{"id":23237091,"url":"https://github.com/nicolay-r/rusentrel-leaderboard","last_synced_at":"2025-08-11T20:12:13.679Z","repository":{"id":109047999,"uuid":"417766991","full_name":"nicolay-r/RuSentRel-Leaderboard","owner":"nicolay-r","description":"This is an official Leaderboard for the RuSentRel-1.1 dataset originally described in paper (arxiv:1808.08932)","archived":false,"fork":false,"pushed_at":"2023-12-28T18:08:03.000Z","size":1967,"stargazers_count":8,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-07T05:51:35.429Z","etag":null,"topics":["attention","attention-mechanism","benchmark","bert-model","bilstm","chatgpt","classifiers","cnn","language-models","leaderboard","low-resource-nlp","neural-networks","relation-extraction","sentiment-analysis"],"latest_commit_sha":null,"homepage":"https://github.com/nicolay-r/RuSentRel","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nicolay-r.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2021-10-16T08:31:09.000Z","updated_at":"2024-10-29T15:03:28.000Z","dependencies_parsed_at":"2023-12-27T01:36:49.164Z","dependency_job_id":null,"html_url":"https://github.com/nicolay-r/RuSentRel-Leaderboard","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/nicolay-r/RuSentRel-Leaderboard","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nicolay-r%2FRuSentRel-Leaderboard","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nicolay-r%2FRuSentRel-Leaderboard/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nicolay-r%2FRuSentRel-Leaderboard/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nicolay-r%2FRuSentRel-Leaderboard/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nicolay-r","download_url":"https://codeload.github.com/nicolay-r/RuSentRel-Leaderboard/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nicolay-r%2FRuSentRel-Leaderboard/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":269948859,"owners_count":24501821,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-11T02:00:10.019Z","response_time":75,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["attention","attention-mechanism","benchmark","bert-model","bilstm","chatgpt","classifiers","cnn","language-models","leaderboard","low-resource-nlp","neural-networks","relation-extraction","sentiment-analysis"],"created_at":"2024-12-19T04:13:26.617Z","updated_at":"2025-08-11T20:12:13.646Z","avatar_url":"https://github.com/nicolay-r.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# RuSentRel Leaderboard \n[![](https://img.shields.io/badge/AREkit--ss_Compatible-0.23.1-purple.svg)](https://github.com/nicolay-r/arekit-ss#usage)\n\n\u003e 📓 **Update 01 October 2023**: this collection **is now available in [arekit-ss](https://github.com/nicolay-r/arekit-ss)**\n\u003e for a [quick sampling](https://github.com/nicolay-r/arekit-ss#usage) of contexts with all subject-object relation mentions with just **single script into\n\u003e `JSONL/CSV/SqLite`** including (optional) language transferring 🔥 [[Learn more ...]](https://github.com/nicolay-r/arekit-ss#usage)\n\n**Dataset description**: RuSentRel collection consisted of analytical articles from Internet-portal `inosmi.ru`. \nThese are translated into Russian texts in the domain of international politics obtained from foreign authoritative sources.\nThe collected articles contain both the author's opinion on the subject matter \nof the article and a large number of references mentioned between the participants of the described situations. \nIn total, 73 large analytical texts were labeled with about 2000 relations.\n\nThis repository is an official results benchmark for automatic\n*sentiment attitude extraction* task within *RuSentRel* collection.\nLet's follow the [task](#task) section for greater details.\n\n**Contributing**: Please feel free to make pull requests, and at \n[awesome-sentiment-attitude-extraction](https://github.com/nicolay-r/awesome-sentiment-attitude-extraction) \nespecially!\n\n\u003e For more details about RuSentRel please proceed with the related [repository](https://github.com/nicolay-r/RuSentRel).