Predicting Correlations Between Lexical Alignments and Semantic Inferences

Robust Incremental Neural Semantic Graph Parsing

Parsing sentences to linguistically-expressive semantic representations is a key goal of Natural Language Processing. Yet statistical parsing has focused almost exclusively on bilexical dependencies or domain-specific lo…

Context-Aware Attention Network for Image-Text Retrieval

As a typical cross-modal problem, image-text bi-directional retrieval relies heavily on the joint embedding learning and similarity measure for each image-text pair. It remains challenging because prior works seldom expl…

DSS: Text Similarity Using Lexical Alignments of Form, Distributional Semantics and Grammatical Relations

On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries

Large-scale semantic parsing datasets annotated with logical forms have enabled major advances in supervised approaches. But can richer supervision help even more? To explore the utility of fine-grained, lexical-level su…

Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention

We present a neural encoder-decoder AMR parser that extends an attention-based model by predicting the alignment between graph nodes and sentence tokens explicitly with a pointer mechanism. Candidate lemmas are predicted…