TensorFlow has announced a new on-device embedding-based search library feature that allows one to quickly find similar images, text or audio from millions of data samples in a few milliseconds. It works by using a model to embed the search query into a high-dimensional vector representing the semantic meaning of the query. Then it uses ScaNN (Scalable Nearest Neighbors) to search for similar items from a predefined database. Given below is a walkthrough of an end-to-end example of building a text-to-image search feature (retrieve the images given textual queries) using the new TensorFlow Lite Searcher Library. The dual encoder model consists of an image encoder and a text encoder.

This work shows how to improve and interpret the commonly used dual encoder model for response suggestion in dialogue. We present an attentive dual encoder model that includes an attention mechanism on top of the extracted word-level features from two encoders, one for context and one for label respectively. To improve the interpretability in the dual encoder models, we design a novel regularization loss to minimize the mutual information between unimportant words and desired labels, in addition to the original attention method, so that important words are emphasized while unimportant words are de-emphasized. This can help not only with model interpretability, but can also further improve model accuracy. We propose an approximation method that uses a neural network to calculate the mutual information. Furthermore, by adding a residual layer between raw word embeddings and the final encoded context feature, word-level interpretability is preserved at the final prediction of the model. We compare the proposed model with existing methods for the dialogue response task on two public datasets (Persona and Ubuntu). The experiments demonstrate the effectiveness of the proposed model in terms of better Recall@1 accuracy and visualized interpretability.