One state-of-the-art model for semantic parsing is our recently introduced <cite>relaxed hybrid tree</cite> model <cite>(Lu, 2014)</cite> , which performs integrated lexicon acquisition and semantic parsing within a single framework utilizing efficient algorithms for training and inference. Unfortunately, the <cite>relaxed hybrid tree</cite> model has an important limitation: <cite>it</cite> essentially does not allow certain sentence-semantics pairs to be jointly encoded using the proposed <cite>relaxed hybrid tree</cite> representations. Thus, <cite>the model</cite> is unable to identify joint representations for certain sentence-semantics pairs during the training process, and is unable to produce desired outputs for certain inputs during the evaluation process. In this work, we propose a solution addressing the above limitation, which makes our model more robust.