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NLP models excel on tasks with clean inputs, but are less accurate with noisyinputs. In particular, character-level noise such as human-written typos andadversarially-engineered realistic-looking misspellings often appears in textand can easily trip up NLP models. Prior solutions to address character-levelnoise often alter the content of the inputs (low fidelity), thus inadvertentlylowering model accuracy on clean inputs. We proposed FiRo, an approach to boostNLP model performance on noisy inputs without sacrificing performance on cleaninputs. FiRo sanitizes the input text while preserving its fidelity byinferring the noise-free form for each token in the input. FiRo usesfinite-context aggregation to obtain contextual embeddings which is then usedto find the noise-free form within a restricted output space. The output spaceis restricted to a small cluster of probable candidates in order to predict thenoise-free tokens more accurately. Although the clusters are small, FiRo'seffective vocabulary (union of all clusters) can be scaled up to betterpreserve the input content. Experimental results show NLP models that use FiRooutperforming baselines on six classification tasks and one sequence labelingtask at various degrees of noise.

Finite-context Indexing of Restricted Output Space for NLP Models Facing Noisy Input