Papers with decision rule
Unobserved Local Structures Make Compositional Generalization Hard (2022.emnlp-main)
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| Challenge: | Recent studies show sequence-to-sequence models struggle to generalize to new compositions . little is known on what makes generalization hard on a particular test instance . |
| Approach: | They propose a criterion for the difficulty of an example that is hard if it contains a local structure that was not observed at training time. |
| Outcome: | The proposed rule predicts instance-level generalization well across 5 different datasets. |
Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation (2020.coling-main)
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| Challenge: | Recent studies have revealed a number of pathologies of neural machine translation systems. |
| Approach: | They propose to use maximum a posteriori decoding to identify the highest-scoring translation, i.e. the mode problem, to validate the model and its training algorithm. |
| Outcome: | The proposed model reproduces the statistical data well, but the beam search strays from the statistics. |
Supervised and Unsupervised Probing of Shortcut Learning: Case Study on the Emergence and Evolution of Syntactic Heuristics in BERT (2025.findings-acl)
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| Challenge: | Contemporary language models (LMs) rely on shortcut learning, using superficial cues that are spuriously correlated with labels. |
| Approach: | They propose to use syntactic heuristics to learn shortcuts in BERT when performing a task in Natural Language Understanding to investigate where these shortcuts emerge, how they evolve and how they impact the latent knowledge of the LM. |
| Outcome: | The proposed model rely on syntactic heuristics when performing a task in Natural Language Understanding. |
Thought calibration: Efficient and confident test-time scaling (2025.emnlp-main)
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| Challenge: | Existing methods for teaching language models to be economical with their token budgets have failed to achieve the desired results. |
| Approach: | They propose to calibrate a language model's growing body of thoughts to determine when new reasoning plateaus. |
| Outcome: | The proposed framework preserves model performance with up to 60% reduction in thinking tokens on in-distribution data, and up to 20% in out-of-difference data. |
Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing methods to improve beam search quality are inadequate in many ways . a new approximation to the beam search curse has been proposed . |
| Approach: | They propose an approximation to minimum Bayes risk decoding that would solve the beam search curse. |
| Outcome: | The proposed approximation has no equivalent to the beam search curse. |