RecipeMeta: Metapath-enhanced Recipe Recommendation on Heterogeneous Recipe Network

Recipe is a set of instructions that describes how to make food. It can help people from the preparation of ingredients, food cooking process, etc. to prepare the food, and increasingly in demand on the Web. To help users find the vast amount of recipes on the Web, we address the task of recipe recommendation. Due to multiple data types and relationships in a recipe, we can treat it as a heterogeneous network to describe its information more accurately. To effectively utilize the heterogeneous network, metapath was proposed to describe the higher-level semantic information between two entities by defining a compound path from peer entities. Therefore, we propose a metapath-enhanced recipe recommendation framework, RecipeMeta, that combines GNN (Graph Neural Network)-based representation learning and specific metapath-based information in a recipe to predict User-Recipe pairs for recommendation. Through extensive experiments, we demonstrate that the proposed model, RecipeMeta, outperforms state-of-the-art methods for recipe recommendation

Neighborhood Matching Network for Entity Alignment

Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge. NMN estimates the similarities between entities to capture both the topological structure and the neighborhood difference. It provides two innovative components for better learning representations for entity alignment. It first uses a novel graph sampling method to distill a discriminative neighborhood for each entity. It then adopts a cross-graph neighborhood matching module to jointly encode the neighborhood difference for a given entity pair. Such strategies allow NMN to effectively construct matching-oriented entity representations while ignoring noisy neighbors that have a negative impact on the alignment task. Extensive experiments performed on three entity alignment datasets show that NMN can well estimate the neighborhood similarity in more tough cases and significantly outperforms 12 previous state-of-the-art methods.Comment: 11 pages, accepted by ACL 202

RGAT: A Deeper Look into Syntactic Dependency Information for Coreference Resolution

Although syntactic information is beneficial for many NLP tasks, combining it with contextual information between words to solve the coreference resolution problem needs to be further explored. In this paper, we propose an end-to-end parser that combines pre-trained BERT with a Syntactic Relation Graph Attention Network (RGAT) to take a deeper look into the role of syntactic dependency information for the coreference resolution task. In particular, the RGAT model is first proposed, then used to understand the syntactic dependency graph and learn better task-specific syntactic embeddings. An integrated architecture incorporating BERT embeddings and syntactic embeddings is constructed to generate blending representations for the downstream task. Our experiments on a public Gendered Ambiguous Pronouns (GAP) dataset show that with the supervision learning of the syntactic dependency graph and without fine-tuning the entire BERT, we increased the F1-score of the previous best model (RGCN-with-BERT) from 80.3% to 82.5%, compared to the F1-score by single BERT embeddings from 78.5% to 82.5%. Experimental results on another public dataset - OntoNotes 5.0 demonstrate that the performance of the model is also improved by incorporating syntactic dependency information learned from RGAT.Comment: 8 pages, 5 figure