Papers with linear model
COVID-19 and Misinformation: A Large-Scale Lexical Analysis on Twitter (2021.acl-srw)
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| Challenge: | Social media is used by individuals and organisations as a platform to spread misinformation. |
| Approach: | They compile a large corpus of tweets related to coronavirus and perform an analysis to discover patterns with respect to vocabulary usage. |
| Outcome: | The proposed model based on lexical features is effective in identifying misinformation-related tweets with accuracy over 80%. |
Hexatagging: Projective Dependency Parsing as Tagging (2023.acl-short)
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| Challenge: | Using a pretrained language model, we can train language models on increasingly large amounts of data. |
| Approach: | They propose a dependency parser that constructs dependency trees by tagging words with elements from a finite set of possible tags. |
| Outcome: | The proposed approach achieves state-of-the-art performance of 96.4 LAS and 97.4 UAS on the Penn Treebank test set. |
TuckER: Tensor Factorization for Knowledge Graph Completion (D19-1)
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| Challenge: | Knowledge graphs contain only a small subset of all possible facts . link prediction is a task of inferring missing facts based on existing facts - knowledge graphs are expensive and lack of information is needed to add new information. |
| Approach: | They propose a linear model based on Tucker decomposition of knowledge graph triples . they show that the model is expressive and has sufficient bounds on its embedding dimensionalities . |
| Outcome: | The proposed model outperforms state-of-the-art models across standard datasets and acts as a strong baseline for more elaborate models. |
Local Interpretation of Transformer Based on Linear Decomposition (2023.acl-long)
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| Challenge: | Existing work on local explanation generation attempts to understand model dynamics on word-level or phraselevel by assigning importance scores on input features. |
| Approach: | They propose to interpret neural networks by linear decomposition by a Transformer model on a single input and a linear decomposing of the output to generate local explanations. |
| Outcome: | The proposed method achieves competitive performance in sentiment classification and machine translation, and fidelity of explanation. |
BERT-Beta: A Proactive Probabilistic Approach to Text Moderation (2021.emnlp-main)
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| Challenge: | Existing approaches to text moderation are reactive and do not account for user generated content. |
| Approach: | They propose a text toxicity propensity model to characterize extent to which a user generated text attracts toxic comments and introduce a beta regression model to do the probabilistic modeling. |
| Outcome: | The proposed model performs well in comprehensive experiments and is scalable. |
LLM-induced Rationales for More Compact Explainable Style Classification Models (2026.findings-acl)
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| Challenge: | Existing methods for extracting explanations from complex models are based on discovering a large number of features, and this affects interpretability. |
| Approach: | They propose a model that leverages Large Language Models and clustering algorithms to discover a compact set of interpretable features. |
| Outcome: | The proposed model reduces the number of features on 3 Style Classification tasks by 85–99% while reducing the number by 85. |