Papers with real applications
Unleashing Large Language Models’ Proficiency in Zero-shot Essay Scoring (2024.findings-emnlp)
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| Challenge: | Recent advances in automated essay scoring (AES) have relied on labeled essays, requiring tremendous cost and expertise for their acquisition. |
| Approach: | They propose a zero-shot prompting framework that automatically decomposes writing proficiency into distinct traits and generates scoring criteria for each trait. |
| Outcome: | The proposed framework outperforms straightforward prompting (Vanilla) on TOEFL11 and ASAP, while the small-sized Llama2-13b-chat significantly outperformed ChatGPT. |
Few-shot Natural Language Generation for Task-Oriented Dialog (2020.findings-emnlp)
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| Challenge: | Existing methods for NLG depend on heavily annotated data, which is infeasible for new domains. |
| Approach: | They propose a system that converts a dialog act into a response in natural language . they propose 'nuclear language generation' to simulate a few-shot learning setting . |
| Outcome: | The proposed model outperforms existing methods on a large set of annotated datasets. |
Robust Semantic Parsing with Adversarial Learning for Domain Generalization (N19-2)
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| Challenge: | Using adversarial learning to train models on a higher level of abstraction to increase their robustness to lexical and stylistic variations is crucial for the integration of Semantic Parsing technologies in real applications. |
| Approach: | They propose to perform Semantic Parsing with a domain classification adversarial task and an unsupervised domain discovery approach that yields equivalent improvements. |
| Outcome: | The proposed approach improves on a French corpus of encyclopedic documents annotated with FrameNet and an unsupervised domain discovery approach yields equivalent improvements. |
Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding (2022.emnlp-industry)
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Jun Wang, Patrick Ng, Alexander Hanbo Li, Jiarong Jiang, Zhiguo Wang, Bing Xiang, Ramesh Nallapati, Sudipta Sengupta
| Challenge: | Recent research on Text-to-SQL semantic parsing relies on parser or heuristic based approach to understand natural language query. |
| Approach: | They propose a general-purpose, modular neural semantic parsing framework that is based on token-level fine-grained query understanding. |
| Outcome: | The proposed framework outperforms the state-of-the-art model by 2.7% on a WikiTableQuestions test set. |
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction (P19-1)
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| Challenge: | Existing algorithms address aspect term extraction and aspect sentiment classification as separate tasks, which can be complicated for real applications. |
| Approach: | They propose a dual crOss-sharEd RNN framework to generate all aspect term-polarity pairs of the input sentence simultaneously. |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks on three benchmark datasets. |
Sentence Embedding Alignment for Lifelong Relation Extraction (N19-1)
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| Challenge: | Existing approaches to relation extraction require a fixed set of relations . Existing methods assume a closed set of relationships and perform once-and-for-all training on a set of datasets. |
| Approach: | They propose to improve the stochastic gradient methods with a replay memory to alleviate the forgetting problem by anchoring the sentence embedding space. |
| Outcome: | The proposed method outperforms state-of-the-art methods on multiple benchmarks. |
Taming Language Models for Text-attributed Graph Learning with Decoupled Aggregation (2025.acl-long)
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| Challenge: | Existing approaches to learning text-attributed graphs neglect interaction between textual and structural information. |
| Approach: | They propose a framework that integrates textual and structural information into TAG learning . they propose combining semantic aggregation and structural aggregations to improve learning a . |
| Outcome: | The proposed framework outperforms state-of-the-art learning methods while requiring less resources. |
Neural-based Mixture Probabilistic Query Embedding for Answering FOL queries on Knowledge Graphs (2022.emnlp-main)
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| Challenge: | Existing methods to embed entities and first-order logical queries in a vector space are often violated in real applications and limit their performance. |
| Approach: | They propose a Neural-based Mixture Probabilistic Query Embedding Model that embeds entities and first-order logical queries in a vector space. |
| Outcome: | The proposed model outperforms state-of-the-art methods on benchmark datasets. |
CXR Data Annotation and Classification with Pre-trained Language Models (2022.coling-1)
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Nina Zhou, Ai Ti Aw, Zhuo Han Liu, Cher heng Tan, Yonghan Ting, Wen Xiang Chen, Jordan sim zheng Ting
| Challenge: | Existing tools for clinical data annotation are limited to specific institutions due to differences in writing style, structure, language use and label definition. |
| Approach: | They propose a weak supervision annotation framework with two improvements over existing ones . the framework provides an efficient form of sample selection and data auto-annotation . |
| Outcome: | The proposed framework provides better results for clinical data annotation tasks compared to existing frameworks. |
C3LRSO: A Chinese Corpus for Complex Logical Reasoning in Sentence Ordering (2025.coling-main)
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| Challenge: | Existing sentence ordering datasets for non-English languages are unavailable. |
| Approach: | They propose a parameter-free sentence ordering dataset that provides genuinely unordered sentences without artificial segmentation cues. |
| Outcome: | The proposed method outperforms existing methods on the sentence ordering task. |
Federated Chinese Word Segmentation with Global Character Associations (2021.findings-acl)
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| Challenge: | Chinese word segmentation (CWS) is a fundamental task for natural language processing. |
| Approach: | They propose a neural model for Chinese word segmentation with federated learning to help CWS deal with data isolation. |
| Outcome: | The proposed model outperforms baselines on a simulated environment with five nodes. |
Co-Teaching Student-Model through Submission Results of Shared Task (2021.findings-emnlp)
