Representing documents into high dimensional embedding space while preserving
the structural similarity between document sources has been an ultimate goal
for many works on text representation learning. Current embedding models,
however, mainly rely on the availability of label supervision to increase the
expressiveness of the resulting embeddings. In contrast, unsupervised
embeddings are cheap, but they often cannot capture implicit structure in
target corpus, particularly for samples that come from different distribution
with the pretraining source.
Our study aims to loosen up the dependency on label supervision by learning
document embeddings via Sequence-to-Sequence (Seq2Seq) text generator.
Specifically, we reformulate keyphrase generation task into multi-label keyword
generation in community-based Question Answering (cQA). Our empirical results
show that KeyGen2Vec in general is superior than multi-label keyword classifier
by up to 14.7% based on Purity, Normalized Mutual Information (NMI), and
F1-Score metrics. Interestingly, although in general the absolute advantage of
learning embeddings through label supervision is highly positive across
evaluation datasets, KeyGen2Vec is shown to be competitive with classifier that
exploits topic label supervision in Yahoo! cQA with larger number of latent
topic labels.

我们的研究旨在通过使用序列到序列（Seq2Seq）文本生成器学习文档嵌入，以减少对标签监督的依赖，并将关键短语生成任务重新形式化为基于社区的问答（cQA）中的多标签关键词生成。实证结果显示，相对于多标签关键词分类器，KeyGen2Vec 在纯度、归一化互信息（NMI）和 F1 值指标上的整体优势高达 14.7％。有趣的是，尽管在评估数据集中，通过标签监督学习嵌入的绝对优势普遍是高度正面的，但在 Yahoo！cQA 上，使用大量潜在主题标签的分类器中，KeyGen2Vec 在竞争上表现出色。

KeyGen2Vec: 通过多标签关键词生成在问答中学习文档嵌入

KeyGen2Vec: Learning Document Embedding via Multi-label Keyword  Generation in Question-Answering

In this paper, we present a generative retrieval method for sponsored search
engine, which uses neural machine translation (NMT) to generate keywords
directly from query. This method is completely end-to-end, which skips query
rewriting and relevance judging phases in traditional retrieval systems.
Different from standard machine translation, the target space in the retrieval
setting is a constrained closed set, where only committed keywords should be
generated. We present a Trie-based pruning technique in beam search to address
this problem. The biggest challenge in deploying this method into a real
industrial environment is the latency impact of running the decoder.
Self-normalized training coupled with Trie-based dynamic pruning dramatically
reduces the inference time, yielding a speedup of more than 20 times. We also
devise an mixed online-offline serving architecture to reduce the latency and
CPU consumption. To encourage the NMT to generate new keywords uncovered by the
existing system, training data is carefully selected. This model has been
successfully applied in Baidu's commercial search engine as a supplementary
retrieval branch, which has brought a remarkable revenue improvement of more
than 10 percents.

本篇论文提出了一种使用神经机器翻译方法来直接从查询中生成关键字的检索方法，并采用基于 Trie 的修剪技术来解决目标空间为限制闭合集合的检索环境下的关键字生成问题。该方法被应用于百度商业搜索引擎作为辅助检索分支，使得收益提升 10% 以上。