语言缺陷下多跳推理的可能性与局限性
On the Possibilities and Limitations of Multi-hop Reasoning Under Linguistic Imperfections
摘要：
用于语言理解的系统在克服涉及短语匹配或简单推理的任务中的语言缺陷方面已经变得非常强大。然而，随着推理步骤的增加，它们的准确性急剧下降。我们提出了第一个正式的框架来研究这种经验观察。它允许一个量化的数量和影响的模糊性，冗余，不完整性，和不准确的语言使用时，介绍了一个隐藏的概念空间。我们的想法是考虑两个相互关联的空间：一个概念意义空间是明确的，完整的，但隐藏的，和语言空间，捕捉一个嘈杂的接地的意义空间在一个语言的话-所有系统的水平，无论是神经或符号，操作。将此框架应用于一类特殊的多跳推理，即概念之间的关系图中的连通性问题，即使在这种简化的设置下，我们也会得到严格的直觉和不可能的结果。例如，如果查询需要一个中等大小的由于语义图中的跳数（对数），在基于语言的嘈杂图上运行的推理系统不可能正确地回答它。这突出了扩展到更广泛类别的推理问题和系统的基本障碍，并提出了替代路径：专注于通过更丰富的表示来对齐两个空间，然后再投资于具有许多跳的推理。
英文摘要：
Systems for language understanding have become remarkably strong at overcoming linguistic imperfections in tasks involving phrase matching or simple reasoning. Yet, their accuracy drops dramatically as the number of reasoning steps increases. We present the first formal framework to study such empirical observations. It allows one to quantify the amount and effect of ambiguity, redundancy, incompleteness, and inaccuracy that the use of language introduces when representing a hidden conceptual space. The idea is to consider two interrelated spaces: a conceptual meaning space that is unambiguous and complete but hidden, and a linguistic space that captures a noisy grounding of the meaning space in the words of a language---the level at which all systems, whether neural or symbolic, operate. Applying this framework to a special class of multi-hop reasoning, namely the connectivity problem in graphs of relationships between concepts, we derive rigorous intuitions and impossibility results even under this simplified setting. For instance, if a query requires a moderately large (logarithmic) number of hops in the meaning graph, no reasoning system operating over a noisy graph grounded in language is likely to correctly answer it. This highlights a fundamental barrier that extends to a broader class of reasoning problems and systems, and suggests an alternative path forward: focusing on aligning the two spaces via richer representations, before investing in reasoning with many hops.
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