Papers with classification problem
SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction (2021.findings-acl)
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| Challenge: | Document-level relation extraction (doc-level RE) is a classification problem that predicts relations for all entity pairs in a document. |
| Approach: | They propose a document-level relation extraction architecture to represent intra- and inter-sentential relations in different ways. |
| Outcome: | The proposed architecture outperforms the state-of-the-art methods on the public datasets. |
Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection (2024.acl-long)
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| Challenge: | Stance detection is used to infer attitudes from human communications . stance decisions involve complex judgments generated by LLMs . |
| Approach: | They propose a method for stance detection which relies on a new prompting framework . it allows for more than one stance object type and no examples of stance attribution . |
| Outcome: | The proposed method outperforms fine-tuned stance detection systems. |
Knowledge Stimulated Contrastive Prompting for Low-Resource Stance Detection (2022.findings-emnlp)
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| Challenge: | Stance Detection Tasks require background knowledge especially when there is no explicit target mentioned in text. |
| Approach: | They propose a masked language prompt joint contrastive learning approach to stimulate the knowledge inherit from pre-trained models. |
| Outcome: | The proposed model is effective in stance detection on three benchmarks. |
AtTGen: Attribute Tree Generation for Real-World Attribute Joint Extraction (2023.acl-long)
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| Challenge: | Attribute extraction aims to identify attribute names and the corresponding attribute values from descriptive texts. |
| Approach: | They propose a unified formulation for real-world attribute extraction application, where closed-world, open-world and semi-open attribute extraction tasks are modeled uniformly. |
| Outcome: | The proposed model outperforms existing methods on three datasets and outperformed existing methods by a large margin. |
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification (2022.acl-long)
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| Challenge: | Recent studies suggest that pre-trained language models have gained rich knowledge during pre-training. |
| Approach: | They propose to tune pre-trained language models with task-specific prompts to improve and stabilize prompttuning. |
| Outcome: | Extensive experiments on zero and few-shot text classification tasks show that prompt-tuning improves and stabilizes prompttun-ing. |
Improving Neural Political Statement Classification with Class Hierarchical Information (2022.findings-acl)
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Erenay Dayanik, Andre Blessing, Nico Blokker, Sebastian Haunss, Jonas Kuhn, Gabriella Lapesa, Sebastian Pado
| Challenge: | skewed classification of fine-grained categories in text-based computational social science is challenging on the NLP side. |
| Approach: | They propose to use hierarchical relations among categories in codebooks to create constraints on the learned model. |
| Outcome: | The proposed model improves on two datasets and multiple languages. |
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs (2022.naacl-main)
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| Challenge: | Existing conversation models treat knowledge selection as a sentence ranking problem where each sentence is handled individually, ignoring the internal semantic connection between sentences. |
| Approach: | They propose to automatically convert background knowledge documents into document semantic graphs and perform knowledge selection over such graphs. |
| Outcome: | The proposed model improves on the knowledge selection task and the response generation task on HollE and generalizes on unseen topics in WoW. |
Don’t Generate, Classify! Low-Latency Prompt Optimization with Structured Complementary Prompt (2026.eacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated strong performance across diverse tasks, but their performance varies significantly across different prompts. |
| Approach: | They propose a framework that reframes prompt engineering as a classification problem. |
| Outcome: | The proposed framework improves answer quality by up to 26.5% in win rate compared to prior methods while reducing latency by upto 1,956 times. |
A Neural Pairwise Ranking Model for Readability Assessment (2022.findings-acl)
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| Challenge: | Automatic Readability Assessment (ARA) is traditionally treated as a classification problem in NLP research. |
| Approach: | They propose a neural ranking approach to automatic readability assessment (ARA) they propose 'neural' ranking methods that can be used to rank texts by reading level . |
| Outcome: | The proposed approach performs well in monolingual single/cross corpus testing scenarios and achieves a zero-shot cross-lingual ranking accuracy of over 80% for both French and Spanish when trained on English data. |
WER-BERT: Automatic WER Estimation with BERT in a Balanced Ordinal Classification Paradigm (2021.eacl-main)
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| Challenge: | Automatic Speech Recognition (ASR) systems are evaluated using Word Error Rate (WER) a higher WER means a lower percentage of errors between the ground truth and the transcription of the system. |
| Approach: | They propose a new balanced paradigm for automatic Word Error Rate estimation using a Librispeech dataset and a Google Cloud's Speech-to-Text API. |
| Outcome: | The proposed approach is more effective than regression in a classification setting, but suffers from heavy class imbalance. |
Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality (2021.naacl-main)
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| Challenge: | Existing benchmarks for lexical substitution depend on human recall as the only source of data, authors say . existing benchmarks lack coverage of the appropriate substitutes that would be most helpful to humans . |
| Approach: | They propose a benchmark for lexical substitution to find appropriate substitutes for a target word in context . existing benchmarks depend on human recall as the only source of data, they argue . |
| Outcome: | The new benchmark for lexical substitution uses a context-free thesaurus . it has 3x as many substitutes per target word for the same quality, and substitutes are 1.4x more appropriate . |
Zero-shot User Intent Detection via Capsule Neural Networks (D18-1)
