This paper describes a neural-network model which performed competitively
(top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS)
task. Our system employs an attention-based recurrent neural network model that
optimizes the sentence similarity. In this paper, we describe our participation
in the multilingual STS task which measures similarity across English, Spanish,
and Arabic.

本文介绍了一种基于注意力机制的循环神经网络模型，该模型在 2017 年 SemEval 跨语言语义文本相似度（STS）任务中取得了竞争性表现（前六名），并描述了我们参加跨语言 STS 任务的情况，该任务涉及英语、西班牙语和阿拉伯语的相似度比较。

SemEval-2017 任务 1 中的 Neobility: 一种基于注意力机制的句子相似性模型

Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity  Model

Recent progress in neural learning demonstrated that machines can do well in
regularized tasks, e.g., the game of Go. However, artistic activities such as
poem generation are still widely regarded as human's special capability. In
this paper, we demonstrate that a simple neural model can imitate human in some
tasks of art generation. We particularly focus on traditional Chinese poetry,
and show that machines can do as well as many contemporary poets and weakly
pass the Feigenbaum Test, a variant of Turing test in professional domains. Our
method is based on an attention-based recurrent neural network, which accepts a
set of keywords as the theme and generates poems by looking at each keyword
during the generation. A number of techniques are proposed to improve the
model, including character vector initialization, attention to input and
hybrid-style training. Compared to existing poetry generation methods, our
model can generate much more theme-consistent and semantic-rich poems.

本文通过基于注意力机制的循环神经网络，利用一组关键字作为主题，生成类似于人类古诗词的诗歌，方法包括字符向量初始化、注重输入和混合式训练等技术。相比于现有的诗歌生成方法，我们的模型生成的诗歌与主题更加一致，语义更加丰富。