In many machine learning tasks, a model needs to be presented with both correct and incorrect examples during the training. For instance, given a query, a search engine can be trained to predict the relevant (positive) documents from irrelevant (negative) ones. While each query is associated with a handful of relevant documents, the number of irrelevant documents can be vast. This imbalance can bias a document retrieval model while the mere volume of irrelevant documents can result in long training times. In this paper, we show the affect of a tailored negative sampling on the performance of the Deep Structure Semantic Model (DSSM). We show that a naive random sampling method outperforms more sophisticated ways of selecting negative data.
REF studies the affect of a tailored sample strategy on the performance of document retrieval task.
The Effect of Negative Sampling Strategy on Capturing Semantic Similarity in Document Embeddings.
SemDeep@IWCS
Computer Science