AntNLP · lala370 · Mar 25, 2022 · Apr 30, 2022 · Apr 30, 2022 · Sep 25, 2022
diff --git a/2022Spring_AntNLP/readme.md b/2022Spring_AntNLP/readme.md
@@ -1,8 +1,8 @@
-# AntNLP Seminar -- 2021 Spring
+# AntNLP Seminar -- 2022 Spring
 
-Time: **9:00 am, Friday**
+Time: **3:00 pm, Friday**
 
-Venue: B914, Science Building.
+Venue: B914, Science Building; Online
 
 Welcome to AntNLP Seminar 2022 Spring. : )
 
@@ -40,23 +40,22 @@ Welcome to AntNLP Seminar 2022 Spring. : )
 
 | Week | Date | Speaker | Paper | Materials |
 | ---- | ---- | ------- | ----- | --------- |
-| 1    | 3.4  | 纪焘    |       |           |
-| 2    | 3.11 | 刘宇芳  |       |           |
-| 3    | 3.18 | 高怡    |       |           |
-| 4    | 3.25 | 杨晰    |       |           |
-| 5    | 4.1  | 杜威    |       |           |
-| 6    | 4.8  | 王志承  |       |           |
-| 7    | 4.15 | 李雨倩  |       |           |
-| 8    | 4.22 | 汪杰    |       |           |
-| 9    | 4.29 |         |       |           |
-| 10   | 5.6  |         |       |           |
-| 11   | 5.13 |         |       |           |
-| 12   | 5.20 |         |       |           |
-| 13   | 5.27 |         |       |           |
-| 14   | 6.3  |         |       |           |
-| 15   | 6.10 |         |       |           |
-| 16   | 6.17 |         |       |           |
-| 17   | 6.24 |         |       |           |
+| 1    | 3.11 | 纪焘    | PRETRAINED LANGUAGE MODEL IN CONTINUAL LEARNING: A COMPARATIVE STUDY <br>[TACL2021]Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs    | [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week1)          |
+| 2    | 3.18 | 刘宇芳  | Dataset Distillat  <br> [ICLR2021]DATASET CONDENSATION WITH GRADIENT MATCHING    | [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week2)          |
+| 3    | 3.25 | 高怡    |       | [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week3)          |
+| 4    | 4.1  | 杨晰    | [EMNLP19] Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks <br>  [EMNLP20] Inducing Target-Specific Latent Structures for Aspect Sentiment Classification     | [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week4)          |
+| 5    | 4.8  | 杜威    |[EMNLP21]Zero-Shot Information Extraction as a Unified Text-to-Triple Translation | [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week5)          |
+| 6    | 4.15 | 王志承  |[ICLR2018]Measuring the Intrinsic Dimension of Objective Landscapes <br>[ACL2021]Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning | [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week6)          |
+| 7    | 4.22 | 刘宇芳  | [ICML2020]Certified Data Removal from Machine Learning Models<br> [AAAI2022]Hard to Forget: Poisoning Attacks on Certified Machine Unlearning <br> [AISTATS2021]Approximate Data Deletion from Machine Learning Models     | [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week7)    |
+| 8    | 4.29 | 纪焘    | [ACL2022]Knowledge Neurons in Pretrained Transformers <br> [EMNLP21]MultiEURLEX – A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer <br>[ACL2022]Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora    | [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week8)          |
+| 9    | 5.6  | 高怡    |       | [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week9)          |
+| 10   | 5.13 | 杨晰    | [ACL18]Neural Open Information Extraction <br> [EMNLP20]Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction <br>[EMNLP20]OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction <br>[EMNLP21]Maximal Clique Based Non-Autoregressive Open Information Extraction | [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week10)         |
+| 11   | 5.20 | 李鹏    |       | [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week11)          |
+| 12   | 5.27 | 杜威  |[EMNLP16] Creating a Large Benchmark for Open Information Extraction <br> [EMNLP20] Multi2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT | [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week12)          |
+| 13   | 6.3  | 休息  |       |           |
+| 14   | 6.10 | 王志承   |[ACL2022]An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels <br> [ACL2022]Prototypical Verbalizer for Prompt-based Few-shot Tuning       |  [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week14)         |
+| 15   | 6.17 | 休息    |       |           |
+| 16   | 6.24 | 汪杰<br>李雨倩    |      | [Slides](https://github.com/AntNLP/seminar/tree/master/2022Spring_AntNLP/week16)          |
 
