Human conversations naturally evolve around different topics and fluently
move between them. In research on dialog systems, the ability to actively and
smoothly transition to new topics is often ignored. In this paper we introduce
TIAGE, a new topic-shift aware dialog benchmark constructed utilizing human
annotations on topic shifts. Based on TIAGE, we introduce three tasks to
investigate different scenarios of topic-shift modeling in dialog settings:
topic-shift detection, topic-shift triggered response generation and
topic-aware dialog generation. Experiments on these tasks show that the
topic-shift signals in TIAGE are useful for topic-shift response generation. On
the other hand, dialog systems still struggle to decide when to change topic.
This indicates further research is needed in topic-shift aware dialog modeling.

本研究介绍了一种新的话题转换感知会话基准 TIAGE，其具有检测、触发和对话生成三个任务，实验结果表明，TIAGE 中的话题转移信号对话题转移响应生成有用，但仍需要在话题转换感知对话建模方面进行进一步研究。

TIAGE: 面向话题漂移感知的对话建模基准

TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling

In the retrieval-based multi-turn dialogue modeling, it remains a challenge
to select the most appropriate response according to extracting salient
features in context utterances. As a conversation goes on, topic shift at
discourse-level naturally happens through the continuous multi-turn dialogue
context. However, all known retrieval-based systems are satisfied with
exploiting local topic words for context utterance representation but fail to
capture such essential global topic-aware clues at discourse-level. Instead of
taking topic-agnostic n-gram utterance as processing unit for matching purpose
in existing systems, this paper presents a novel topic-aware solution for
multi-turn dialogue modeling, which segments and extracts topic-aware
utterances in an unsupervised way, so that the resulted model is capable of
capturing salient topic shift at discourse-level in need and thus effectively
track topic flow during multi-turn conversation. Our topic-aware modeling is
implemented by a newly proposed unsupervised topic-aware segmentation algorithm
and Topic-Aware Dual-attention Matching (TADAM) Network, which matches each
topic segment with the response in a dual cross-attention way. Experimental
results on three public datasets show TADAM can outperform the state-of-the-art
method, especially by 3.3% on E-commerce dataset that has an obvious topic
shift.

本文提出了一种新颖的基于主题感知的无监督多轮对话建模方案，该方案通过无监督的主题感知分割算法和主题感知双交互匹配网络实现，在多个公共数据集上的实验结果表明 TADAM 可以胜过现有的最新方法，尤其是在出现明显主题转移的电子商务数据集上提高了 3.3％。