\n\n## Contents\n* [Task](#task)\n* [Approaches](#approaches)\n* [Submission Evaluation](#submission-evaluation)\n* [Leaderboard](#leaderboard)\n    * [Neural Networks Optimization](#neural-networks-optimization)\n* [Related works](#related-works)\n* [References](#references)\n\n## Task \n\nGiven a subset of documents in the RuSentRel collection, where each document is\npresented by a pair: (1) text, (2) a list of selected named entities.\nFor each document, it is required to complete a list of such entity pairs (e\u003csub\u003es\u003c/sub\u003e, e\u003csub\u003eo\u003c/sub\u003e), \nfor which text conveys the presence of sentiment relation from the *e\u003csub\u003es\u003c/sub\u003e* (subject) towards an *e\u003csub\u003eo\u003c/sub\u003e* (object).\nLabel assignation can be *neg* or *pos*. \n\n| Example                                                                                                                                                                                                                                                     |\n|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| ... При этом \u003cins\u003eМосква\u003c/ins\u003e неоднократно подчеркивала, что ее активность на \u003cins\u003eбалтике\u003c/ins\u003e является ответом именно на действия **\u003cins\u003eНАТО\u003c/ins\u003e** и эскалацию враждебного подхода к **\u003cins\u003eРосcии\u003c/ins\u003e** вблизи ее восточных границ ... *(... Meanwhile \u003cins\u003eMoscow\u003c/ins\u003e has repeatedly emphasized that its activity in the \u003cins\u003eBaltic Sea\u003c/ins\u003e is a response precisely to actions of **\u003cins\u003eNATO\u003c/ins\u003e** and the escalation of the hostile approach to **\u003cins\u003eRussia\u003c/ins\u003e** near its eastern borders ...)*\n| (NATO-\u003eRussia, neg), (Russia-\u003eNATO, neg)                                                                                                                                                                                                                    |\n\nTask paper: https://arxiv.org/pdf/1808.08932.pdf\n\n## Approaches\n\nThe task is considered as a context classification problem, in which *context* is a text region with mentioned pair (attitude participants) in it.\nThen classified context-level attitudes transfers onto document-level by averaging context labels of the related pair (using the voting method).\n\nWe implement [AREkit](https://github.com/nicolay-r/AREkit) toolkit which becomes a framework for the following applications:\n* BERT-based language models [[code]](https://github.com/nicolay-r/bert-for-attitude-extraction-with-ds);\n* Neural Networks with (and w/o) Attention mechanism [[code]](https://github.com/nicolay-r/neural-networks-for-attitude-extraction);\n* Conventional Machine Learning methods [[code]](https://github.com/nicolay-r/sentiment-relation-classifiers);\n\n[Back to Top](#contents)\n\n## Submission Evaluation\n\n![](https://img.shields.io/badge/Python-3.6-brightgreen.svg)\n\nSource code exported from [AREkit-0.21.0 library](https://github.com/nicolay-r/AREkit/tree/0.21.0-rc) and yields of: \n* [Evaluation](evaluation) directory for details of the evaluator implementation and the related dependencies;\n* [Test](test) directory, which includes test scripts that allow applying evaluator for the archived [results](test/data).\n\nUse `evaluate.py` to evaluate your submissions. \nBelow is an example for assessing the results of `ChatGPT-3.5-0613`:\n```python\npython3 evaluate.py --input data/chatgpt-avg.zip --mode classification --split cv3\n```\n\n[Back to Top](#contents)\n \n## Leaderboard \n\nResults ordered from the latest to the oldest. We measure `F1` (scaled by 100) across the following foldings (see [evaluator](#evaluator) section for greater details):\n* F1\u003csub\u003ecv\u003c/sub\u003e - the average `F1` of a 3-fold CV check; \nfoldings carried out by preserving the same number of sentences in each of them;\n* F\u003csub\u003et\u003c/sub\u003e -- `F1` over the predefined TEST set;\n\nThe result assessment organized in experiments:\n* `3l` -- subject-object pairs extraction.