diff --git a/docs/constituency_parsing.md b/docs/constituency_parsing.md
index 261ca82..a28d5df 100644
--- a/docs/constituency_parsing.md
+++ b/docs/constituency_parsing.md
@@ -48,8 +48,10 @@ EM, F1, LP and LR can be calculated using the [Evalb](https://nlp.cs.nyu.edu/eva
| System | EM | F1 | LP | LR | code |
| --- | --- | --- | --- | --- | --- |
| [Liu and Zhang (2017)](https://www.aclweb.org/anthology/Q17-1029/) | 44.94 | 91.81 | - | - | [Github](https://github.com/dpfried/rnng-bert) |
+| [Fried et al. (2019)](https://aclanthology.org/P19-1031/) | 44.42 | 92.14 | - | - | [Github](https://github.com/dpfried/rnng-bert) |
| [Zhou and Zhao (2019)](https://www.aclweb.org/anthology/P19-1230/) | - | 92.18 | 92.33 | 92.03 | [Github](https://github.com/DoodleJZ/HPSG-Neural-Parser) |
| [Mrini et al. (2020)](https://arxiv.org/abs/1911.03875) | - | 92.64 | 93.45 | 91.85 | [Github](https://github.com/KhalilMrini/LAL-Parser) |
+| [Tian et al. (2020)](https://aclanthology.org/2020.findings-emnlp.153/) | - | 92.66 | 92.50 | 92.83 | [Github](https://github.com/cuhksz-nlp/SAPar) |
| [Yang and Deng (2020)](https://arxiv.org/abs/2010.14568) | 49.72 | 93.59 | 93.80 | 93.40 | [Github](https://github.com/princeton-vl/attach-juxtapose-parser) |
diff --git a/docs/entity_tagging.md b/docs/entity_tagging.md
index b138787..8e0fce5 100644
--- a/docs/entity_tagging.md
+++ b/docs/entity_tagging.md
@@ -105,9 +105,11 @@ Paper summarizing the bakeoff:
| System | F-score |
| --- | --- |
+| [Song et al. (2021)](https://arxiv.org/abs/2105.01279) | 96.2 |
| [Liu et al (2020)](https://arxiv.org/pdf/1909.07606.pdf) | 95.7 |
| [Meng et. al. (2019)](https://arxiv.org/abs/1901.10125) | 95.5 |
| [Ma et al (2020)](https://www.aclweb.org/anthology/2020.acl-main.528.pdf) | 95.4 |
+| [Diao et al. (2020)](https://aclanthology.org/2020.findings-emnlp.425/)| 95.3 |
| [Sun et al (2020)](https://arxiv.org/pdf/1907.12412.pdf) | 95.0 |
| [Yan et al (2020)](https://ieeexplore.ieee.org/abstract/document/9141551) | 94.1 |
| [Liu et. al. (2019)](https://www.aclweb.org/anthology/N19-1247) | 93.74 |
@@ -142,6 +144,8 @@ Using the test split by http://www.aclweb.org/anthology/E17-2113:
| System | F-score (name mentions) | F-score (nominal mentions) | F-score (Overall) |
| --- | --- | --- | --- |
| [Ma et al (2020)](https://www.aclweb.org/anthology/2020.acl-main.528.pdf) | 70.9 | 67.0 | 70.5 |
+| [Nie et al. (2020)](https://aclanthology.org/2020.emnlp-main.107/)| | | 69.80 |
+| [Nie et al. (2020)](https://aclanthology.org/2020.findings-emnlp.378/)| | | 69.78 |
| [Meng et. al. (2019)](https://arxiv.org/abs/1901.10125) | 67.6 | | |
| [Hu and Zheng (2020)](https://www.jstage.jst.go.jp/article/transinf/E103.D/7/E103.D_2019EDP7253/_pdf/-char/ja) | 56.4 | | |
| [Sui et al. (2019)](https://www.aclweb.org/anthology/D19-1396/) | 56.45 | 68.32 | 63.09 |
diff --git a/docs/question_answering.md b/docs/question_answering.md
index 0bff637..5fa33fc 100644
--- a/docs/question_answering.md
+++ b/docs/question_answering.md
@@ -87,6 +87,7 @@ NLPCC DBQA 2016
| System | MRR | F1 |
| --- | --- | --- |
+| [ZEN 2.0](https://arxiv.org/abs/2105.01279) | 96.1 | 86.5 |
| [ERNIE 2.0](https://arxiv.org/pdf/1907.12412.pdf) | 95.8 | 85.8 |
| [Meng et. al. (2019)](https://arxiv.org/pdf/1901.10125.pdf) (Glyce + BERT) | - | 83.4 |
| [ERNIE(baidu)](https://arxiv.org/pdf/1904.09223.pdf) | 95.1 | 82.7 |
diff --git a/docs/sentiment_analysis.md b/docs/sentiment_analysis.md
index 1c4900a..ffecb53 100644
--- a/docs/sentiment_analysis.md
+++ b/docs/sentiment_analysis.md
@@ -104,6 +104,8 @@ F1-score
| | F1 | Accuracy |
| --- | --- | --- |
