Text matching systems have become a fundamental service in most Searching platforms. For instance, they are responsible for matching user queries to relevant candidate items, or rewriting the user-input query to a pre-selected high-performing one for a better search experience. In practice, both the queries and items often contain multiple attributes, such as the category of the item and the location mentioned in the query, which represent condensed key information that is helpful for matching. However, most of the existing works downplay the effectiveness of attributes by integrating them into text representations as supplementary information. Hence, in this work, we focus on exploring the relationship between the attributes from two sides. Since attributes from two ends are often not aligned in terms of number and type, we propose to exploit the benefit of attributes by multiple-intent modeling. The intents extracted from attributes summarize the diverse needs of queries and provide rich content of items, which are more refined and abstract, and can be aligned for paired inputs. Concretely, we propose a multi-intent attribute-aware matching model (MIM), which consists of three main components: attribute-aware encoder, multi-intent modeling, and intent-aware matching. In the attribute-aware encoder, the text and attributes are weighted and processed through a scaled attention mechanism with regard to the attributes' importance. Afterward, the multi-intent modeling extracts intents from two ends and aligns them. Herein, we come up with a distribution loss to ensure the learned intents are diverse but concentrated, and a kullback-leibler divergence loss that aligns the learned intents. Finally, in the intent-aware matching, the intents are evaluated by a self-supervised masking task, and then incorporated to output the final matching result. Extensive experiments on three real-world datasets from different matching scenarios show that MIM significantly outperforms state-of-the-art matching baselines. MIM is also tested by online A/B test, which brings significant improvements over three business metrics in query rewriting and query-item relevance tasks compared with the online baseline in Alipay App.

Due to the growing importance of using graph neural networks in high-stakes applications, there is a pressing need to interpret the predicted results of these models. Existing methods for explanation have mainly focused on generating sub-graphs comprising important edges for a specific prediction. However, these methods face two issues. Firstly, they lack counterfactual validity as removing the subgraph may not affect the prediction, and generating plausible counterfactual examples has not been adequately explored. Secondly, they cannot be extended to heterogeneous graphs as the complex information involved in such graphs increases the difficulty of generating interpretations. This paper proposes a novel counterfactual learning method, named CF-HGExplainer, for heterogeneous graphs. The method incorporates a semantic-aware attentive pooling strategy for the heterogeneous graph classifier and designs a heterogeneous decision boundaries extraction module to find the common logic for similar graphs based on the extracted graph embeddings from the classifier. Additionally, we propose to greedily perturb nodes and edges based on the distribution of node features and edge plausibility to train a neural network for heterogeneous edge weight learning. Extensive experiments on two public academic datasets demonstrate the effectiveness of CF-HGExplainer compared to state-of-the-art methods on the graph classification task and graph interpretation task.

Molecular graph generation via deep generative models has attracted increasing attention. This is a challenging problem because it requires optimizing a given objective under a huge search space while obeying the chemical valence rules. Although recently developed molecular generation models have achieved promising results on generating novel, valid and unique molecules, few efforts have been made toward interpretable molecular graph generation. In this work, we propose DEMO, a flow-based model for DisEntangled Molecular graph generatiOn in a completely unsupervised manner, which is able to generate molecular graphs w.r.t. the learned disentangled latent factors that are relevant to molecular semantic features and interpretable structural patterns. Specifically, DEMO is composed of a VAE-encoder and a flow-generator. The VAE-encoder focuses on extracting global features of molecular graphs, and the flow-generator aims at disentangling these features to be corresponding to certain types of understandable molecular structure features while learning data distributions. To generate molecular graphs, DEMO simply runs the flow-generator in the reverse order due to the reversibility of the flow-based models. Extensive experimental results on two benchmark datasets demonstrate that DEMO outperforms the state-of-the-art methods in molecular generation, and takes the first step in interpretable molecular graph generation.

Generating novel molecules with desired properties is a fundamental problem in modern drug discovery. This is a challenging problem because it requires the optimization of the given objectives while obeying the rules of chemical valence. An effective approach is to incorporate the molecular graph with deep generative models. However, recent generative models with high-performance are still computationally expensive. In this paper, we propose GF-VAE, a flow-based variational autoencoder (VAE) model for molecular graph generation. Specifically, the model equips VAE a lightweight flow model as its decoder, in which, the encoder aims to accelerate the training process of the decoder, while the decoder in turns to optimize the performance of the encoder. Thanks to the invertibility of flow model, the generation process is easily accomplished by reversing the decoder. Additionally, the final generated molecules are processed by validity correction. Therefore, our GF-VAE inherits the advantages of both VAE and flow-based methods. We validate our model on molecule generation and reconstruction, smoothness of learned latent space, property optimization and constrained property optimization. The results show that our model achieves state-of-the-arts performance on these tasks. Moreover, the time performance of GF-VAE on two classical datasets can achieve 31.3% and 62.9% improvements separately than the state-of-the-art model.