Voice assistants such as Alexa, Siri, and Google Assistant have become
increasingly popular worldwide. However, linguistic variations, variability of
speech patterns, ambient acoustic conditions, and other such factors are often
correlated with the assistants misinterpreting the user's query. In order to
provide better customer experience, retrieval based query reformulation (QR)
systems are widely used to reformulate those misinterpreted user queries.
Current QR systems typically focus on neural retrieval model training or direct
entities retrieval for the reformulating. However, these methods rarely focus
on query expansion and entity weighting simultaneously, which may limit the
scope and accuracy of the query reformulation retrieval. In this work, we
propose a novel Query Expansion and Entity Weighting method (QEEW), which
leverages the relationships between entities in the entity catalog (consisting
of users' queries, assistant's responses, and corresponding entities), to
enhance the query reformulation performance. Experiments on Alexa annotated
data demonstrate that QEEW improves all top precision metrics, particularly 6%
improvement in top10 precision, compared with baselines not using query
expansion and weighting; and more than 5% improvement in top10 precision
compared with other baselines using query expansion and weighting.

本文提出了一种新的查询扩展和实体加权方法，利用实体目录中的实体关系改进查询重构性能，实验发现该方法相比不使用查询扩展和加权的基准模型，特别是在前 10 个结果中，精度有 6% 的提高，在使用查询扩展和加权的其他基准模型中，精度有 5% 以上的提高。

语音助手系统中的查询扩展和实体加权查询重构检索

Query Expansion and Entity Weighting for Query Reformulation Retrieval in Voice Assistant Systems

Most state-of-the-art open-domain question answering systems use a neural
retrieval model to encode passages into continuous vectors and extract them
from a knowledge source. However, such retrieval models often require large
memory to run because of the massive size of their passage index. In this
paper, we introduce Binary Passage Retriever (BPR), a memory-efficient neural
retrieval model that integrates a learning-to-hash technique into the
state-of-the-art Dense Passage Retriever (DPR) to represent the passage index
using compact binary codes rather than continuous vectors. BPR is trained with
a multi-task objective over two tasks: efficient candidate generation based on
binary codes and accurate reranking based on continuous vectors. Compared with
DPR, BPR substantially reduces the memory cost from 65GB to 2GB without a loss
of accuracy on two standard open-domain question answering benchmarks: Natural
Questions and TriviaQA. Our code and trained models are available at
this https URL

本篇论文介绍一种名为 Binary Passage Retriever (BPR) 的神经检索模型，它将学习哈希技术集成到 Dense Passage Retriever (DPR) 中，以紧凑的二进制代码表示待检索文本，从而大幅降低了内存成本，同时保证了一致的精度，这适用于开放领域的问答。

使用哈希的高效开放域问答过程检索

Efficient Passage Retrieval with Hashing for Open-domain Question  Answering

Passage retrieval addresses the problem of locating relevant passages,
usually from a large corpus, given a query. In practice, lexical term-matching
algorithms like BM25 are popular choices for retrieval owing to their
efficiency. However, term-based matching algorithms often miss relevant
passages that have no lexical overlap with the query and cannot be finetuned to
downstream datasets. In this work, we consider the embedding-based two-tower
architecture as our neural retrieval model. Since labeled data can be scarce
and because neural retrieval models require vast amounts of data to train, we
propose a novel method for generating synthetic training data for retrieval.
Our system produces remarkable results, significantly outperforming BM25 on 5
out of 6 datasets tested, by an average of 2.45 points for Recall@1. In some
cases, our model trained on synthetic data can even outperform the same model
trained on real data

本文通过采用两塔架构来建立神经检索模型，并提出了一种新的用于检索的合成数据生成方法，显著优于基于词汇匹配算法的 BM25，并在 5 个测试数据集中平均提高 Recall@1 指标 2.45 个点。

基于嵌入的零样本检索通过查询生成

Embedding-based Zero-shot Retrieval through Query Generation

Systems for Open-Domain Question Answering (OpenQA) generally depend on a
retriever for finding candidate passages in a large corpus and a reader for
extracting answers from those passages. In much recent work, the retriever is a
learned component that uses coarse-grained vector representations of questions
and passages. We argue that this modeling choice is insufficiently expressive
for dealing with the complexity of natural language questions. To address this,
we define ColBERT-QA, which adapts the scalable neural retrieval model ColBERT
to OpenQA. ColBERT creates fine-grained interactions between questions and
passages. We propose an efficient weak supervision strategy that iteratively
uses ColBERT to create its own training data. This greatly improves OpenQA
retrieval on Natural Questions, SQuAD, and TriviaQA, and the resulting system
attains state-of-the-art extractive OpenQA performance on all three datasets.

利用 ColBERT 模型定义了 ColBERT-QA，采用细粒度相互作用和弱监督策略来提升开放域问答的效果，实验结果表明在三个数据集上均取得了最先进的提取式开放域问答性能。