In conventional supervised training, a model is trained to fit all the
training examples. However, having a monolithic model may not always be the
best strategy, as examples could vary widely. In this work, we explore a
different learning protocol that treats each example as a unique pseudo-task,
by reducing the original learning problem to a few-shot meta-learning scenario
with the help of a domain-dependent relevance function. When evaluated on the
WikiSQL dataset, our approach leads to faster convergence and achieves
1.1%-5.4% absolute accuracy gains over the non-meta-learning counterparts.

本文提出一种基于元学习及相关性函数的 few-shot 元学习方法，将原先监督学习的每个样本视为一个独立的伪任务，经过实验验证，该方法在 WikiSQL 数据集上取得较好的成果。

基于元学习的自然语言转结构化查询

Natural Language to Structured Query Generation via Meta-Learning

Online reviews are often our first port of call when considering products and
purchases online. When evaluating a potential purchase, we may have a specific
query in mind, e.g. `will this baby seat fit in the overhead compartment of a
747?' or `will I like this album if I liked Taylor Swift's 1989?'. To answer
such questions we must either wade through huge volumes of consumer reviews
hoping to find one that is relevant, or otherwise pose our question directly to
the community via a Q/A system.
In this paper we hope to fuse these two paradigms: given a large volume of
previously answered queries about products, we hope to automatically learn
whether a review of a product is relevant to a given query. We formulate this
as a machine learning problem using a mixture-of-experts-type framework---here
each review is an `expert' that gets to vote on the response to a particular
query; simultaneously we learn a relevance function such that `relevant'
reviews are those that vote correctly. At test time this learned relevance
function allows us to surface reviews that are relevant to new queries
on-demand. We evaluate our system, Moqa, on a novel corpus of 1.4 million
questions (and answers) and 13 million reviews. We show quantitatively that it
is effective at addressing both binary and open-ended queries, and
qualitatively that it surfaces reviews that human evaluators consider to be
relevant.

使用混合专家框架，本文旨在自动学习如何判断一个产品评论是否与给定查询相关，并在一个包括 1.4 百万个问题和答案以及 13 百万个评论的语料库上评估我们的系统，Moqa，并表明它对于处理二元和开放性查询都有效，在定性研究中还展示了这个系统能够提供人工评估认为相关的评论。