Papers with dependency modeling
Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging? (2022.naacl-main)
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| Challenge: | Recent Part-Of-Speech (POS) induction models assume certain independence assumptions that do not hold in real languages. |
| Approach: | They propose a Masked Part-of-Speech Model (MPoSM) that can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction. |
| Outcome: | The proposed model can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction. |
Non-Autoregressive Translation by Learning Target Categorical Codes (2021.naacl-main)
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| Challenge: | Existing non-autoregressive text generation models still fall behind in translation quality . authors propose a model that learns implicitly categorical codes as latent variables . |
| Approach: | They propose a non-autoregressive Transformer model that implicitly categorizes latent variables into decoding . they find it improves translation quality by introducing more informative decoder inputs . |
| Outcome: | The proposed model achieves comparable or better performance in machine translation tasks than strong baselines. |
SLICEFORMER: Static Program Slicing Using Language Models With Dataflow-Aware Pretraining and Constrained Decoding (2026.acl-long)
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| Challenge: | Static program slicing is a software engineering technique for isolating code relevant to specific variables. |
| Approach: | They propose a new approach that reformulates static program slicing as a sequence-to-sequence task using small language models such as CodeT5+. |
| Outcome: | The proposed approach improves on Java and Python program slicing benchmarks with up to 22% gain in ExactMatch. |
CascadeFix: Multi-Location Program Repair via Cascading Planning and Generation (2026.findings-acl)
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| Challenge: | Existing methods for automating program repair face insufficient bug dependency modeling and inadequate global repair planning when addressing semantically complex multi-location bugs. |
| Approach: | They propose a multi-location automatic repair method via cascading planning and generation . they propose to model dependencies among bugs and cluster them to ensure rationality . |
| Outcome: | The proposed method resolves 84 multi-location bugs, achieving a 31% improvement over current methods. |
Lost in Decomposition: Analyzing and Mitigating the Limitations of Long Context Methods via Context Dependency (2026.findings-acl)
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| Challenge: | Existing workflow-based long context methods do not perform well on specific datasets . performance degradation is associated with the indiscriminate application of long context models . |
| Approach: | They propose a training-free adaptive routing strategy to improve long context large language models' robustness. |
| Outcome: | The proposed method can be generalized to all types of datasets, but performance degradation is a concern. |