Ranking responses for a given dialogue context is a popular benchmark in
which the setup is to re-rank the ground-truth response over a limited set of
$n$ responses, where $n$ is typically 10. The predominance of this setup in
conversation response ranking has lead to a great deal of attention to building
neural re-rankers, while the first-stage retrieval step has been overlooked.
Since the correct answer is always available in the candidate list of $n$
responses, this artificial evaluation setup assumes that there is a first-stage
retrieval step which is always able to rank the correct response in its top-$n$
list. In this paper we focus on the more realistic task of full-rank retrieval
of responses, where $n$ can be up to millions of responses. We investigate both
dialogue context and response expansion techniques for sparse retrieval, as
well as zero-shot and fine-tuned dense retrieval approaches. Our findings based
on three different information-seeking dialogue datasets reveal that a learned
response expansion technique is a solid baseline for sparse retrieval. We find
the best performing method overall to be dense retrieval with intermediate
training, i.e. a step after the language model pre-training where sentence
representations are learned, followed by fine-tuning on the target
conversational data. We also investigate the intriguing phenomena that harder
negatives sampling techniques lead to worse results for the fine-tuned dense
retrieval models. The code and datasets are available at
this https URL.

本文针对完整回应的检索任务，提出了对话背景和回应扩充技术的检索方法，探析了稀疏检索和零样本以及微调密集检索方法，并发现通过练习 “学习到的回应扩充技术” 是稀疏检索的一个坚实基础。我们最终发现，整体表现最好的方法是密集的检索中级训练，即是模型先预训练语言模型，再根据目标对话数据进行微调，同时我们研究发现，更难的负采样技术导致微调的稠密点检索模型更糟糕。

对话响应全秩检索的稀疏和密集方法

Sparse and Dense Approaches for the Full-rank Retrieval of Responses for Dialogues

Neural ranking models are traditionally trained on a series of random
batches, sampled uniformly from the entire training set. Curriculum learning
has recently been shown to improve neural models' effectiveness by sampling
batches non-uniformly, going from easy to difficult instances during training.
In the context of neural Information Retrieval (IR) curriculum learning has not
been explored yet, and so it remains unclear (1) how to measure the difficulty
of training instances and (2) how to transition from easy to difficult
instances during training. To address both challenges and determine whether
curriculum learning is beneficial for neural ranking models, we need
large-scale datasets and a retrieval task that allows us to conduct a wide
range of experiments. For this purpose, we resort to the task of conversation
response ranking: ranking responses given the conversation history. In order to
deal with challenge (1), we explore scoring functions to measure the difficulty
of conversations based on different input spaces. To address challenge (2) we
evaluate different pacing functions, which determine the velocity in which we
go from easy to difficult instances. We find that, overall, by just
intelligently sorting the training data (i.e., by performing curriculum
learning) we can improve the retrieval effectiveness by up to 2%.

本文探讨了在神经检索模型训练中，如何通过对训练数据进行智能排序（即通过实行课程学习）来提高检索效率。作者利用大规模数据和会话响应排序任务，研究了如何确定难度以及如何过渡到更难的实例，取得了 2％的性能改进。