We develop a novel technique to parse English sentences into Abstract Meaning
Representation (AMR) using SEARN, a Learning to Search approach, by modeling
the concept and the relation learning in a unified framework. We evaluate our
parser on multiple datasets from varied domains and show an absolute
improvement of 2% to 6% over the state-of-the-art. Additionally we show that
using the most frequent concept gives us a baseline that is stronger than the
state-of-the-art for concept prediction. We plan to release our parser for
public use.

本文介绍了一种使用 SEARN 方法（一种基于学习的搜索方法），将英语句子解析成抽象的意义表示（AMR）的新技术，通过在统一框架中建立概念和关系学习。我们使用多个不同领域的数据集评估了我们的解析器，并显示其相对于最先进技术的绝对改进量在 2％到 6％之间。此外，我们还展示了最频繁的概念的使用给我们提供了比最先进技术更强的概念预测基线。我们计划为公众提供我们的解析器。

使用学习搜索技术的抽象意义表示解析器

Parser for Abstract Meaning Representation using Learning to Search

We present Searn, an algorithm for integrating search and learning to solve
complex structured prediction problems such as those that occur in natural
language, speech, computational biology, and vision. Searn is a meta-algorithm
that transforms these complex problems into simple classification problems to
which any binary classifier may be applied. Unlike current algorithms for
structured learning that require decomposition of both the loss function and
the feature functions over the predicted structure, Searn is able to learn
prediction functions for any loss function and any class of features. Moreover,
Searn comes with a strong, natural theoretical guarantee: good performance on
the derived classification problems implies good performance on the structured
prediction problem.

提出了 Searn，这是一种集成搜索和学习的算法，可以解决自然语言、语音、计算生物学和视觉中出现的复杂结构预测问题，是一种元算法，可以将这些复杂问题转换为简单的分类问题。