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DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules

What it is about

DADA adapts a model trained on Standard American English to English dialects without a dialect classifier. For each Multi-VALUE rule (Ziems et al.), the paper generates a synthetic training set and trains one adapter; nearly 200 feature adapters are trained in all. A fusion layer then mixes them with attention at test time, and a null adapter keeps Standard American outputs unchanged. With RoBERTa Base on MNLI, the average gain over the Standard American baseline across five dialects is 2.16%; on AAVE alone the gain is 3.2% (86.6% accuracy), and on Standard American it is 1.4%. On the AAVE version of six GLUE tasks with FLAN-T5 Base, the mean rises from 77.59 to 79.39. The paper also finds that the fusion weights correlate with the rules present in the input, but only in layers 0 to 3. A ChatGPT prompt that asks it to act as a native speaker does not help, and it is worse than the plain prompt on all tasks evaluated.

Ideas

For Mova

Chapters: 5. Auxiliaries and modals and 11. The biblical style (rule sets for a historical variety), 37. Register and variation. Code: en/src/morph.rs and en/src/emode.rs (the closed table of forms, which is where a rule would fire), world/src/knowledge.rs for the school measure. Measures: per-rule firing agreement with hand marks; the null-rule ablation on standard text; the gain curve over an ordered rule list. Run twice and compare the output before claiming a gain.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.emnlp-main.850

@inproceedings{liu2023dada,
  title = {{DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules}},
  author = {Liu, Yanchen and Held, William and Yang, Diyi},
  booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
  year = {2023},
  doi = {10.18653/v1/2023.emnlp-main.850},
  url = {https://doi.org/10.18653/v1/2023.emnlp-main.850}
}

References

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