Compositional Zero-Shot Learning (CZSL) aims at recognizing novel compositions by combining concepts learned from seen compositions. The key to tackle CZSL is disentangling highly coupled attribute-object compositions and learning exclusive concepts. Previous works mainly design networks to learn visual concepts from top-layer representations provided by visual backbones. As visual backbones progressively integrate information layer by layer, some low-level but critical information for concept learning may be lost, and the coupling between attribute and object features deepens. To address these issues, we propose to extract multi-scale visual features and fuse them in an adaptive way by Mixture of Experts (MoE) networks. We also employ feature-level similarity and a maximum entropy regularization term to constrain the model to effectively disentangle and learn concepts from multi-scale visual information. Comprehensive experiments on three CZSL benchmark datasets demonstrate that our method significantly outperforms previous SOTA methods in both closed-world and open-world settings.

Duplicate Question Identification (DQI) improves the processing efficiency and accuracy of large-scale community question answering and automatic QA system. The purpose of DQI task is to identify whether the paired questions are semantically equivalent. However, how to distinguish the synonyms or homonyms in paired questions is still challenging. Most previous works focus on the word-level or phrase-level semantic differences. We firstly propose to explore the asking emphasis of a question as a key factor in DQI. Asking emphasis bridges semantic equivalence between two questions. In this paper, we propose an attention model with multi-fusion asking emphasis (MFAE) for DQI. At first, BERT is used to obtain the dynamic pre-trained word embeddings. Then we get inter- and intra-asking emphasis by summing inter-attention and self-attention, respectively; the idea is that, the more a word interacts with others, the more important the word is. Finally, we use eight-way combinations to generate multi-fusion asking emphasis and multi-fusion word representation. Experimental results demonstrate that our model achieves state-of-the-art performance on both Quora Question Pairs and CQADupStack data. In addition, our model can also improve the results for natural language inference task on SNLI and MultiNLI datasets. The code is available at https://github.com/rzhangpku/MFAE.