DADA : Dialect Adaptation via Dynamic Aggregation of Linguistic Rules (2023)

Abstract: Existing large language models (LLMs) that mainly focus on Standard American English (SAE) often lead to significantly worse performance when being applied to other English dialects. While existing mitigations tackle discrepancies for individual target dialects, they assume access to high-accuracy dialect identification systems. The boundaries between dialects are inherently flexible, making it difficult to categorize language into discrete predefined categories. In this paper, we propose DADA (Dialect Adaptation via Dynamic Aggregation), a modular approach to imbue SAE-trained models with multi-dialectal robustness by composing adapters which handle specific linguistic features. The compositional architecture of DADA allows for both targeted adaptation to specific dialect variants and simultaneous adaptation to various dialects. We show that DADA is effective for both single task and instruction finetuned language models, offering an extensible and interpretable framework for adapting existing LLMs to different English dialects.

## introduction as natural language processing (nlp) becomes even more impactful, the equitable distribution of its benefits becomes an increasing concern. specifically, nlp tooling is often trained and evaluated on dominant language variants, such as standard american english (sae). this results in a significant decline in the performance when these tools are applied to non-sae dialects. studies have revealed that sae models tested on african american vernacular english (aave) encounter difficulties in language identification @xcite as well as various other natural language tasks @xcite drop_aux: aave allows copula deletion and other auxiliary dropping. ## dada we introduce dialect adaptation via dynamic aggregation (dada), a modular method for adapting an existing model trained on the standard american english (sae) to accommodate dialect variants at a finer-grained level. our proposed method deploys a dynamic aggregation of feature adapters, which characterize the divergence of linguistic features between sae and its dialect variants. specifically, dada involves the creation of a synthetic training dataset for each individual feature using transformation rules @xcite . these synthetic datasets are used to train respective adapters for each linguistic feature. finally, we compose these feature adapters to create a single model via an additional fusion layer. ## multi-dialect adaptation in this section, we demonstrate how dada can enable the adaptation of an existing sae model to multiple dialect variants, taking multi-genre natural language inference (mnli; williams et al. ( 2018 )) task as an example. ## interpretability as discussed in sec. 3, dada can implicitly identify the relevant linguistic features for a given input and activate the corresponding feature adapters. we validate this by investigating the correlation between attention scores within each layer of dada and the presence of linguistic features, to determine whether the contributing feature adapters are relevant to the features present. ## conclusion in this paper, we present dialect adaptation via dynamic aggregation (dada), a fine-grained and modular approach designed to adapt an established model trained on standard american english to its dialect variants through the compositional aggregation of linguistic features. our experiments demonstrate that the compositionality of dada en-ables targeted adaptation to specific dialects, and demonstrated improved robustness across multiple evaluated dialects, including appe, chce, collsge, inde, and aave. our analysis also highlights the interpretability of dada, as shown through its capability to identify relevant linguistic features for a given input and trigger the corresponding adapters. furthermore, our experiments on flan-t5 illustrate the potential of applying dada to taskagnostic instruction-tuned large language models, showcasing its generalizability.