Papers with prediction quality
COMBO: State-of-the-Art Morphosyntactic Analysis (2021.emnlp-demo)
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| Challenge: | COMBO is an end-to-end NLP system for accurate part-of-speech tagging, morphological analysis, and (enhanced) dependency parsing. |
| Approach: | They propose a fully neural NLP system for accurate part-of-speech tagging, morphological analysis, lemmatisation, and (enhanced) dependency parsing. |
| Outcome: | The proposed system predicts categorical morphosyntactic features whilst also exposes their vector representations, extracted from hidden layers. |
The Larger they are, the Harder they Fail: Language Models do not Recognize Identifier Swaps in Python (2023.findings-acl)
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| Challenge: | Large Language Models (LLMs) are used for programming tasks but lack a deep understanding of the content they manipulate. |
| Approach: | They show that LLMs fail to correctly generate correct Python code when default function names are swapped . they also show that they become more confident in their incorrect predictions as the model size increases . |
| Outcome: | The proposed models fail to generate correct Python code when default function names are swapped, and become more confident in their incorrect predictions as the model size increases. |
Empirical Sufficiency Lower Bounds for Language Modeling with Locally-Bootstrapped Semantic Structures (2023.starsem-1)
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| Challenge: | a recent attempt at language modeling with predicted semantic structure failed to establish empirical lower bounds on what could have made the attempt successful. |
| Approach: | They propose a concise binary vector representation of semantic structure at the lexical level and evaluate how good an incremental tagger needs to be to achieve better-than-baseline performance. |
| Outcome: | The proposed model can achieve better-than-baseline performance without losing its main advantages and lower bounds on prediction quality can't be established via a single score alone. |
Intent Detection in the Age of LLMs (2024.emnlp-industry)
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| Challenge: | Traditional approaches to intent detection struggle with out-of-scope (OOS) detection. |
| Approach: | They propose to use adaptive in-context learning and chain-of-thought prompting to detect intent in SOTA LLMs. |
| Outcome: | The proposed system achieves 2% of native accuracy with 50% less latency. |
A Bayesian Optimization Approach to Machine Translation Reranking (2025.naacl-long)
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| Challenge: | reranking is a method of improving prediction quality but can add computational cost. |
| Approach: | They propose to score a list of prediction candidates with an external scoring model and return the highest-scoring candidate. |
| Outcome: | The proposed method achieves the same CometKiwi score using 70 evaluations on average compared to scoring a subset of 180 candidates. |
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects (D18-1)
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| Challenge: | a confound exists in time series data that violates assumptions of linear models . time series may violate assumptions through temporal diffusion . |
| Approach: | They propose a statistical model that borrows from digital signal processing to fit latent impulse response functions of arbitrary shape. |
| Outcome: | The proposed model recovers true latent IRFs and improves prediction quality . it is based on a new technique that borrows from digital signal processing . |
Unveiling Intrinsic Dimension of Texts: from Academic Abstract to Creative Story (2026.eacl-long)
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Pedashenko Vladislav, Laida Kushnareva, Yana Khassan Nibal, Eduard Tulchinskii, Kristian Kuznetsov, Vladislav Zharchinskii, Yury Maximov, Irina Piontkovskaya
| Challenge: | a new study grounding intrinsic dimension in interpretable text properties is published . entropy-like measures are ubiquitous in training and evaluation, but geometric complexity remains underexplored. |
| Approach: | They propose to ground intrinsic dimension (ID) in interpretable text properties through cross-encoder analysis, linguistic features, and sparse autoencodes. |
| Outcome: | The proposed method shows that scientific prose shows low ID ( 8), encyclopedic content medium ID ( > 9), creative/opinion writing high ID (> 10.5) |
Debiasing Multi-Entity Aspect-Based Sentiment Analysis with Norm-Based Data Augmentation (2024.lrec-main)
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| Challenge: | Recent research has explored strategies for reduce measurable biases in NLP predictions while maintaining prediction accuracy on held-out test sets. |
| Approach: | They propose to augment training data with norm-based language templates derived from previous language resources to reduce biases in NLP models. |
| Outcome: | The proposed model reduces topical bias to less than half while maintaining prediction quality on held-out test sets. |
Referring Image Segmentation via Joint Mask Contextual Embedding Learning and Progressive Alignment Network (2023.emnlp-main)
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| Challenge: | Existing methods for referring image segmentation may encounter limitations in maintaining focus on relevant information during specific stages and rectifying errors propagated from early stages. |
| Approach: | They propose a network that integrates a Learnable Contextual Embedding module and a Progressive Alignment Network to enhance the cascade framework. |
| Outcome: | The proposed network achieves state-of-the-art results on three commonly used benchmarks. |
ForestCast: Open-Ended Event Forecasting with Semantic News Forest (2025.findings-emnlp)
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| Challenge: | Existing approaches and datasets overlook the complex relationships among events . current research lacks comprehensive evaluation methods to evaluate OEEF . |
| Approach: | They propose a prediction pipeline that extracts forecast-relevant events from news data . forestcast organizes news events into a story tree and predicts subsequent events along each path . |
| Outcome: | The proposed pipeline extracts forecast-relevant events from news data and predicts subsequent events along each path. |
Towards Label-Agnostic Emotion Embeddings (2021.emnlp-main)
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| Challenge: | Existing representation schemes for emotion analysis are based on label formats, natural languages, and even disparate model architectures. |
| Approach: | They propose a training scheme that learns a shared latent representation of emotion independent from different label formats, natural languages, and even disparate model architectures. |
| Outcome: | The proposed model performs well on a wide range of datasets without penalizing prediction quality. |