Span-based Neural Structured Prediction
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A long-standing goal in artificial intelligence is for machines to understand natural language. With ever-growing amounts of data in the world, it is crucial to automate many aspects of language understanding so that users can make sense of this data in the face of information overload. The main challenge stems from the fact that the surface form of language, either as speech or text, is unstructured. Without programmatic access to the semantics of natural language, it is challenging to build general, robust systems that are usable in practice. Towards achieving this goal, we propose a series of neural structured-prediction algorithms for natural language processing. In particular, we address a challenge common to all such algorithms: the space of possible output structures can be extremely large, and inference in this space can be intractable. Despite the seeming incompatibility of neural representations with dynamic programs from traditional structured prediction algorithms, we can leverage these rich representations to learn more accurate models while using simpler or lazier inference. We focus on algorithms that model the most basic substructure of language: spans of text. We present state-of-the-art models for tasks that require modeling the internal structure of spans, such as syntactic parsing, and modeling structure between spans, such as question answering and coreference resolution. The proposed techniques are applicable to many problems, and we expect that they will further push the limits of neural structured prediction for natural language processing.