In this study, we investigate interaction-based neural matching models for
ad-hoc cross-lingual information retrieval (CLIR) using cross-lingual word
embeddings (CLWEs). With experiments conducted on the CLEF collection over four
language pairs, we evaluate and provide insight into different neural model
architectures, different ways to represent query-document interactions and
word-pair similarity distributions in CLIR. This study paves the way for
learning an end-to-end CLIR system using CLWEs.

通过研究交互式神经匹配模型，结合多语言词向量，为跨语言信息检索建立端到端系统提供了途径。

跨语言 IR 的神经匹配模型研究

A Study of Neural Matching Models for Cross-lingual IR

The recent boom of AI has seen the emergence of many human-computer
conversation systems such as Google Assistant, Microsoft Cortana, Amazon Echo
and Apple Siri. We introduce and formalize the task of predicting questions in
conversations, where the goal is to predict the new question that the user will
ask, given the past conversational context. This task can be modeled as a
"sequence matching" problem, where two sequences are given and the aim is to
learn a model that maps any pair of sequences to a matching probability. Neural
matching models, which adopt deep neural networks to learn sequence
representations and matching scores, have attracted immense research interests
of information retrieval and natural language processing communities. In this
paper, we first study neural matching models for the question retrieval task
that has been widely explored in the literature, whereas the effectiveness of
neural models for this task is relatively unstudied. We further evaluate the
neural matching models in the next question prediction task in conversations.
We have used the publicly available Quora data and Ubuntu chat logs in our
experiments. Our evaluations investigate the potential of neural matching
models with representation learning for question retrieval and next question
prediction in conversations. Experimental results show that neural matching
models perform well for both tasks.

本论文介绍了利用深度神经网络训练的神经匹配模型来解决提问检索和会话中的下一个问题预测的任务，并进行了实验和评估，证明神经匹配模型在这两个任务中表现良好。