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A true interpreting agent not only understands sign language and translatesto text, but also understands text and translates to signs. Much of the AI workin sign language translation to date has focused mainly on translating fromsigns to text. Towards the latter goal, we propose a text-to-sign translationmodel, SignNet, which exploits the notion of similarity (and dissimilarity) ofvisual signs in translating. This module presented is only one part of adual-learning two task process involving text-to-sign (T2S) as well assign-to-text (S2T). We currently implement SignNet as a single channelarchitecture so that the output of the T2S task can be fed into S2T in acontinuous dual learning framework. By single channel, we refer to a singlemodality, the body pose joints.  In this work, we present SignNet, a T2S task using a novel metric embeddinglearning process, to preserve the distances between sign embeddings relative totheir dissimilarity. We also describe how to choose positive and negativeexamples of signs for similarity testing. From our analysis, we observe thatmetric embedding learning-based model perform significantly better than theother models with traditional losses, when evaluated using BLEU scores. In thetask of gloss to pose, SignNet performed as well as its state-of-the-art (SoTA)counterparts and outperformed them in the task of text to pose, by showingnoteworthy enhancements in BLEU 1 - BLEU 4 scores (BLEU 1: 31->39; ~26%improvement and BLEU 4: 10.43->11.84; ~14\% improvement) when tested on thepopular RWTH PHOENIX-Weather-2014T benchmark dataset

SignNet: Single Channel Sign Generation using Metric Embedded Learning