Papers with predictor
Stylistic approaches to predicting Reddit popularity in diglossia (2021.acl-srw)
Copied to clipboard
| Challenge: | Past research has shown that style is a strong predictor of community response, but what about a diglossia? |
| Approach: | They propose to use punctuation, stopwords and part-of-speech tags to predict the popularity of a Reddit post in a diglossia in Singapore where the basilect co-exists with an acrolect . |
| Outcome: | The proposed approach combines natural language processing (NLP) techniques with punctuation, stopwords and part-of-speech tags to predict popular posts in a diglossia. |
Interlocking-free Selective Rationalization Through Genetic-based Learning (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to selective rationalization suffer from interlocking, a phenomenon known as interlock. |
| Approach: | They propose a genetically-based disjoint training architecture for selective rationalization that avoids interlocking by performing genetic global search. |
| Outcome: | The proposed model outperforms state-of-the-art models on a synthetic and real-world benchmark. |
Unsupervised Speech-text word-level alignment with Dynamic Programming (2025.findings-naacl)
Copied to clipboard
| Challenge: | Word-level alignment in speech-text pretraining models is limited by word-level annotated data . authors propose an iterative training method for USDP that reduces the dependency on scarce annotation resources. |
| Approach: | They propose an Unsupervised Speech-text word-level alignment with Dynamic Programming (USDP) this method uses Dynamic programming principles to iteratively refine temporal alignment predictions . |
| Outcome: | The proposed method significantly improves on speech-text pretraining tasks compared to existing methods. |
Reinforced Product Metadata Selection for Helpfulness Assessment of Customer Reviews (D19-1)
Copied to clipboard
| Challenge: | a helpful review is largely concerned with the metadata of its target product . a selector learns from both the key-value product metadata and one of its reviews to take an action . |
| Approach: | They propose a framework that uses product metadata to assess helpfulness of free-text reviews . they use two real-world datasets from amazon.com and Yelp.com to test the framework . |
| Outcome: | The proposed framework can achieve state-of-the-art performance with substantial improvements . it uses two real-world datasets from Amazon.com and Yelp.com . |
Reinforced Training Data Selection for Domain Adaptation (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to learn domains with massive data are not easy to implement and require a predefined threshold. |
| Approach: | They propose a framework that searches for training instances relevant to the target domain and learns better representations for them. |
| Outcome: | The proposed framework is effective in data selection and representation, but generalized to accommodate different NLP tasks. |
The Limitations of Limited Context for Constituency Parsing (2021.acl-long)
Copied to clipboard
| Challenge: | a language model that is syntax-aware can produce better samples, authors say . a recent study shows that neural approaches to syntax can perform unsupervised syntactic parsing . |
| Approach: | They propose to incorporate syntax into neural approaches in NLP to produce better samples . they find that the first time neural approaches were able to perform unsupervised syntactic parsing . |
| Outcome: | The proposed models can perform unsupervised syntactic parsing, but they are lagging behind . the proposed models are based on a sandbox of probabilistic context-free-grammars . |
LiGen: Active Lipid Generation via a Molecular Language Model (2026.acl-long)
Copied to clipboard
| Challenge: | Lipid nanoparticles (LNPs) can deliver cargos to tumor and immune cells . traditional approaches rely on experimental screening and expert judgment . |
| Approach: | They propose a method to generate lipid molecules efficiently and actively using deep learning. |
| Outcome: | The proposed method outperforms baseline methods on multiple cell lines and achieves a 30% improvement over the current methods. |
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control (D19-1)
Copied to clipboard
| Challenge: | Selective rationalization is a common mechanism to ensure that predictive models reveal how they use any available features. |
| Approach: | They propose a co-operative method which uses introspection to explicitly predict and incorporate the outcome into the selection process. |
| Outcome: | The proposed model maintains high predictive accuracy and leads to comprehensive rationales. |
RikiNet: Reading Wikipedia Pages for Natural Question Answering (2020.acl-main)
Copied to clipboard
| Challenge: | Using Wikipedia pages to answer open-domain questions remains challenging in natural language understanding. |
| Approach: | They propose a model which reads Wikipedia pages for natural question answering . it uses a dynamic paragraph dual-attention reader and a cascaded answer predictor . |
| Outcome: | The proposed model outperforms the human model on the Natural Questions dataset . it achieves 74.3 F1 and 57.9 F1 on long-answer and short-answer tasks . |
The Distribution and Prosodic Realization of Verb Forms in German Infant-Directed Speech (L18-1)
Copied to clipboard
| Challenge: | Infant-directed speech is often seen as a predictor for infants' speech processing abilities, for instance speech segmentation or word learning. |
| Approach: | They examine the syntactic distribution, accentuation and prosodic phrasing of German verb forms and show that many verb forms are prime candidates for early segmentation. |
| Outcome: | The findings suggest that infants ought to be able to extract verbs as early as nouns, given appropriate stimulus materials. |
Unsupervised Selective Rationalization with Noise Injection (2023.acl-long)
Copied to clipboard
| Challenge: | Unsupervised selective rationalization produces rationales alongside predictions, but does not ensure that the rationale contains a plausible explanation for the prediction. |
| Approach: | They propose a technique that injects noise between a rationale generator and a predictor to limit generation of implausible rationales. |
| Outcome: | The proposed method achieves significant improvements in plausibility and task accuracy over the state-of-the-art models while maintaining or improving model faithfulness. |
MGR: Multi-generator Based Rationalization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to explain NLP models have two key challenges: spurious correlation and degeneration. |
| Approach: | They propose a rationalization framework using a generator and a predictor to construct a self-explaining NLP model with spurious correlation and degeneration as key challenges. |
| Outcome: | The proposed method improves the F1 score by 20.9% compared to state-of-the-art methods. |
Predicting Performance for Natural Language Processing Tasks (2020.acl-main)
Copied to clipboard
| 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. |
Learning to Select: Query-Aware Adaptive Dimension Selection for Dense Retrieval (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for dense retrieval use pseudo-relevance feedback to model dimension importance . however, they learn global transformations shared across queries and do not model dimension-aware dimension importance. |
| Approach: | They propose a Query-Aware Adaptive Dimension Selection framework that learns to predict per-dimension importance directly from query embedding. |
| Outcome: | The proposed framework improves retrieval effectiveness over the full-dimensional and PRF-based models. |
On the Proper Treatment of Tokenization in Psycholinguistics (2024.emnlp-main)
Copied to clipboard
| Challenge: | Language models are used in computational psycholinguistics to test theories that relate the surprisal of a region of interest to its cognitive cost experienced by readers. |
| Approach: | They propose to marginalize token-level language models into character-level ones before they are used in psycholinguistic studies. |
| Outcome: | The proposed model over token strings is better than character-level model, the authors show . the proposed model marginalizes token-level models into character-based models before they are used in psycholinguistic studies. |
ShadowLLM: Predictor-based Contextual Sparsity for Large Language Models (2024.emnlp-main)
Copied to clipboard
Yash Akhauri, Ahmed AbouElhamayed, Jordan Dotzel, Zhiru Zhang, Alexander Rush, Safeen Huda, Mohamed Abdelfattah
| Challenge: | Prior work has focused on contextual sparsity, but it has not been successful. |
| Approach: | They propose a novel pruning predictor that can shadow the LLM behavior and enforce better sparsity patterns. |
| Outcome: | The proposed model can shadow the LLM behavior and enforce better sparsity patterns, resulting in 15% improvement in end-to-end accuracy compared to prior methods. |