Question generation (QG) is the task of generating a question from a
reference sentence and a specified answer within the sentence. A major
challenge in QG is to identify answer-relevant context words to finish the
declarative-to-interrogative sentence transformation. Existing
sequence-to-sequence neural models achieve this goal by proximity-based answer
position encoding under the intuition that neighboring words of answers are of
high possibility to be answer-relevant. However, such intuition may not apply
to all cases especially for sentences with complex answer-relevant relations.
Consequently, the performance of these models drops sharply when the relative
distance between the answer fragment and other non-stop sentence words that
also appear in the ground truth question increases. To address this issue, we
propose a method to jointly model the unstructured sentence and the structured
answer-relevant relation (extracted from the sentence in advance) for question
generation. Specifically, the structured answer-relevant relation acts as the
to the point context and it thus naturally helps keep the generated question to
the point, while the unstructured sentence provides the full information.
Extensive experiments show that to the point context helps our question
generation model achieve significant improvements on several automatic
evaluation metrics. Furthermore, our model is capable of generating diverse
questions for a sentence which conveys multiple relations of its answer
fragment.

该研究提出了一种新的问句生成方法，通过同时建模结构化的答案相关关系和非结构化的句子来生成准确简短的问题，在多项自动评估指标上实现了显著的提升，并可针对包含多个答案相关关系的句子生成多种问句。

利用关键背景提高问题生成

Improving Question Generation With to the Point Context

Yor\`ub\'a is a widely spoken West African language with a writing system
rich in tonal and orthographic diacritics. With very few exceptions, diacritics
are omitted from electronic texts, due to limited device and application
support. Diacritics provide morphological information, are crucial for lexical
disambiguation, pronunciation and are vital for any Yor\`ub\'a text-to-speech
(TTS), automatic speech recognition (ASR) and natural language processing (NLP)
tasks. Reframing Automatic Diacritic Restoration (ADR) as a machine translation
task, we experiment with two different attentive Sequence-to-Sequence neural
models to process undiacritized text. On our evaluation dataset, this approach
produces diacritization error rates of less than 5%. We have released
pre-trained models, datasets and source-code as an open-source project to
advance efforts on Yor\`ub\'a language technology.

本论文提出了两种不同的基于注意力机制的序列到序列神经网络模型来处理未带变音符的约鲁巴语文本，将自动变音符恢复重新定位为机器翻译任务。在评估数据集上，这种方法产生少于 5％的变音符错误率，并开源了预训练模型、数据集和源代码项目，以推进约鲁巴语言技术的努力。

基于注意力机制的序列到序列学习在 Yorùbá 语文本的音标恢复中的应用

Attentive Sequence-to-Sequence Learning for Diacritic Restoration of  Yorùbá Language Text

It has been shown that Chinese poems can be successfully generated by
sequence-to-sequence neural models, particularly with the attention mechanism.
A potential problem of this approach, however, is that neural models can only
learn abstract rules, while poem generation is a highly creative process that
involves not only rules but also innovations for which pure statistical models
are not appropriate in principle. This work proposes a memory-augmented neural
model for Chinese poem generation, where the neural model and the augmented
memory work together to balance the requirements of linguistic accordance and
aesthetic innovation, leading to innovative generations that are still
rule-compliant. In addition, it is found that the memory mechanism provides
interesting flexibility that can be used to generate poems with different
styles.

使用增强记忆的神经网络模型在中国古诗文的生成中，通过将神经网络和增强内存结合使用，来平衡规则与美学，实现符合规范和有创意的诗歌生成，并提供了生成不同风格诗歌的灵活性。