How do learners acquire languages from the limited data available to them?
This process must involve some inductive biases - factors that affect how a
learner generalizes - but it is unclear which inductive biases can explain
observed patterns in language acquisition. To facilitate computational modeling
aimed at addressing this question, we introduce a framework for giving
particular linguistic inductive biases to a neural network model; such a model
can then be used to empirically explore the effects of those inductive biases.
This framework disentangles universal inductive biases, which are encoded in
the initial values of a neural network's parameters, from non-universal
factors, which the neural network must learn from data in a given language. The
initial state that encodes the inductive biases is found with meta-learning, a
technique through which a model discovers how to acquire new languages more
easily via exposure to many possible languages. By controlling the properties
of the languages that are used during meta-learning, we can control the
inductive biases that meta-learning imparts. We demonstrate this framework with
a case study based on syllable structure. First, we specify the inductive
biases that we intend to give our model, and then we translate those inductive
biases into a space of languages from which a model can meta-learn. Finally,
using existing analysis techniques, we verify that our approach has imparted
the linguistic inductive biases that it was intended to impart.

介绍一种通过元学习来探索语言习得中归纳偏好的影响的框架，并展示了一个基于音节结构的案例研究来验证该方法传递了预期的语言归纳偏好。

元学习通过普遍的语言归纳偏好

Universal linguistic inductive biases via meta-learning

This paper presents a novel, syllable-structured Chinese lyrics generation
model given a piece of original melody. Most previously reported lyrics
generation models fail to include the relationship between lyrics and melody.
In this work, we propose to interpret lyrics-melody alignments as syllable
structural information and use a multi-channel sequence-to-sequence model with
considering both phrasal structures and semantics. Two different RNN encoders
are applied, one of which is for encoding syllable structures while the other
for semantic encoding with contextual sentences or input keywords. Moreover, a
large Chinese lyrics corpus for model training is leveraged. With automatic and
human evaluations, results demonstrate the effectiveness of our proposed lyrics
generation model. To the best of our knowledge, there is few previous reports
on lyrics generation considering both music and linguistic perspectives.

介绍了一种用于生成汉语歌词的模型，该模型基于原始旋律并考虑了汉语音节结构及语义。通过使用多通道序列到序列模型，同时考虑词组结构和语义，其中一个用于编码音节结构，另一个用于语义编码。使用大规模的汉语歌词语料库进行模型训练，并通过自动和人工评估验证了模型的有效性。是目前关于考虑音乐和语言的双重视角的汉语歌词生成较少的报告之一。