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Multitask learning has been proven a useful technique in a number of Natural Language Processing applications where data is scarce and naturally diverse. Examples include learn- ing from data of …...

AbstractMeasuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in a static setting and unsupervised manner. However, …...

AbstractWe present open domain dialogue generation with meta-words. A meta-word is a structured record that describes attributes of a response, and thus allows us to explicitly model the one-to-many relationship …...

AbstractBilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion. However, they require large batch sizes, which becomes a performance bottleneck when training on large …...

AbstractAlthough neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data. Recently, pre-trained language models show potential in …...

AbstractFraming analysis is predominantly qualitative and quantitative, examining a small dataset with manual coding. Easy access to digital data in the last two decades prompts scholars in both computation and …...

AbstractRecent language generative models are mostly trained on large-scale datasets, while in some real scenarios, the training datasets are often expensive to obtain and would be small-scale. In this paper …...