Recently, there has been an increasing interest in unsupervised parsers that
optimize semantically oriented objectives, typically using reinforcement
learning. Unfortunately, the learned trees often do not match actual syntax
trees well. Shen et al. (2018) propose a structured attention mechanism for
language modeling (PRPN), which induces better syntactic structures but relies
on ad hoc heuristics. Also, their model lacks interpretability as it is not
grounded in parsing actions. In our work, we propose an imitation learning
approach to unsupervised parsing, where we transfer the syntactic knowledge
induced by the PRPN to a Tree-LSTM model with discrete parsing actions. Its
policy is then refined by Gumbel-Softmax training towards a semantically
oriented objective. We evaluate our approach on the All Natural Language
Inference dataset and show that it achieves a new state of the art in terms of
parsing $F$-score, outperforming our base models, including the PRPN.

我们提出了一种模仿学习的无监督句法分析方法，将 PRPN 诱导的句法知识转移到带有离散句法分析操作的 Tree-LSTM 模型上，并通过 Gumbel-Softmax 训练不断优化其策略以实现更好的语义目标。在 All-NLI 数据集上进行实验，证明我们的方法在句法得分上超过了基准模型，包括 PRPN。

一种基于模仿学习的无监督解析方法

An Imitation Learning Approach to Unsupervised Parsing

Discourse structure is integral to understanding a text and is helpful in
many NLP tasks. Learning latent representations of discourse is an attractive
alternative to acquiring expensive labeled discourse data. Liu and Lapata
(2018) propose a structured attention mechanism for text classification that
derives a tree over a text, akin to an RST discourse tree. We examine this
model in detail, and evaluate on additional discourse-relevant tasks and
datasets, in order to assess whether the structured attention improves
performance on the end task and whether it captures a text's discourse
structure. We find the learned latent trees have little to no structure and
instead focus on lexical cues; even after obtaining more structured trees with
proposed model modifications, the trees are still far from capturing discourse
structure when compared to discourse dependency trees from an existing
discourse parser. Finally, ablation studies show the structured attention
provides little benefit, sometimes even hurting performance.

通过结构化的注意力机制来获得文本的 RST 语篇树结构是一种学习蕴含语篇结构的潜在表示的有吸引力的替代方法。通过评估其他与语篇相关的任务和数据集后，我们发现这种方法并不能很好地捕捉文本的语篇结构，即使模型做了一些结构性的改进，也不能和基于现有语篇分析器的语篇依存树相提并论。最后，消融研究表明，结构化的注意力机制甚至有时会损害性能。

结构化文本表示中的话语评估

Evaluating Discourse in Structured Text Representations

Pronouns are often dropped in Chinese sentences, and this happens more
frequently in conversational genres as their referents can be easily understood
from context. Recovering dropped pronouns is essential to applications such as
Information Extraction where the referents of these dropped pronouns need to be
resolved, or Machine Translation when Chinese is the source language. In this
work, we present a novel end-to-end neural network model to recover dropped
pronouns in conversational data. Our model is based on a structured attention
mechanism that models the referents of dropped pronouns utilizing both
sentence-level and word-level information. Results on three different
conversational genres show that our approach achieves a significant improvement
over the current state of the art.

本文介绍了一种基于结构化关注机制的端到端神经网络模型，用于恢复中文会话数据中省略代词的指代。通过实验结果表明，该模型能够在三种不同类型的会话数据上显著提高省略代词恢复的效果。