Query-Based Retrieval Using Universal Sentence Encoder

Abstract

In Natural language processing, various tasks can be implemented with the features provided by word embeddings. But for obtaining embeddings for larger chunks like sentences, the efforts applied through word embeddings will not be sufficient. To resolve such issues sentence embeddings can be used. In sentence embeddings, complete sentences along with their semantic information are represented as vectors so that the machine finds it easy to understand the context. In this paper, we propose a Question Answering System (QAS) based on sentence embeddings. Our goal is to obtain the text from the provided context for a user-query by extracting the sentence in which the correct answer is present. Traditionally, infersent models have been used on SQUAD for building QAS. In recent times, Universal Sentence Encoder with USECNN and USETrans have been developed. In this paper, we have used another variant of the Universal sentence encoder, i.e. Deep averaging network in order to obtain pre-trained sentence embeddings. The results on the SQUAD-2.0 dataset indicate our approach (USE with DAN) performs well compared to Facebook’s infersent embedding.

Depression Detection Model Usingword and Sentence Embedding With Different Classifiers

“…Even though the second one requires less processing, it is less accurate. A 512-dimensional vector is produced when different lengths of English text are provided [24]. Sentences were encoded into embedding vectors using two alternative methods.…”