Papers with domain experts
NLP Lean Programming Framework: Developing NLP Applications More Effectively (N18-5)
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| Challenge: | NLPf is a framework for creating custom natural language processing models and pipelines by utilizing common software development build systems. |
| Approach: | They propose a framework for creating custom NLP models and pipelines by utilizing common software development build systems. |
| Outcome: | This framework allows developers to train and integrate domain-specific NLP pipelines into their applications seamlessly. |
Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction (2024.findings-eacl)
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| Challenge: | Existing frameworks for fine-grained few-shot entity extraction are difficult to implement in the chemical domain due to the information overload of scientific papers. |
| Approach: | They propose a sequence-to-sequence based few-shot entity extraction approach . it uses a seq2seq entity extractor and a self-validation module to reconstruct original input sentence . |
| Outcome: | The proposed framework achieves 8.26% and 6.84% performance gains on two datasets. |
Incorporating Fine-grained Events in Stock Movement Prediction (D19-51)
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| Challenge: | Existing studies mainly adopt coarse-grained events, which loses the specific semantic information of diverse event types. |
| Approach: | They propose to use a finance event dictionary to extract fine-grained events from finance news to train a neural model that uses the extracted events as the distant supervised label to train stock prediction. |
| Outcome: | The proposed method outperforms baselines and has good generalizability. |
Improving Hierarchical Text Clustering with LLM-guided Multi-view Cluster Representation (2024.emnlp-industry)
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| Challenge: | a multi-stage approach to hierarchical clustering of interaction drivers in contact centers is proposed . silhouette score and human preference score are improved by 36.7% for top-level clusters compared to standard agglomerative clustering . |
| Approach: | They propose a multi-stage approach that introduces different perspectives or views to improve the quality of hierarchical clustering of interaction drivers in a contact center. |
| Outcome: | The proposed approach improves the quality of generated clusters on public datasets with minimal query time compared to the current state-of-the-art approaches. |
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests (2020.coling-main)
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| Challenge: | Existing methods to generate distractors for multiple choice questions are expensive and time-consuming. |
| Approach: | They propose a question and answer guided distractor generation framework to automate distractors generated by domain experts. |
| Outcome: | The proposed model outperforms existing models and achieves state-of-the-art on a large-scale dataset. |
Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making (2021.acl-long)
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| Challenge: | Entity Matching (EM) aims at recognizing entity records that denote the same real-world object. |
| Approach: | They propose a novel EM framework that consists of Heterogeneous Information Fusion and Key Attribute Tree Induction to decouple feature representation from matching decision. |
| Outcome: | The proposed framework outperforms SOTA EM models on 6 public datasets and 3 industrial datasets. |
SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graph (2024.acl-long)
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| Challenge: | Existing KG construction methods rely on human intervention to attain qualified KGs, which severely hinders the practical application of domain KG. |
| Approach: | They propose a general KG construction framework that uses large language models as "S**killed" A**utomatic C**onstructors for domain knowledge (G**raph) |
| Outcome: | The proposed framework generates specialized multi-level knowledge graphs at the scale of over one million nodes and achieves 89.32% precision rate compared to state-of-the-art methods. |
Question Answering in Climate Adaptation for Agriculture: Model Development and Evaluation with Expert Feedback (2025.findings-acl)
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| Challenge: | Existing domain-specific question answering systems have generative capabilities, but their ability to answer climate adaptation questions remains unclear. |
| Approach: | They propose an iterative framework that enables LLMs to dynamically aggregate information from heterogeneous sources, such as climate literature and structured tabular climate data from climate model projections and historical observations. |
| Outcome: | The proposed framework enables LLMs to dynamically aggregate information from heterogeneous sources, such as text from climate literature and structured tabular climate data from climate model projections and historical observations. |
ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications (2024.naacl-long)
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| Challenge: | Existing statistical phrasal or hierarchical machine translation systems relies on a large set of translation rules which results in engineering challenges. |
| Approach: | They propose to use factorized grammar from the field of linguistics as more general translation rules from XTAG English Grammar to generate a manually crafted summarization dataset. |
| Outcome: | The proposed method outperforms existing methods on low-resource language translation tasks with less training data. |
