Attention networks have proven to be an effective approach for embedding categorical
inference within a deep neural network. However, for many tasks we may want to model …

Transfer and multi-task learning have traditionally focused on either a single source-target
pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and …

This paper deals with a challenging task of video scene graph generation (VidSGG), which
could serve as a structured video representation for high-level understanding tasks. We …

Sentence pair modeling is a crucial problem in the field of natural language processing. In
this paper, we propose a model to measure the similarity of a sentence pair focusing on the …

If we want a future where AI serves a plurality of interests, then we should pay attention to
the factors that drive its success. While others have studied the importance of data …

In Natural Language Processing (NLP), we often need to extract information from tree
topology. Sentence structure can be represented via a dependency tree or a constituency …

Sentence similarity modeling lies at the core of many natural language processing
applications, and thus has received much attention. Owing to the success of word …

Metalearning of deep neural network (DNN) architectures and hyperparameters has
become an increasingly important area of research. Loss functions are a type of …

This paper proposes a patent citation classification model based on deep learning, and
collects the patent datasets in text analysis and communication area from Google patent …

Dependency analysis can assist neural networks to capture semantic features within a
sentence for entity relation extraction (RE). Both hard and soft strategies of encoding …