2019/11/01 by Ning Miao, Miao, Ning, Hao Zhou +7
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Topic Modeling #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.1911.00274
arxiv created 2019/11/01 · openalex publication_date 2019/11/01 · arxiv updated 2019/11/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Neural models for text generation require a softmax layer with proper token embeddings during the decoding phase. Most existing approaches adopt single point embedding for each token. However, a word may have multiple senses according to different context, some of which might be distinct. In this paper, we propose KerBS, a novel approach for learning better embeddings for text generation. KerBS embodies two advantages: (a) it employs a Bayesian composition of embeddings for words with multiple senses; (b) it is adaptive to semantic variances of words and robust to rare sentence context by imposing learned kernels to capture the closeness of words (senses) in the embedding space. Empirical studies show that KerBS significantly boosts the performance of several text generation tasks.