4 accepted papers
Pre-trained encoder-only and sequence-to-sequence (seq2seq) models each have advantages, however training both model types from scratch is computationally expensive. We explore recipes to improve pre-training efficiency by initializing one model from the other. (1) Extracting the encoder from a seq2…
We present a hybrid approach for product review summarization which consists of: (i) an unsupervised extractive step to extract the most important sentences out of all the reviews, and (ii) a supervised abstractive step to summarize the extracted sentences into a coherent short summary. This approac…
In conversational AI agents, Query Rewriting (QR) plays a crucial role in reducing user frictions and satisfying their daily demands. User frictions are caused by various reasons, such as errors in the conversational AI system, users’ accent or their abridged language. In this work, we present a nov…
We present LINGUIST, a method for generating annotated data for Intent Classification and Slot Tagging (IC+ST), via fine-tuning AlexaTM 5B, a 5-billion-parameter multilingual sequence-to-sequence (seq2seq) model, on a flexible instruction prompt. In a 10-shot novel intent setting for the SNIPS datas…