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Conversational understanding is an integral part of modern intelligentdevices. In a large fraction of the global traffic from customers using smartdigital assistants, frictions in dialogues may be attributed to incorrectunderstanding of the entities in a customer's query due to factors includingambiguous mentions, mispronunciation, background noise and faulty on-devicesignal processing. Such errors are compounded by two common deficiencies fromintelligent devices namely, (1) the device not being tailored to individualcustomers, and (2) the device responses being unaware of the context in theconversation session. Viewing this problem via the lens of retrieval-basedsearch engines, we build and evaluate a scalable entity correction system,PENTATRON. The system leverages a parametric transformer-based language modelto learn patterns from in-session customer-device interactions coupled with anon-parametric personalized entity index to compute the correct query, whichaids downstream components in reasoning about the best response. In addition toestablishing baselines and demonstrating the value of personalized andcontext-aware systems, we use multitasking to learn the domain of the correctentity. We also investigate the utility of language model prompts. Throughextensive experiments, we show a significant upward movement of the key metric(Exact Match) by up to 500.97% (relative to the baseline).

PENTATRON: PErsonalized coNText-Aware Transformer for Retrieval-based cOnversational uNderstanding