Copy mechanisms explicitly obtain unchanged tokens from the source (input)
sequence to generate the target (output) sequence under the neural seq2seq
framework. However, most of the existing copy mechanisms only consider single
word copying from the source sentences, which results in losing essential
tokens while copying long spans. In this work, we propose a plug-and-play
architecture, namely BioCopy, to alleviate the problem aforementioned.
Specifically, in the training stage, we construct a BIO tag for each token and
train the original model with BIO tags jointly. In the inference stage, the
model will firstly predict the BIO tag at each time step, then conduct
different mask strategies based on the predicted BIO label to diminish the
scope of the probability distributions over the vocabulary list. Experimental
results on two separate generative tasks show that they all outperform the
baseline models by adding our BioCopy to the original model structure.

本文介绍了一种名为 BioCopy 的插件式机制，通过在训练和推理阶段引入 BIO 标记和各种掩码策略提升了神经 seq2seq 框架下的输出准确性和生成任务的效果。

BioCopy：Seq2Seq 模型中即插即用的跨度复制机制

BioCopy: A Plug-And-Play Span Copy Mechanism in Seq2Seq Models

The neural seq2seq based question generation (QG) is prone to generating
generic and undiversified questions that are poorly relevant to the given
passage and target answer. In this paper, we propose two methods to address the
issue. (1) By a partial copy mechanism, we prioritize words that are
morphologically close to words in the input passage when generating questions;
(2) By a QA-based reranker, from the n-best list of question candidates, we
select questions that are preferred by both the QA and QG model. Experiments
and analyses demonstrate that the proposed two methods substantially improve
the relevance of generated questions to passages and answers.

通过部分复制和 QA-based reranker，我们提出了两种方法来提高神经序列到序列的问题生成模型的生成问题与给定段落和答案相关性的准确性。