Bailin Wang

TL;DR: This work presents a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder and achieves the new state-of-the-art performance on the Spider leaderboard.

TL;DR: The U NIFIED SKG framework is proposed, which unifies 21 SKG tasks into a text-to-text format, aiming to promote systematic SKG research, instead of being exclu-sive to a single task, domain, or dataset.

TL;DR: GraPPa is an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data and significantly outperforms RoBERTa-large as the feature representation layers and establishes new state-of-the-art results on all of them.

TL;DR: This work proposes a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets and shows that the model built on top of such a new representation is able to capture features and interactions that cannot be captured by previous models while maintaining a low time complexity for inference.

TL;DR: A segmentation attention based LSTM model which can effectively capture the structural dependencies between the target and the sentiment expressions with a linear-chain conditional random field (CRF) layer is proposed.