Review Conversational Reading Comprehension

Xu Hu, Liu Bing, Shu Lei, Yu Philip S.. Arxiv 2019

[Paper] [Code]    
Agentic BERT Fine Tuning Has Code Model Architecture Pretraining Methods RAG Reinforcement Learning Survey Paper Training Techniques

Inspired by conversational reading comprehension (CRC), this paper studies a novel task of leveraging reviews as a source to build an agent that can answer multi-turn questions from potential consumers of online businesses. We first build a review CRC dataset and then propose a novel task-aware pre-tuning step running between language model (e.g., BERT) pre-training and domain-specific fine-tuning. The proposed pre-tuning requires no data annotation, but can greatly enhance the performance on our end task. Experimental results show that the proposed approach is highly effective and has competitive performance as the supervised approach. The dataset is available at \url{https://github.com/howardhsu/RCRC}

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