Question answering (QA) over text passages is a problem of long-standing
interest in information retrieval. Recently, the conversational setting has
attracted attention, where a user asks a sequence of questions to satisfy her
information needs around a topic. While this setup is a natural one and similar
to humans conversing with each other, it introduces two key research
challenges: understanding the context left implicit by the user in follow-up
questions, and dealing with ad hoc question formulations. In this work, we
demonstrate CROWN (Conversational passage ranking by Reasoning Over Word
Networks): an unsupervised yet effective system for conversational QA with
passage responses, that supports several modes of context propagation over
multiple turns. To this end, CROWN first builds a word proximity network (WPN)
from large corpora to store statistically significant term co-occurrences. At
answering time, passages are ranked by a combination of their similarity to the
question, and coherence of query terms within: these factors are measured by
reading off node and edge weights from the WPN. CROWN provides an interface
that is both intuitive for end-users, and insightful for experts for
reconfiguration to individual setups. CROWN was evaluated on TREC CAsT data,
where it achieved above-median performance in a pool of neural methods.

该研究旨在解决目前有关与话题相关的关于文本段落的会话式问答中存在的两个研究挑战。该文提出了一种无监督但有效的系统，名为 CROWN，它使用大型语料库构建一个单词相似性网络，并通过该网络将上下文引入返回的段落中，以对问答进行排名，并在 TREC CAsT 数据中实现中等以上表现。

通过利用词接近网络实现对篇章的对话问答

Conversational Question Answering over Passages by Leveraging Word  Proximity Networks

We propose a direct-to-word sequence model which uses a word network to learn
word embeddings from letters. The word network can be integrated seamlessly
with arbitrary sequence models including Connectionist Temporal Classification
and encoder-decoder models with attention. We show our direct-to-word model can
achieve word error rate gains over sub-word level models for speech
recognition. We also show that our direct-to-word approach retains the ability
to predict words not seen at training time without any retraining. Finally, we
demonstrate that a word-level model can use a larger stride than a sub-word
level model while maintaining accuracy. This makes the model more efficient
both for training and inference.

通过字级别的单词网络，我们提出了一种直接生成单词嵌入的序列模型，这种直接生成单词的方法可以有效提高语音识别中的词义判断准确率，并且可以更高效地进行训练和推理。