 ------
 
@@ -69,4 +68,4 @@ Welcome to AntNLP Seminar 2022 Spring. : )
   - Peng Li, [ruhao9805@gmail.com](mailto:ruhao9805@gmail.com)
   - Tao Ji, [taoji.cs@gmail.com](mailto:taoji.cs@gmail.com)
   - Yang Wei, [i@godweiyang.com](mailto:i@godweiyang.com)
-- any quesitons, please feel free to contact us.
+- any quesitons, please feel free to contact us.
diff --git a/2022Spring_AntNLP/week10/0513.pdf b/2022Spring_AntNLP/week10/0513.pdf
diff --git a/2022Spring_AntNLP/week12/0605.pptx b/2022Spring_AntNLP/week12/0605.pptx
diff --git a/2022Spring_AntNLP/week14/0610.pptx b/2022Spring_AntNLP/week14/0610.pptx
diff --git a/2022Spring_AntNLP/week2/0318.pdf b/2022Spring_AntNLP/week2/0318.pdf
diff --git a/2022Spring_AntNLP/week4/0401.pdf b/2022Spring_AntNLP/week4/0401.pdf
diff --git a/2022Spring_AntNLP/week5/0408.pptx b/2022Spring_AntNLP/week5/0408.pptx
diff --git a/2022Spring_AntNLP/week6/0415.pptx b/2022Spring_AntNLP/week6/0415.pptx
diff --git a/2022Spring_AntNLP/week7/0422.pdf b/2022Spring_AntNLP/week7/0422.pdf
diff --git a/2022Spring_AntNLP/week8/0429.pdf b/2022Spring_AntNLP/week8/0429.pdf
diff --git a/2023Spring_StatNLP/readme.md b/2023Spring_StatNLP/readme.md
@@ -0,0 +1,43 @@
+# Statistical Natural Language Processing Seminar -- 2023 Spring
+
+Time: **9:50, Thursday**
+
+Venue: Tin Ka Ping Building 234 (田家炳楼234)
+
+Welcome to StatNLP Seminar 2023 Spring. : )
+
+## On Papers
+
+- Please choose recent papers (2023, 2022, 2021) from top NLP/AI venues. A (incomplete) list is
+  - NLP: ACL, TACL, EMNLP, NAACL, EACL
+  - ML: ICML, NeurIPS, AISTATS, JMLR, ICLR
+  - AI: AAAI, IJCAI
+  - IR/DM: SIGIR, CIKM, WSDM, KDD, WWW
+- While we are interested in a broad range of NLP/AI topics, this year we will pay special attentions on two themes
+  - foundation models (pre-training models)
+  - explaining methods
+- We hope that, with high probability, you choose papers from [here](https://github.com/AntNLP/seminar/blob/master/2023Spring_StatNLP/2023-paper-list.md)
+- other materials with broad interests are welcome (e.g., tutorials form top conferences, high-quality surveys).
+
+## For Presenters
+
+- Please fill your slots in the Agenda at least one week before your presentation.
+
+  - Please format Paper fields with *[venue+year]title* (e.g. [ACL21]A Good Paper).
+- Please upload your slides, and add links to them in Slides fields.
+
+- Besides technical novelties, please give enough background knowledge in case people are unfamiliar with your topic.
+
+- It would be great to keep your presentation within 60 min.
+
+## For Audiences
+
+- Please read abstract/introduction sections before the seminar.
+
+## Agenda
+
+
+
+| Week | Date | Speaker | Paper | Materials |
+| ---- | ---- | ------- | ----- | --------- |
+| 1    | 3.2  |         |       |           |
diff --git a/readme.md b/readme.md
@@ -0,0 +1,43 @@
+# Statistical Natural Language Processing Seminar -- 2023 Spring
+
+Time: **9:50, Thursday**
+
+Venue: Tin Ka Ping Building 234 (田家炳楼234)
+
+Welcome to StatNLP Seminar 2023 Spring. : )
+
+## On Papers
+
+- Please choose recent papers (2023, 2022, 2021) from top NLP/AI venues. A (incomplete) list is
+  - NLP: ACL, TACL, EMNLP, NAACL, EACL
+  - ML: ICML, NeurIPS, AISTATS, JMLR, ICLR
+  - AI: AAAI, IJCAI
+  - IR/DM: SIGIR, CIKM, WSDM, KDD, WWW
+- While we are interested in a broad range of NLP/AI topics, this year we will pay special attentions on two themes
+  - foundation models (pre-training models)
+  - explaining methods
+- We hope that, with high probability, you choose papers from [here](https://github.com/AntNLP/seminar/blob/master/2023Spring_StatNLP/2023-paper-list.md)
+- other materials with broad interests are welcome (e.g., tutorials form top conferences, high-quality surveys).
+
+## For Presenters
+
+- Please fill your slots in the Agenda at least one week before your presentation.
+
+  - Please format Paper fields with *[venue+year]title* (e.g. [ACL21]A Good Paper).
+- Please upload your slides, and add links to them in Slides fields.
+
+- Besides technical novelties, please give enough background knowledge in case people are unfamiliar with your topic.
+
+- It would be great to keep your presentation within 60 min.
+
+## For Audiences
+
+- Please read abstract/introduction sections before the seminar.
+
+## Agenda
+
+
+
+| Week | Date | Speaker | Paper | Materials |
+| ---- | ---- | ------- | ----- | --------- |
+| 1    | 3.2  |         |       |           |