No training required: Exploring random encoders for sentence classification

This post introduces a novel baseline for sentence embeddings by exploring random encoders that require no additional training. It demonstrates that much of the performance gain in sentence embeddings comes from pre-trained word representations, and that random parameterizations can match or exceed the performance of established sentence encoders like SkipThought and InferSent. The work also provides recommendations for proper experimental protocol in sentence classification evaluation.