Summarizing novel chapters is a difficult task due to the input length and
the fact that sentences that appear in the desired summaries draw content from
multiple places throughout the chapter. We present a pipelined
extractive-abstractive approach where the extractive step filters the content
that is passed to the abstractive component. Extremely lengthy input also
results in a highly skewed dataset towards negative instances for extractive
summarization; we thus adopt a margin ranking loss for extraction to encourage
separation between positive and negative examples. Our extraction component
operates at the constituent level; our approach to this problem enriches the
text with spinal tree information which provides syntactic context (in the form
of constituents) to the extraction model. We show an improvement of 3.71
Rouge-1 points over best results reported in prior work on an existing novel
chapter dataset.

采用皮线式提取性 - 生成性方法，提炼文本信息，生成对章节的摘要。利用成分级别的提取组件，以脊树信息实现句法呈现，并采用边界分数损失函数鼓励提取器分辨出正面实例和负面实例。该方法表现优于现有小说章节数据集上的之前研究工作。

使用脊柱树感知的子句内容选择的小说章节抽象摘要

Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection

End-to-end relation extraction aims to identify named entities and extract
relations between them. Most recent work models these two subtasks jointly,
either by casting them in one structured prediction framework, or performing
multi-task learning through shared representations. In this work, we present a
simple pipelined approach for entity and relation extraction, and establish the
new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC),
obtaining a 1.7%-2.8% absolute improvement in relation F1 over previous joint
models with the same pre-trained encoders. Our approach essentially builds on
two independent encoders and merely uses the entity model to construct the
input for the relation model. Through a series of careful examinations, we
validate the importance of learning distinct contextual representations for
entities and relations, fusing entity information early in the relation model,
and incorporating global context. Finally, we also present an efficient
approximation to our approach which requires only one pass of both entity and
relation encoders at inference time, achieving an 8-16$\times$ speedup with a
slight reduction in accuracy.

本研究提出了一种简单的流水线方法来进行实体和关系抽取，并在标准基准测试中确立了新的最佳结果，通过对实体和关系进行不同的上下文表示，融合实体信息来提高性能，同时在全局上下文中引入信息以提高准确性。

一种简单易行的实体和关系抽取方法

A Frustratingly Easy Approach for Entity and Relation Extraction

Task-oriented dialog (TOD) systems often need to formulate knowledge base
(KB) queries corresponding to the user intent and use the query results to
generate system responses. Existing approaches require dialog datasets to
explicitly annotate these KB queries -- these annotations can be time
consuming, and expensive. In response, we define the novel problems of
predicting the KB query and training the dialog agent, without explicit KB
query annotation. For query prediction, we propose a reinforcement learning
(RL) baseline, which rewards the generation of those queries whose KB results
cover the entities mentioned in subsequent dialog. Further analysis reveals
that correlation among query attributes in KB can significantly confuse memory
augmented policy optimization (MAPO), an existing state of the art RL agent. To
address this, we improve the MAPO baseline with simple but important
modifications suited to our task. To train the full TOD system for our setting,
we propose a pipelined approach: it independently predicts when to make a KB
query (query position predictor), then predicts a KB query at the predicted
position (query predictor), and uses the results of predicted query in
subsequent dialog (next response predictor). Overall, our work proposes first
solutions to our novel problem, and our analysis highlights the research
challenges in training TOD systems without query annotation.

本文提出了如何在没有知识库查询注释的情况下，使用强化学习和管道方法来训练任务导向型对话系统，并解决了相关问题，并通过分析强化学习算法的相关性和差异，突出了训练任务导向型对话系统的挑战。