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| Challenge: | Existing methods to classify intents are labor-intensive and time-consuming as intents will be diverse and new intents may be involved. |
| Approach: | They propose a zero-shot intent detection problem which aims to detect emerging user intents where no labeled utterances are currently available. |
| Outcome: | The proposed model can discriminate emerging intents when no labeled utterances are available in training data. |
SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific Topics (2024.emnlp-main)
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| Challenge: | Recent studies have used prompt-based fine-tuning methods for text classification tasks . however, the difficulty and costs of manually selecting domain label terms for the verbalizer remain unexplored . |
| Approach: | They propose a framework to automatically retrieve scientific topic-related terms for low-resource text classification tasks. |
| Outcome: | The proposed method outperforms state-of-the-art methods on scientific text classification tasks under few and zero-shot settings. |
MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media (2025.naacl-long)
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| Challenge: | Existing methods for profiling news media focus on textual features, causing them to overlook complex relationships between entities. |
| Approach: | They propose a framework for profiling news media from the lens of political bias and factuality. |
| Outcome: | The proposed framework improves existing models and improves them by integrating structural information from similar nodes. |
Evaluating Extreme Hierarchical Multi-label Classification (2022.acl-long)
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| Challenge: | Several natural language processing tasks are defined as a classification problem in its most complex form: Multi-label Hierarchical Extreme classification. |
| Approach: | They propose a classification metric inspired by the Information Contrast Model (ICM) they use a set of formal properties to analyze the evaluation metrics. |
| Outcome: | The proposed evaluation metrics are suitable for multi-label hierarchical extreme classification scenarios. |
Parameter-Efficient Instruction Tuning of Large Language Models For Extreme Financial Numeral Labelling (2024.naacl-long)
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Subhendu Khatuya, Rajdeep Mukherjee, Akash Ghosh, Manjunath Hegde, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal
| Challenge: | Existing methods to automatically annotate relevant numerals (GAAP metrics) occurring in financial documents are not cost-effective nor scalable. |
| Approach: | They propose a generative paradigm for annotating GAAP metrics with XBRL tags using metric metadata and a parameter efficient model using LoRA. |
| Outcome: | The proposed model outperforms baseline models on two financial numeric labeling datasets and outperformed several strong baseline models. |
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications (2021.emnlp-main)
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| Challenge: | Sentence-level Quality estimation (QE) is traditionally a regression task . but large multilingual contextualized language models are expensive and infeasible for real-world applications. |
| Approach: | They evaluate several model compression techniques for QE and find they are inefficient . they argue that a full model parameterization is required to achieve SoTA results . |
| Outcome: | The proposed models are poorly expressive in a regression task, the authors argue . they show that reframing QE as a classification problem and evaluating models would improve their performance in real-world applications. |
From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models (2022.findings-emnlp)
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| Challenge: | Existing models for natural language processing (NLP) are fine-tuned and released for research and deployments. |
| Approach: | They propose a PLM reuse paradigm that merges teacher-PLM knowledge into a student model. |
| Outcome: | The proposed paradigm can reduce the computational cost and environmental side-effects of retraining the PLM from scratch. |
Interactive Refinement of Cross-Lingual Word Embeddings (2020.emnlp-main)
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| Challenge: | Cross-lingual word embeddings transfer knowledge between languages to models trained on resource-rich languages can predict in low-resource languages. |
| Approach: | They propose an interactive system to quickly refine cross-lingual word embeddings for a given classification problem. |
| Outcome: | The proposed system improves on identifying health-related text in four low-resource languages. |
Extracting Financial Events from Raw Texts via Matrix Chunking (2024.lrec-main)
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| Challenge: | Event Extraction (EE) is widely used in the Chinese financial field to provide valuable structured information. |
| Approach: | They propose a task which extracts financial events from raw texts and an efficient method called MACK. |
| Outcome: | The proposed method is fault-tolerant and can visualize interactions among text components. |
Beyond Text: Leveraging Multi-Task Learning and Cognitive Appraisal Theory for Post-Purchase Intention Analysis (2024.findings-acl)
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| Challenge: | Recent studies have shown that user-level features can carry more task-related information than the text itself. |
| Approach: | They evaluate multi-task learning frameworks grounded in Cognitive Appraisal Theory to predict user behavior as a function of users’ self-expression and psychological attributes. |
| Outcome: | The proposed models improve on the language and traits of users, while lacking rich annotations of other attributes. |
DDO: Dual-Decision Optimization for LLM-Based Medical Consultation via Multi-Agent Collaboration (2025.emnlp-main)
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| Challenge: | Existing LLMs fail to capture the dual nature of medical consultation (MC) this mismatch often results in ineffective symptom inquiry and unreliable disease diagnosis. |
| Approach: | They propose a novel LLM-based framework that performs Dual-Decision Optimization by decoupling the two sub-tasks and optimizing them with distinct objectives through a collaborative multi-agent workflow. |
| Outcome: | The proposed framework outperforms existing LLM-based approaches on three real-world MC datasets and achieves competitive performance with state-of-the-art generation-based methods. |