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| Challenge: | Shared tasks require participants to submit only system outputs and descriptions. |
| Approach: | They propose to utilize all system outputs in a shared task to build a unified system that performs better than the task's single best system. |
| Outcome: | The proposed scheme outperforms the best system in the SHINRA2019-JP shared task with nine participants. |
BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction (2024.acl-long)
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| Challenge: | Aspect sentiment quad prediction aims to predict aspects due to distinct data distribution. |
| Approach: | They propose a method that aggregates multiple templates with a broader view . they first construct a few-shot ASQP dataset that contains richer categories . |
| Outcome: | The proposed method outperforms the state-of-the-art methods under four few-shot settings and other public datasets. |
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation (2022.acl-long)
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| Challenge: | Existing work on pre-trained generative models often fails to detect non-existent or incorrect content . Existing studies have attempted to detect hallucinations based on oracle references . |
| Approach: | They propose a token-level, reference-free hallucination detection task based on Wikipedia annotations to detect non-existent or incorrect content. |
| Outcome: | The proposed task is token-level, reference-free hallucination detection task and dataset . authors argue that the proposed task can be used in real-time to detect hallucines . |
Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation (D19-1)
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| Challenge: | Existing methods for news comment generation have not been well studied. |
| Approach: | They propose a “read-attend-comment” procedure for automatic news comment generation and formalize it with a reading network and a generation network. |
| Outcome: | The proposed procedure outperforms existing methods in terms of automatic evaluation and human judgment on two public datasets. |
Lexi: Self-Supervised Learning of the UI Language (2022.findings-emnlp)
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| Challenge: | Existing models rely on UI metadata, which is often missing or not accessible. |
| Approach: | They propose a vision and language model that can learn generic visio-linguistic representations of UIs . they use a dataset to train Lexi, which is based on UI metadata . |
| Outcome: | The proposed model can handle unique features of UI screens, including text richness and context sensitivity. |
Towards Distribution-shift Robust Text Classification of Emotional Content (2023.findings-acl)
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| Challenge: | Recent work has shown that supervised models are more robust to change in domain and distribution, but the decrease in performance due to the distribution shift is still a major issue for supervised systems. |
| Approach: | They propose to fine-tune supervised models on task-specific datasets to achieve out-of-distribution performance. |
| Outcome: | The proposed model outperforms all available models in distribution and out of distribution with only a few thousand training samples. |
Recognizing Limits: Investigating Infeasibility in Large Language Models (2025.findings-emnlp)
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| Challenge: | Large language models fail to handle queries that exceed their knowledge and capabilities, leading to incorrect or fabricated responses. |
| Approach: | They conceptualize four main categories of infeasible tasks for LLMs which cover a broad spectrum of hallucination-related challenges identified in prior literature. |
| Outcome: | The proposed models can handle requests exceeding their knowledge and capabilities and refuse them . |
How to Ask Good Questions? Try to Leverage Paraphrases (2020.acl-main)
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| Challenge: | Existing methods to generate human-like questions rely on paraphrases to generate good questions. |
| Approach: | They propose to integrate paraphrase knowledge into question generation to generate human-like questions by combining paraphrases with a back-translation method. |
| Outcome: | The proposed model achieves obvious performance gain over several strong baselines and human evaluation validates that it can ask questions of high quality by leveraging paraphrase knowledge. |
An Empirical Study of Many-to-Many Summarization with Large Language Models (2025.acl-long)
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Jiaan Wang, Fandong Meng, Zengkui Sun, Yunlong Liang, Yuxuan Cao, Jiarong Xu, Haoxiang Shi, Jie Zhou
| Challenge: | Recent studies have shown that large language models (LLMs) have strong multilingual abilities, giving them the potential to perform M2MS in real applications. |
| Approach: | They propose to use many-to-many summarization (M2MS) to generate a brief summary in any language given a document also in any other language. |
| Outcome: | The proposed model outperforms zero-shot LLMs in terms of automatic evaluations. |
PCR4ALL: A Comprehensive Evaluation Benchmark for Pronoun Coreference Resolution in English (2022.lrec-1)
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| Challenge: | Existing PCR systems are not reliable in real applications due to weak semantic meanings of pronouns. |
| Approach: | They propose a benchmark and toolbox that evaluates performance of PCR systems from different perspectives. |
| Outcome: | The proposed benchmark and toolbox evaluates the performance of PCR systems from different perspectives. |
Text Classification Using Label Names Only: A Language Model Self-Training Approach (2020.emnlp-main)
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| Challenge: | Current text classification methods require a large number of labeled documents as training data. |
| Approach: | They propose a model that uses only the label name of each class to train classification models on unlabeled data without using any labeled examples. |
| Outcome: | The proposed model achieves 90% accuracy on four benchmark datasets using label names as the only supervision . |
MMTabReal: Real-World Benchmark for Multimodal Table Understanding (2026.findings-acl)
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| Challenge: | Multimodal tables are ubiquitous in real applications but are difficult to evaluate in multimodal large language models. |
| Approach: | They propose a multimodal table benchmark that compares 500 real-world tables with 4021 question–answer pairs. |
| Outcome: | MMtabReal spans four question types, five reasoning categories, and eight structural archetypes. |