Papers with prediction model
Predicting Cross-lingual Trends in Microblogs (2025.emnlp-industry)
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| Challenge: | Existing trend prediction methods only make predictions within a language, but this is not enough to predict cross-lingual trends. |
| Approach: | They propose a method to predict which microblog trends will cross linguistic boundaries to become popular in other languages and when. |
| Outcome: | The proposed model outperforms existing trend prediction methods and LLM-based approaches by 4% in F1-score . |
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems (N19-1)
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| Challenge: | Experimental results show that extreme multi-label learning improves label prediction quality by 3% to 5% in three of the 5 tasks and is competitive in the others. |
| Approach: | They propose a submodular maximization framework with linear cost to find informative labels which are most relevant to other labels yet least redundant with each other. |
| Outcome: | The proposed model improves label prediction quality by 3% to 5% in three of the 5 tasks and is competitive in the others. |
Graph Neural Networks for Multiparallel Word Alignment (2022.findings-acl)
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| Challenge: | Generally, word alignment algorithms only use bitext and do not make use of the fact that many parallel corpora are multiparallel. |
| Approach: | They propose a multiparallel word alignment graph and graph neural networks to exploit it . they add and remove edges from the initial alignments and generalize the model . |
| Outcome: | The proposed method outperforms previous work on three word alignment datasets and on a downstream task. |
REAM♯: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation (2021.findings-acl)
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| Challenge: | Existing evaluation metrics for open-domain dialogue systems are limited by the diversity of possible outcomings. |
| Approach: | They propose a method to augment a reference set to improve reliability . they propose BLEU to measure similarity between a predicted response and a small set of references . |
| Outcome: | The proposed model improves the reliability of reference-based metrics with augmented reference sets. |
Expanding Abbreviations in a Strongly Inflected Language: Are Morphosyntactic Tags Sufficient? (L18-1)
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| Challenge: | In this paper, the problem of recovery of morphological information lost in abbreviated forms is addressed . correct inflected form of expanded abbrevation can be deduced from context words . |
| Approach: | They propose a deep bidirectional LSTM network with tag embedding to predict abbreviated words . they train on 10 million words from the Polish Sejm Corpus and achieve 74.2% prediction accuracy . |
| Outcome: | The proposed model achieves 74.2% accuracy on a smaller but more general corpus of Polish words. |
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. |
Towards More Fine-grained and Reliable NLP Performance Prediction (2021.eacl-main)
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| Challenge: | Performance prediction is a task of estimating a system’s performance without performing experiments. |
| Approach: | They propose to understand reliability of performance prediction models from two angles: confidence intervals and calibration. |
| Outcome: | The proposed methods demonstrate the feasibility of fine-grained performance prediction and the necessity to perform reliability analysis for performance prediction methods in the future. |
Hybrid Preferences: Learning to Route Instances for Human vs. AI Feedback (2025.acl-long)
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Lester James Validad Miranda, Yizhong Wang, Yanai Elazar, Sachin Kumar, Valentina Pyatkin, Faeze Brahman, Noah A. Smith, Hannaneh Hajishirzi, Pradeep Dasigi
| Challenge: | Learning from human feedback has enabled the alignment of language models (LMs) with human preferences. |
| Approach: | They propose a Hybrid Preference routER that defers an annotation to either humans or LMs, achieving better annotation quality while reducing the cost of human-only annotation. |
| Outcome: | The proposed model achieves better annotation quality while reducing the cost of human-only annotation. |
Detecting Causal Language Use in Science Findings (D19-1)
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| Challenge: | Prior studies on identifying inappropriate use of causal language relied on manual content analysis, which is not scalable for examining a large volume of science publications. |
| Approach: | They developed a prediction model that classifies conclusion sentences into “no relationship”, “correlational”, “conditional causal” and “direct causal” categories. |
| Outcome: | The proposed model can be used to identify the inappropriate use of causal language in scientific publications and news articles. |
Detecting Health Advice in Medical Research Literature (2021.emnlp-main)
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| Challenge: | Health and medical researchers often give clinical and policy recommendations to inform health practice and public health policy. |
| Approach: | They developed a BERT-based prediction model that can predict whether a sentence gives strong advice, weak advice, or not. |
| Outcome: | The proposed model can predict whether a sentence gives strong advice, weak advice, or not with a macro-averaged F1 score of 0.93. |
Zero-shot Visual Question Answering with Language Model Feedback (2023.findings-acl)
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| Challenge: | Existing methods for knowledge-based visual question answering are based on pre-trained language models. |
| Approach: | They propose a language model guided captioning approach that leverages a pre-trained language model to generate captions for an image to help answer a visual question. |
| Outcome: | The proposed method outperforms several competing methods on the knowledge-based VQA task and achieves comparable results to a fine-tuned VLP model. |
Exploring the Value of Personalized Word Embeddings (2020.coling-main)
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| Challenge: | a subset of words belonging to specific psycholinguistic categories vary more in their representations across users . combining generic and personalized word embeddings yields the best performance . |
| Approach: | They propose personalized word embeddings and compare their performance to generic ones . they show that personalized word representations can be leveraged for improved performance . |
| Outcome: | The proposed model can be used for authorship attribution. |
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture (2023.findings-emnlp)
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Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo Wang
| Challenge: | Existing low-resource learning techniques focus on label annotation while neglecting the natural language explanation of a data point. |
| Approach: | They propose a novel architecture that leverages an explanation-generation model to produce explanations guided by human explanations and a prediction model that utilizes generated explanations toward prediction faithfully. |
| Outcome: | The proposed architecture produces explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a data diversity-based AL sampling strategy that benefits from the explanation annotations. |