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Knowl. Discov. Data"],"published-print":{"date-parts":[[2021,6,28]]},"abstract":"<jats:p>For guiding natural language generation, many semantic-driven methods have been proposed. While clearly improving the performance of the end-to-end training task, these existing semantic-driven methods still have clear limitations: for example, (i) they only utilize shallow semantic signals (e.g., from topic models) with only a single stochastic hidden layer in their data generation process, which suffer easily from noise (especially adapted for short-text etc.) and lack of interpretation; (ii) they ignore the sentence order and document context, as they treat each document as a bag of sentences, and fail to capture the long-distance dependencies and global semantic meaning of a document. To overcome these problems, we propose a novel semantic-driven language modeling framework, which is a method to learn a Hierarchical Language Model and a Recurrent Conceptualization-enhanced Gamma Belief Network, simultaneously. For scalable inference, we develop the auto-encoding Variational Recurrent Inference, allowing efficient end-to-end training and simultaneously capturing global semantics from a text corpus. Especially, this article introduces concept information derived from high-quality lexical knowledge graph Probase, which leverages strong interpretability and anti-nose capability for the proposed model. Moreover, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence concept dependence. Experiments conducted on several NLP tasks validate the superiority of the proposed approach, which could effectively infer meaningful hierarchical concept structure of document and hierarchical multi-scale structures of sequences, even compared with latest state-of-the-art Transformer-based models.<\/jats:p>","DOI":"10.1145\/3451167","type":"journal-article","created":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T17:08:53Z","timestamp":1621444133000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Hierarchical Concept-Driven Language Model"],"prefix":"10.1145","volume":"15","author":[{"given":"Yashen","family":"Wang","sequence":"first","affiliation":[{"name":"China Academy of Electronics and Information Technology of CETC, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huanhuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Academy of Electronics and Information Technology of CETC, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhirun","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,5,19]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Neural machine translation by jointly learning to align and translate. 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