| [Chen et al., 2020: 3SiBert](https://www.aclweb.org/anthology/2020.lrec-1.293.pdf) | | 0.967 | https://www.aclweb.org/anthology/2020.lrec-1.293.pdf
+| [ZEN 2.0](https://arxiv.org/abs/2105.01279) | | 0.965 |
+| [ZEN](https://aclanthology.org/2020.findings-emnlp.425/) | | 0.961 |
| [ERNIE 2.0](https://arxiv.org/pdf/1907.12412.pdf) | | 0.958 |
| [ERNIE](https://arxiv.org/pdf/1904.09223.pdf) | | 0.954 |
| BERT * | | 0.943 |
diff --git a/docs/topic_classification.md b/docs/topic_classification.md
index b94a295..9b06524 100644
--- a/docs/topic_classification.md
+++ b/docs/topic_classification.md
@@ -40,6 +40,9 @@ Sina News RSS subscription channel data from 2005 to 2011, which contains 74 mil
| | Accuracy |
| --- | --- |
| [J. Chen, C. Cao, X. Jiang](https://www.aclweb.org/anthology/2020.lrec-1.293.pdf) | 98.7% |
+| [Song, et al., (2021)](https://arxiv.org/abs/2105.01279) | 97.93% |
+| [Cui, et al., (2020)](https://arxiv.org/pdf/2004.13922.pdf) | 97.9% |
+| [Diao, et al., (2020)](https://aclanthology.org/2020.findings-emnlp.425/) | 97.64% |
| [Y. Song](https://iopscience.iop.org/article/10.1088/1742-6596/1453/1/012156/pdf)| 97.56% |
| [W. Liu, P. Zhou, et al](https://www.aclweb.org/anthology/2020.acl-main.537.pdf) | 96.71% |
| [S. Xin](https://iopscience.iop.org/article/10.1088/1742-6596/1549/2/022011/pdf) | 96.04% |
diff --git a/docs/word_embedding.md b/docs/word_embedding.md
index 08e5429..b03648c 100644
--- a/docs/word_embedding.md
+++ b/docs/word_embedding.md
@@ -61,7 +61,9 @@ See e.g. [Torregrossa et al., 2020](https://www.aclweb.org/anthology/2020.lrec-1
| System | wordsim-240 (⍴) | wordsim-296 (⍴) |
| --- | --- | --- |
| [Sun et. al. (2019)](https://arxiv.org/pdf/1902.08795.pdf) (VCWE) | 57.81 | 61.29 |
+| [Song et. al. (2018)](https://www.ijcai.org/Proceedings/2018/0608.pdf) | 54.14 | 57.04 |
| [Yu et. al. (2017)](https://www.aclweb.org/anthology/D17-1027) (JWE) | 51.92 | 59.84 |
+| Baseline (CBOW) | 51.01 | 53.65 |
@@ -146,6 +148,7 @@ Given “France : Paris :: China : ?”, a system should come up with the answer
| Name | Additional features | Training Corpus Size | Source |
| --- | --- | --- | --- |
+| DSG | Leverage directional information to improve skip-gram algorithm | | [Song et al., 2018](https://aclanthology.org/N18-2028/) |
| FastText | - | 374M characters | [Grave et al., 2018](https://arxiv.org/pdf/1802.06893.pdf) |
| Mimick | Interpolate between similar characters to improve rare words, multilingual | | [Pinter et al., 2017](https://www.aclweb.org/anthology/D17-1010.pdf) |
| Glyph2vec | Uses character bitmaps, canjie to address OOV problem | 10M chars | [Chen et al., 2020](https://www.aclweb.org/anthology/2020.acl-main.256.pdf) |
diff --git a/docs/word_segmentation.md b/docs/word_segmentation.md
index f9d1deb..1ac5f29 100644
--- a/docs/word_segmentation.md
+++ b/docs/word_segmentation.md
@@ -53,8 +53,8 @@ F1 = 0.857
| Model | AS | CITYU | MSRA | PKU |
| --- | --- | --- | --- | --- |
| [Ke et al. (2021)](https://aclanthology.org/2021.naacl-main.436/) | 97.0 | 98.2 | 98.5 | 96.9 |
-| [Qiu, Pei, Yan, Huang (2020)](https://aclanthology.org/2020.findings-emnlp.260/) | 96.4 | 96.9 | 98.1 | 96.4 |
| [Tian, Song, Xia, Zhang, Wang (2020)](https://www.aclweb.org/anthology/2020.acl-main.734/) | 96.6 | 97.9 | 98.4 | 96.5 |
+| [Qiu, Pei, Yan, Huang (2020)](https://aclanthology.org/2020.findings-emnlp.260/) | 96.4 | 96.9 | 98.1 | 96.4 |
| [Meng et al. (2019)](https://arxiv.org/abs/1901.10125) | 96.7* | 97.9* | 98.3 | 96.7 |
| [Huang et al. (2019)](https://arxiv.org/abs/1903.04190)| 96.6 | 97.6 | 97.9 | 96.6 |
| [Ma et al. (2018)](http://aclweb.org/anthology/D18-1529) | 96.2 | 97.2 | 97.4 | 96.1 |