Papers with automated system
RE-FIN: Retrieval-based Enrichment for Financial data (2025.coling-industry)
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| Challenge: | Financial sentiment analysis (FSA) is a powerful tool to support business decision-making and perform financial forecasting. |
| Approach: | They propose a system that retrieves information from a knowledge base to enrich financial sentences, making them more knowledge-dense and explicit. |
| Outcome: | The proposed system generates propositions from the knowledge base and employs Retrieval-Augmented Generation (RAG) to augment the original text with relevant information. |
CodeGenWrangler: Data Wrangling task automation using Code-Generating Models (2025.naacl-industry)
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| Challenge: | Tabular datasets in industrial settings often encompass extensive data with numerous rows and columns. |
| Approach: | They propose a system that leverages large language models to generate executable code for data wrangling tasks . they identify inherent patterns in the data while leveraging external knowledge . |
| Outcome: | The proposed system detects patterns in the data while leveraging external knowledge . it generates executable code for data-wrangling tasks like missing value imputation and error correction . |
‘Don’t Get Too Technical with Me’: A Discourse Structure-Based Framework for Automatic Science Journalism (2023.emnlp-main)
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| Challenge: | Science journalism is the production of journalistic content covering scientific topics that are not covered in the scientific literature. |
| Approach: | They propose to use a dataset to generate a scientific paper's tuples, a summary snippet and a novel technical framework to integrate a paper' s discourse structure with its metadata to guide generation. |
| Outcome: | The proposed system outperforms baseline methods in elaborating a content plan meaningful for the target audience, simplifying the information selected, and producing a coherent final report in a layman’s style. |
Sentiment Analysis: It’s Complicated! (N18-1)
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Kian Kenyon-Dean, Eisha Ahmed, Scott Fujimoto, Jeremy Georges-Filteau, Christopher Glasz, Barleen Kaur, Auguste Lalande, Shruti Bhanderi, Robert Belfer, Nirmal Kanagasabai, Roman Sarrazingendron, Rohit Verma, Derek Ruths
| Challenge: | a dataset of over 7,000 tweets annotated with 5x coverage is used for sentiment analysis . a "complicated" class of sentiment is used to categorize text based on a predefined notion of sentiment . |
| Approach: | They propose to use a "complicated" class of sentiment to categorize tweets . they build a publicly available tweet sentiment analysis dataset . |
| Outcome: | The proposed classifiers perform better over a new publicly available TSA dataset . the classifier performance is compared with existing methods and improves on existing ones . |
Prompt-driven Detection of Offensive Urdu Language using Large Language Models (2026.eacl-long)
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| Challenge: | Offensive language detection systems require extensive tuning and careful model design . a resource gap exists for addressing offensive languages, especially those transcribed in non-native scripts . |
| Approach: | They evaluate pre-trained LLMs using different transcriptions of the Urdu language to assess their performance . they find that they can detect hateful and offensive content in diverse linguistic contexts . |
| Outcome: | The proposed methods perform comparable to fine-tuned benchmarks in diverse languages. |
PsyGUARD: An Automated System for Suicide Detection and Risk Assessment in Psychological Counseling (2024.emnlp-main)
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| Challenge: | Existing systems for fine-grained suicide detection and risk assessment are lacking . a lack of domain-specific systems for this task poses a challenge to automated crisis intervention aimed at suicide prevention. |
| Approach: | They propose to use a fine-grained suicide detection system to assess risk in counseling . they develop a taxonomy for detecting suicide ideation and a large-scale dataset . |
| Outcome: | The proposed system detects suicidal ideation and assesses risk in counseling . it can provide safe, helpful, and tailored responses for further assessment . |
ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation (2021.acl-long)
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Vijit Malik, Rishabh Sanjay, Shubham Kumar Nigam, Kripabandhu Ghosh, Shouvik Kumar Guha, Arnab Bhattacharya, Ashutosh Modi
| Challenge: | a system that could assist a judge in predicting the outcome of a case should be explainable. |
| Approach: | They propose to use a corpus of 35k Indian Supreme Court cases annotated with original court decisions to promote research in this area. |
| Outcome: | The proposed system has an accuracy of 78% versus 94% for human legal experts. |
Metaphor Reasoning is Meta-reasoning (2026.acl-long)
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Qianyu He, Junting Lu, Yikai Zhang, Siyu Yuan, Xiaojun Meng, Jiansheng Wei, Jiaqing Liang, Yanghua Xiao
| Challenge: | Existing work on metaphor reasoning's impact on reasoning abilities is limited. |
| Approach: | They propose a system for synthesizing metaphorical riddles that satisfy five quality dimensions: diverse, balanced, reasoning-oriented, challenging, and verifiable. |
| Outcome: | The proposed system improves reasoning abilities across six domains using only thousands of metaphorical riddles. |
Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification (2020.coling-main)
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| Challenge: | Existing methods for generating textual-based explanations are highly implausible and damage a user’s trust in the automated system. |
| Approach: | They propose a method which first applies robust transformer models on a real-world, up-to-date, self-collected mergers and acquisitions dataset and then generates plausible, post-hoc, counterfactual explanations. |
| Outcome: | The proposed model improves model accuracy and human performance while generating plausible explanations based on human trials. |