Papers with interpretable model
It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons (2024.eacl-long)
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| Challenge: | a new model to score subjective bias in documents is developed to perform pairwise comparisons . a recent study shows that the model can be explained and validated for other domains based on the training data. |
| Approach: | They propose an interpretable model to score subjective bias in Wikipedia articles . they train the model on pairs of revisions of the same Wikipedia article . |
| Outcome: | The proposed model can interpret parameters to discover words most indicative of bias . it compares legal texts, news media and law amendments in three settings . |
ARNOR: Attention Regularization based Noise Reduction for Distant Supervision Relation Classification (P19-1)
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| Challenge: | Distant supervision is used for relation classification but it introduces noisy labels . a novel approach to distant supervision relation classification is proposed . |
| Approach: | They propose a framework for distant supervision relation classification using attention regularization and attention regularizing . they assume that a trustable relation label should be explained by the neural attention model . |
| Outcome: | The proposed framework improves on the NYT data and noise reduction effect over state-of-the-art methods. |
Through the MUD: A Multi-Defendant Charge Prediction Benchmark with Linked Crime Elements (2024.acl-long)
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| Challenge: | Existing charge prediction datasets focus on single-defendant cases, but real-world cases involve multiple defendants. |
| Approach: | They propose a benchmark that encompasses legal cases involving multiple defendants . they develop an interpretable model called EJudge that incorporates crime elements and legal rules to infer charges. |
| Outcome: | The proposed model outperforms state-of-the-art models in predicting crime charges while providing corresponding rationales. |
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing (2020.findings-emnlp)
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| Challenge: | Using neural machine translation to approximate human parity is difficult due to the lack of parallel training corpora. |
| Approach: | They propose an end-to-end deep learning framework for quality estimation and automatic post-editing of machine translation output. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the English–German dataset and human translators can significantly expedite their post-editing processing with the model. |
Analyzing Semantic Change through Lexical Replacements (2024.acl-long)
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| Challenge: | Modern language models can contextualize words based on their surrounding contexts, but semantic change can compromise this capability. |
| Approach: | They propose a replacement schema where a target word is replaced with lexical replacements of varying relatedness . they leverage the replacement schema as a basis for a novel interpretable model for semantic change . |
| Outcome: | The proposed model is the first to evaluate LLaMa for semantic change detection . it shows that lexical replacements can detect unexpected contexts . |
SenteCon: Leveraging Lexicons to Learn Human-Interpretable Language Representations (2023.findings-acl)
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| Challenge: | In many settings, it is important to understand a model’s decision-making process. |
| Approach: | They propose a method for introducing human interpretability in deep language representations by encoding a passage of text as a layer of interpretable categories. |
| Outcome: | The proposed method outperforms existing interpretable language representations on downstream tasks and on agreement with human characterizations of the text. |
An Adaptive Logical Rule Embedding Model for Inductive Reasoning over Temporal Knowledge Graphs (2022.emnlp-main)
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| Challenge: | Existing methods for temporal knowledge graphs (TKGs) are incomplete and therefore lack interpretability. |
| Approach: | They propose an interpretable temporal knowledge graph reasoning model that captures deep causal logic by learning rule embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models on the ICEWS14, ICEW0515 and ICEw18 datasets. |
Intrinsic Subgraph Generation for Interpretable Graph Based Visual Question Answering (2024.lrec-main)
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| Challenge: | Visual Question Answering (VQA) is acknowledged as a challenging multi-modal task for Machine Learning (ML). |
| Approach: | They propose an interpretable approach for graph-based Visual Question Answering . their model is designed to intrinsically produce a subgraph during the question-answering process as its explanation . |
| Outcome: | The proposed model outperforms existing explainable methods on a graph-based VQA dataset. |