Papers with regression models
Dead or Murdered? Predicting Responsibility Perception in Femicide News Reports (2022.aacl-main)
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| Challenge: | linguistic expressions of gender-based violence can conceptualize the same event from different perspectives by emphasizing certain participants over others. |
| Approach: | They conduct a large-scale perception survey of GBV descriptions from italian newspapers and train regression models that predict the salience of GV participants with respect to different dimensions of perceived responsibility. |
| Outcome: | The proposed model shows that salient focus is more predictable than salient blame, and perpetrators’ salience is more predictable than victims’ salient. |
Predicting Machine Translation Performance on Low-Resource Languages: The Role of Domain Similarity (2024.findings-eacl)
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Eric Khiu, Hasti Toossi, Jinyu Liu, Jiaxu Li, David Anugraha, Juan Flores, Leandro Roman, A. Seza Doğruöz, En-Shiun Lee
| Challenge: | Existing approaches for predicting the performance of NLP models for low-resource languages (LRLs) focus on high-resourced languages, overlooking LRLs and domain shifts. |
| Approach: | They investigate the impact of domain similarity on predicting performance of machine translation models in low-resource languages. |
| Outcome: | The results show that domain similarity has the most important impact on predicting the performance of Machine Translation models. |
FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition (2026.findings-eacl)
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| Challenge: | Named entity recognition (NER) is the task of identifying tokens that belong to a predefined set of classes such as "person" or "location" |
| Approach: | They propose a dataset-creation pipeline that scales the teacher-student paradigm to 91 languages and 25 scripts. |
| Outcome: | The proposed model achieves comparable or improved performance in English, Thai, and Swahili despite being trained on 19x less data than strong baselines. |
Acquiring a Formality-Informed Lexical Resource for Style Analysis (2021.eacl-main)
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| Challenge: | lexico-statistics analysis of formality levels in written communication has long been dominated by application concerns, such as authorship and plagiarism assignment problems. |
| Approach: | They propose a lexicon with entries ordered by their degree of (in)formality and let crowdworkers assess the enlarged set of lexical items on a continuous informal-formal scale as a gold standard for evaluation. |
| Outcome: | The proposed lexicon is evaluated on a German-language email corpus and is then evaluated by crowdworkers. |
Towards Interactive Annotation for Hesitation in Conversational Speech (2020.lrec-1)
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| Challenge: | recognizing affects in spontaneous, non acted speech is expensive in both human resources and time. |
| Approach: | They aim to automatize the labeling of hesitant speech as a marker of expressed uncertainty. |
| Outcome: | The proposed method shows that the number of filled pauses and vowel duration increases with the degree of hesitation, and that automatic prediction of the hesitation degree reaches encouraging RMSE results of 1.6. |
DA-Pred: Performance Prediction for Text Summarization under Domain-Shift and Instruct-Tuning (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) often don’t perform as expected under Domain Shift or after Instruct-tuning. |
| Approach: | They propose a method that uses the known performance in high-resource domains and fine-tuning settings to predict performance in low-resourced domains or base models. |
| Outcome: | The proposed method can help researchers decide if resources should be allocated for data labeling and LLM Instruct-tuning. |
Dating Greek Papyri with Text Regression (2023.acl-long)
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John Pavlopoulos, Maria Konstantinidou, Isabelle Marthot-Santaniello, Holger Essler, Asimina Paparigopoulou
| Challenge: | a large number of Greek papyri documents can only be dated tentatively or in approximation due to the lack of decisive evidence. |
| Approach: | a new study trains regression models to estimate Greek papyri's date using a dataset of 389 transcriptions . the authors propose a method to estimate the date of 159 Greek pamphlets, which are only the upper limit known . |
| Outcome: | a new study predicts a date for Greek papyri with an average MAE of 54 years and an MSE of 1.17 . the model outperforms image classifiers and other baselines for 159 manuscripts, with only the upper limit known . |
Predicting Performance for Natural Language Processing Tasks (2020.acl-main)
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| Challenge: | Natural language processing (NLP) is a vast field, with a wide variety of tasks, languages, and domains. |
| Approach: | They build regression models to predict evaluation score of an NLP experiment . they find that their models can produce meaningful predictions over unseen languages . |
| Outcome: | The proposed model outperforms baseline models and human experts on 9 different tasks. |
When Instructions Multiply: Measuring and Estimating LLM Capabilities of Multiple Instructions Following (2025.findings-emnlp)
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Keno Harada, Yudai Yamazaki, Masachika Taniguchi, Edison Marrese-Taylor, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo
| Challenge: | a large number of languages are increasingly used to evaluate their ability to follow multiple instructions simultaneously. |
| Approach: | They propose two benchmarks to evaluate LLMs' ability to follow multiple instructions simultaneously . they use many instruction-following eval and style-aware Mostly Basic programming problems . |
| Outcome: | The proposed models predict performance on unseen instruction combinations and not used during training with 10% error. |
Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models (2025.acl-long)
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| Challenge: | Existing methods to optimize language model pre-training data mixtures are difficult due to the complexity of the data mixture. |
| Approach: | They propose a method to optimize language model pre-training data mixtures by approximating cross-entropy loss via a Mixture of Data Experts (MDE). |
| Outcome: | The proposed method improves performance on a slimPajama dataset with a mixture of data experts. |
Beyond Averages: Learning with Annotator Disagreement in STS (2025.emnlp-main)
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| Challenge: | Existing approaches to capture and model disagreement in Semantic Textual Similarity (STS) ignore label dispersion and incentivize models to ignore uncertainty crucial for practical settings. |
| Approach: | They propose to capture and model disagreement in Semantic Textual Similarity (STS) a lightweight truncated Gaussian head and a cross-encoder are used to model disagreement . |
| Outcome: | The proposed approach improves accuracy and calibration of models to human judgments. |
Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment (2026.acl-long)
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| Challenge: | Existing LAM benchmarks with thousands of examples create substantial computational barriers. |
| Approach: | They examine whether subsets can reliably evaluate large audio models . they find that subset of 50 examples can achieve over 0.93 Pearson correlation with full benchmark . |
| Outcome: | The proposed method outperforms the full benchmark and subset selection methods. |