Teacher:In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Teacher: Now, understand the problem? Solve this instance: Embedding words in a vector space has gained a lot of research attention in re-<lb>cent years. While state-of-the-art methods provide efficient computation of word<lb>similarities via a low-dimensional matrix embedding, their motivation is often left<lb>unclear. In this paper, we argue that word embedding can be naturally viewed<lb>as a ranking problem. Then, based on this insight, we propose a novel frame-<lb>work WordRank that efficiently estimates word representations via robust ranking.<lb>The performance of WordRank is measured in word similarity and word analogy<lb>benchmarks, and the results are compared to the state-of-the-art word embedding<lb>techniques. Our algorithm produces a vector space with meaningful substructure,<lb>as evidenced by its performance of 77.4% accuracy on a popular word similarity<lb>benchmark and 76% on the Google word analogy benchmark. WordRank per-<lb>forms especially well on small corpora.
Student:
WordRank: Learning Word Embeddings via Robust Ranking