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DEDD: Deep Encoder with Dual Decoder Architecture for Stability and Specificity Preserving Encoding and Translation of Embedding between Domains

Rajesh RanjanDebasmita DasRam Ganesh VYatin KatyalTanmoy Bhowmik

Mastercard AI Garage

摘要:We propose a deep learning-based encoder with a dual decoder system to enrich the expressive power of embeddings pre-trained on two different corpora along with switching representation between domains. There are two scenarios:(a) Each of the corpora is pertaining to the different subject matter or topic of interests and(b) One corpus is a vast super-domain with generic and non-specific embeddings while the second one pertains to one specific sub-domain. In either case, the criterion for high-quality training would be to have enough common words between them. The mapping of contextual embeddings from both the corpus into the common latent space blends the semantic richness of both the corpus-specific learning while maintaining embedding stability. Furthermore, there is one dedicated decoder for either of the domains for generating the representation from common latent space. We evaluated our method for cross-learning between generalized GLOVE embedding and a very specialized skill-embedding developed by random-walk on a graphbased Skills Hierarchy. We demonstrate that our method preserves the stability of the generic embedding, the specificity of the skill domain as well as enriches the semantic representation of either domain through switching enabled by the encoder-to-duel-decoder path.
会议名称:

2021 2nd International Conference on Computing, Networks and Internet of Things

会议时间:

2021-05-20

会议地点:

中国北京

  • 专辑:

    信息科技

  • 专题:

    无线电电子学; 自动化技术

  • DOI:

    10.26914/c.cnkihy.2021.013499

  • 分类号:

    TP18;TN76

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