, topic:transformer, topic:language-model
matched fp:8bb3eff178fa58f6, topic:nlp, readme:natural language processing, readme:text classification