\n* `2l` -- classification of already given subject-object pairs on document level;\n\n|Methods       |F1\u003csub\u003ecv\u003c/sub\u003e (3l)  |F1\u003csub\u003et\u003c/sub\u003e (3l)                    |F1\u003csub\u003ecv\u003c/sub\u003e (2l)   |F1\u003csub\u003et\u003c/sub\u003e (2l)    |\n|--------------|------------|-----------------------------|-------------|-------------|\n|              |            |                             |             |             |\n|Expert Agreement\u003csup\u003e[\\**](#footnote2)\u003c/sup\u003e [[1]](#link1)                      | 55.0       |55.0                         |-            |-            |\n|                        |            |                             |             |             |\n|ChatGPT zero-shot with promptings\u003csup\u003e[\\***](#footnote2)\u003c/sup\u003e [[7]](#link7)  |            |                             |             |             |\n|                        |            |                             |             |             |\n|ChatGPT\u003csub\u003e3.5-0613, avg\u003c/sub\u003e [200 words distance]  |  37.7          |  39.6                   |            |                  |\n|ChatGPT\u003csub\u003e3.5-0613, avg\u003c/sub\u003e [50 words distance]  |            |                              |66.19       |**74.47**         |\n|ChatGPT\u003csub\u003e3.5-0613, first\u003c/sub\u003e [50 words distance]  |            |                              |69.23       |74.09         |\n|                        |            |                             |             |             |\n|*Distant Supervision*\u003csub\u003eRA-2.0-large\u003c/sub\u003e for Language Models (BERT-based) [[6]](#link6)  |            |                             |             |             |\n|[\u003csub\u003ept\u003c/sub\u003e -- pretrained, \u003csub\u003eft\u003c/sub\u003e -- fine-tunded]  |            |                             |             |             |\n|SentenceRuBERT (NLI\u003csub\u003ept\u003c/sub\u003e + NLI\u003csub\u003eft\u003c/sub\u003e) |**39.0**    |38.0                     |70.2         |67.7         |\n|SentenceRuBERT (NLI\u003csub\u003ept\u003c/sub\u003e + QA\u003csub\u003eft\u003c/sub\u003e)  |38.4       |**41.9**                     |69.6         |64.2         |\n|SentenceRuBERT (NLI\u003csub\u003ept\u003c/sub\u003e + C\u003csub\u003eft\u003c/sub\u003e)  |37.9        |39.8                         |70.0         |**69.8**         |\n|RuBERT (NLI\u003csub\u003ept\u003c/sub\u003e + NLI\u003csub\u003eft\u003c/sub\u003e)|36.8        |39.9                         |**71.0**         |68.6         |\n|RuBERT (NLI\u003csub\u003ept\u003c/sub\u003e + QA\u003csub\u003eft\u003c/sub\u003e) |34.8        |37.0                         |69.6         |68.2         |\n|RuBERT (NLI\u003csub\u003ept\u003c/sub\u003e + C\u003csub\u003eft\u003c/sub\u003e)  |35.6        |35.4                         |70.0         |69.8         |\n|mBase (NLI\u003csub\u003ept\u003c/sub\u003e + NLI\u003csub\u003eft\u003c/sub\u003e) |33.6        |36.0                         |69.4         |68.2         |\n|mBase (NLI\u003csub\u003ept\u003c/sub\u003e + QA\u003csub\u003eft\u003c/sub\u003e) |30.1        |35.5                         |69.6         |65.2         |\n|mBase (NLI\u003csub\u003ept\u003c/sub\u003e + C\u003csub\u003eft\u003c/sub\u003e)  |30.5        |31.1                         |68.9         |67.7         |\n|                        |            |                             |             |             |\n|*Distant Supervision*\u003csub\u003eRA-2.0-large\u003c/sub\u003e for *(Attentive) Neural Networks* + Frames annotation [Joined Training] [[6]](#link6)\u003csub\u003ereproduced\u003c/sub\u003e, [[4]](#link4)\u003csub\u003eoriginal\u003c/sub\u003e     |            |                             |             |             |\n|PCNN\u003csub\u003eends\u003c/sub\u003e      |**32.2**        |**39.9**                         |70.2         |67.8         |\n|BiLSTM        |32.0        |38.8                         |**71.2**     |68.4         |\n|PCNN          |31.6        |39.7                         |69.5         |70.5         |\n|LSTM          |31.6        |39.5                         |68.0         |**75.4**         |\n|Att-BiLSTM [[P.Zhou et. al]](https://aclanthology.org/P16-2034.pdf)   |31.0        |37.3                         |66.2         |71.2         |\n|AttCNN\u003csub\u003eends\u003c/sub\u003e    |30.9        |**39.9**                         |66.8         |72.7         |\n|IAN\u003csub\u003eends\u003c/sub\u003e       |30.7        |36.7                         |69.1         |72.6         |\n|                        |            |                             |             |             |\n|*Distant Supervision*\u003csub\u003eRA-1.0\u003c/sub\u003e for Multi-Instance *Neural Networks* [Joined Training] [[5]](#link5)     |            |                             |             |             |\n|                        |            |                             |             |             |\n|MI-PCNN                                    |            |                             |             |**68.0**         |\n|MI-CNN                                    |            |                             |             |62.0         |\n|PCNN                                       |            |                             |            |67.0         |\n|CNN                                       |            |                             |             |63.0         |\n|*Language Models (BERT-based)* [[6]](#link6)|            |                             |             |             |\n|SentenceRuBERT (NLI)                                    |33.4        |32.7                         |69.8         |67.6         |\n|SentenceRuBERT (QA)                                     |34.3        |**38.9**                         |**70.2**      |67.1         |\n|SentenceRuBERT (C)                                     |34.0        |35.2                         |69.3         |65.5         |\n|RuBERT (NLI)                                   |29.4        |39.6                         |68.9         |66.4         |\n|RuBERT (QA)                                       |32.0        |35.3                         |69.5         |66.2         |\n|RuBERT (C)                                     |**36.8**        |37.6                         |67.8         |66.2         |\n|mBase (NLI)                                    |29.2        |37.0                         |67.8         |58.4         |\n|mBase (QA)    |28.6        |33.8                         |66.5         |65.4         |\n|mBase (C)     |26.9        |30.0                         |67.0         |**68.9**     |\n|                        |            |                             |             |             |\n|*(Attentive) Neural Networks* + Frames annotation ([[6]](#link6)\u003csub\u003ereproduced\u003c/sub\u003e, [[3]](#referces)\u003csub\u003eoriginal\u003c/sub\u003e)       |            |                             |             |             |\n|IAN\u003csub\u003eends\u003c/sub\u003e       |**30.8**        |32.2                         |60.8         |63.5         |\n|AttPCNN\u003csub\u003eends\u003c/sub\u003e      |29.9        |**32.6**                         |64.3         |63.3         |\n|PCNN          |29.6        |32.5                         |64.4         |63.3         |\n|CNN                                               |28.7        |31.4                         |63.6         |65.9         |\n|BILSTM        |28.6        |32.4                         |62.3         |**71.2**         |\n|LSTM          |27.9        |31.6                         |61.9         |65.3         |\n|AttCNN\u003csub\u003eends\u003c/sub\u003e       |27.6        |29.7                         |65.0         |66.2         |\n|Att-BiLSTM [[P.Zhou et. al]](https://aclanthology.org/P16-2034.pdf)   |27.5        |32.3                         |**65.7**         |68.2         |\n|                        |            |                             |             |             |\n|*Convolutional networks* [[2]](#link2)           |            |                             |             |             |\n|PCNN [[code]](https://github.com/nicolay-r/sentiment-pcnn)                                   |            | **0.31**                    |             |             |\n|CNN                                               |            | **0.30**                    |             |             |\n|                        |            |                             |             |             |\n|*Conventional methods* [[1]](#link1) [[code]](https://github.com/nicolay-r/sentiment-relation-classifiers)             |            |                             |             |             |\n|Gradient Boosting (Grid search)                        |**20.3**\u003csup\u003e[\\*](#footnote)    |**28.0**                     |             |             |\n|Random Forest (Grid search)                        |19.1\u003csup\u003e[\\*](#footnote)\u003c/sup\u003e        |27.0                     |             |             |\n|Random Forest                                      |15.7\u003csup\u003e[\\*](#footnote)\u003c/sup\u003e        |27.0                     |             |             |\n|Naive Bayes (Bernoulli)                            |15.2\u003csup\u003e[\\*](#footnote)\u003c/sup\u003e        |16.0                          |             |             |\n|SVM                                               |15.1\u003csup\u003e[\\*](#footnote)\u003c/sup\u003e        |15.0                          |             |             |\n|Gradient Boosting                                 |14.4\u003csup\u003e[\\*](#footnote)\u003c/sup\u003e        |27.0                     |             |             |\n|SVM (Grid search)                                 |14.3\u003csup\u003e[\\*](#footnote)\u003c/sup\u003e        |15.0                          |             |             |\n|NaiveBayes (Gauss)                                |9.2\u003csup\u003e[\\*](#footnote)\u003c/sup\u003e         |11.0                          |             |             |\n|KNN                                               |7.0\u003csup\u003e[\\*](#footnote)\u003c/sup\u003e         |9.0                          |             |             |\n|                                                  |            |                             |             |             |\n|Baseline (School) [[link]](https://miem.hse.ru/clschool/)|            |**12.0**                         |             |             |\n|Baseline (Distr)                                  |            |8.0                          |             |             |\n|Baseline (Random)                                 |7.4\u003csup\u003e[\\*](#footnote)\u003c/sup\u003e         |8.0                          |             |             |\n|Baseline (Pos)                                    |3.9\u003csup\u003e[\\*](#footnote)\u003c/sup\u003e         |4.0                          |             |             |\n|Baseline (Neg)                                    |5.2\u003csup\u003e[\\*](#footnote)\u003c/sup\u003e         |5.0                          |             |             |\n\n\u003ca name=\"footnote\"\u003e*\u003c/a\u003e: Results that were not mentioned in papers.\n\n\u003ca name=\"footnote2\"\u003e**\u003c/a\u003e: We asked another super-annotator to label the collection, and compared her annotation with our gold standard using average F-measure of positive and negative classes in the same way as for\nautomatic approaches. In such a way, we can reveal the upper border for automatic\nalgorithms. We obtained that F-measure of human labeling. [[1]](#link1)\n\n\u003ca name=\"footnote3\"\u003e***\u003c/a\u003e: We consider translation into english samples via the [arekit-ss](https://github.com/nicolay-r/arekit-ss) by translating texts into \nenglish first, and then wrapping them into prompts. We consider a `k`-words distance (`50` by default, in english) between words as a upper bound for pairs organization;\nbecause of the latter and prior standards, results might be lower (translation increases distance in words). \n\n[Back to Top](#contents)\n\n### Neural Networks Optimization\n\nThe training process is described in [Rusnachenko et. al., 2020](https://arxiv.org/abs/2006.13730) (section 7.1) and \nrelies on the *Multi-Instance learning* approach, originally proposed in  [Zeng et. al., 2015](https://www.aclweb.org/anthology/D15-1203.pdf) paper. \n(SGD application, bags terminology, instances selection within bags).