Open-Vocabulary Argument Role Prediction For Event Extraction (2022.findings-emnlp)
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| Challenge: | Existing studies on event extraction depend on pre-defined argument roles . despite great progress, many studies still rely on hand-crafted ontologies . |
| Approach: | They propose an unsupervised framework for customizing argument roles for event extraction . they propose a human-annotated event extraction dataset with 143 customized argument roles . |
| Outcome: | The proposed framework outperforms existing methods on an event extraction dataset. |
ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)
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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao
| Challenge: | ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge. |
| Approach: | They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks . |
| Outcome: | ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering. |
NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes (2024.findings-acl)
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| Challenge: | NoteChat is a cooperative multi-agent framework for generating patient-physician dialogues . evaluator finds it outperforms state-of-the-art models for generating clinical notes . clinical documentation is largely done by physicians at both steps . |
| Approach: | They propose a cooperative multi-agent framework leveraging Large Language Models to generate patient-physician dialogues. |
| Outcome: | The proposed framework outperforms state-of-the-art models for generating clinical notes . it can engage patients directly and help clinical documentation, a leading cause of physician burnout . |
Text-to-ES Bench: A Comprehensive Benchmark for Converting Natural Language to Elasticsearch Query (2025.acl-long)
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DonggeXue DonggeXue, Zhili Pu, Zhentao Xia, Hongli Sun, Ruihui Hou, Guangya Yu, Yupian Lin, Yongqi Fan, Jingping Liu, Tong Ruan
| Challenge: | Recent research on text-to-Query has explored using large language models to convert user query intent to executable code. |
| Approach: | They propose a novel semantic parsing task that leverages large language models to generate domain-specific language and post-processing code to support multi-index Elasticsearch queries. |
| Outcome: | The proposed model outperforms DeepSeek-R1 on the large Elasticsearch Dataset (LED) and BirdES datasets. |
Inclusive Leadership in the Age of AI: A Dataset and Comparative Study of LLMs vs. Real-Life Leaders in Workplace Action Planning (2025.findings-emnlp)
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| Challenge: | a new study compares LLMs and human leaders in workplace action planning tasks . the leader success bot guides real-life leaders in generating inclusive workplace action plans . |
| Approach: | They propose a leader success bot that guides leaders in generating inclusive workplace action plans. |
| Outcome: | The Leader Success Bot guides real-life leaders in generating inclusive workplace action plans. |
A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse (2025.emnlp-main)
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| Challenge: | Existing datasets focus on explicit causality in structured text, providing limited support for detecting implicit causal expressions. |
| Approach: | They propose a dataset of Reddit posts annotated across four causal tasks . they use a binary causal classification, explicit vs. implicit causality, cause–effect span extraction and causal gist generation to bridge causal detection and reasoning over informal discourse. |
| Outcome: | The proposed dataset analyzes 10,120 Reddit posts discussing public health related to the COVID-19 pandemic. |
The Sound of Syntax: Finetuning and Comprehensive Evaluation of Language Models for Speech Pathology (2025.emnlp-main)
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| Challenge: | State-of-the-art multimodal language models (MLMs) show promise for supporting SLPs, but their use remains underexplored due to a limited understanding of their performance in high-stakes clinical settings. |
| Approach: | They propose a taxonomy of real-world use cases of multimodal language models in speech-language pathologies to address this gap. |
| Outcome: | The proposed model outperforms 15 state-of-the-art models in speech-language pathologies across five use cases and achieves improvements of over 30% on domain-specific data. |
TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection (2026.acl-long)
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| Challenge: | Existing benchmarks for political user-level stance detection rely on noisy heuristics or distant supervision. |
| Approach: | They propose a large-scale, expert-annotated benchmark for political user-level stance detection with explicit social network structure that integrates user content and followee signals. |
| Outcome: | The proposed framework outperforms baselines in terms of quality and reliability. |
Leveraging External Knowledge for Historical Document Restoration via Retrieval-Augmented Large Language Models (2026.findings-acl)
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| Challenge: | Historical documents suffer from illegibility due to physical deterioration and damage due to deteriorating materials. |
| Approach: | a new framework leverages large language models with retrieval-augmented generation to restore historical documents. authors propose a framework that leverages implicit knowledge of pre-trained LLMs with explicitly retrieved external context. |
| Outcome: | a new framework outperforms existing methods for restoration of historical documents in Korean . the proposed model can restore both general characters and named entities, the authors say . |