\nAll the batch context samples are gathered into *bags*.\nAuthors propose to select the best instance in every bag as follows: \ncalculate the `max` value of p(y\u003csub\u003ei\u003c/sub\u003e|m\u003csub\u003ei\u003c/sub\u003e,j) across i'th values within a particular j'th bag. \nThe latter allows them to adopt `loss` function on bags level.\n\nIn our works, we adopt bags for synonymous context gathering.\nTherefore, for gradients calculation within bags, we choose `avg` function instead. \nThe assumption here is to consider other synonymous attitudes during the gradients calculation procedure.\nWe use `BagSize \u003e 1` in earlier work [Rusnachenko, 2018](https://github.com/nicolay-r/sentiment-pcnn/tree/clls-2018)\nIn the latest experiments, we consider `BagSize = 1` and therefore don't exploit bag values averaging.\n\n[Back to Top](#contents)\n\n## Related works\n[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)\n\n[Awesome Sentiment Attitude Extraction](https://github.com/nicolay-r/awesome-sentiment-attitude-extraction)\n\n[Back to Top](#contents)\n\n## References\n\n\u003ca name=\"link1\"\u003e[1]\u003c/a\u003e\n**Natalia Loukachevitch, Nicolay Rusnachenko**\n*Extracting Sentiment Attitudes from Analytical Texts*\nProceedings of International Conference on Computational Linguistics and Intellectual Technologies Dialogue-2018 (arXiv:1808.08932)\n[[paper]](https://arxiv.org/pdf/1808.08932.pdf)\n[[code]](https://github.com/nicolay-r/sentiment-relation-classifiers)\n\n\u003ca name=\"link2\"\u003e[2]\u003c/a\u003e\n**Nicolay Rusnachenko, Natalia Loukachevitch**\n*Using Convolutional Neural Networks for Sentiment Attitude Extraction from Analytical Texts,*\nEPiC Series in Language and Linguistics 4, 1-10, 2019 \n[[paper]](https://wwww.easychair.org/publications/download/pQrC)\n[[code]](https://github.com/nicolay-r/sentiment-pcnn)\n\n\u003ca name=\"link3\"\u003e[3]\u003c/a\u003e\n**Nicolay Rusnachenko, Natalia Loukachevitch**\n*Studying Attention Models in Sentiment Attitude Extraction Task*\nMétais E., Meziane F., Horacek H., Cimiano P. (eds) Natural Language Processing and Information Systems. NLDB 2020. Lecture Notes in Computer Science, vol 12089. Springer, Cham\n[[paper]](https://arxiv.org/abs/2006.11605)\n[[code]](https://github.com/nicolay-r/attitude-extraction-with-attention)\n\n\u003ca name=\"link4\"\u003e[4]\u003c/a\u003e\n**Nicolay Rusnachenko, Natalia Loukachevitch**\n*Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision*\nThe 10th International Conference on Web Intelligence, Mining and Semantics (WIMS 2020), June 30-July 3 (arXiv:2006.13730)\n[[paper]](https://dl.acm.org/doi/10.1145/3405962.3405985)\n[[code]](https://github.com/nicolay-r/attitude-extraction-with-attention-and-ds)\n\n\u003ca name=\"link5\"\u003e[5]\u003c/a\u003e\n**Nicolay Rusnachenko, Natalia Loukachevitch, Elena Tutubalina**\n*Distant Supervision for Sentiment Attitude Extraction*\nProceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)\n[[paper]](https://aclanthology.org/R19-1118.pdf) \n[[code]](https://github.com/nicolay-r/attitudes-extraction-ds)\n\n\u003ca name=\"link6\"\u003e[6]\u003c/a\u003e\n**Nicolay Rusnachenko**\n*Language Models Application in Sentiment Attitude Extraction Task*\nProceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2021;33(3):199-222. (In Russ.)\n[[paper]](https://nicolay-r.github.io/website/data/rusnachenko2021language.pdf)\n[[code-networks]](https://github.com/nicolay-r/neural-networks-for-attitude-extraction)\n[[code-bert]](https://github.com/nicolay-r/bert-for-attitude-extraction-with-ds)\n\n\u003ca name=\"link7\"\u003e[7]\u003c/a\u003e\n**Bowen Zhang, Daijun Ding, Liwen Jing**\n*How would Stance Detection Techniques Evolve after the Launch of ChatGPT?*\n[[paper]](https://arxiv.org/pdf/2212.14548.pdf)\n\n\n[Back to Top](#contents)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnicolay-r%2Frusentrel-leaderboard","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnicolay-r%2Frusentrel-leaderboard","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnicolay-r%2Frusentrel-leaderboard/lists"}