Though chatbots based on large neural models can often produce fluent
responses in open domain conversations, one salient error type is contradiction
or inconsistency with the preceding conversation turns. Previous work has
treated contradiction detection in bot responses as a task similar to natural
language inference, e.g., detect the contradiction between a pair of bot
utterances. However, utterances in conversations may contain co-references or
ellipsis, and using these utterances as is may not always be sufficient for
identifying contradictions. This work aims to improve the contradiction
detection via rewriting all bot utterances to restore antecedents and ellipsis.
We curated a new dataset for utterance rewriting and built a rewriting model on
it. We empirically demonstrate that this model can produce satisfactory
rewrites to make bot utterances more complete. Furthermore, using rewritten
utterances improves contradiction detection performance significantly, e.g.,
the AUPR and joint accuracy scores (detecting contradiction along with
evidence) increase by 6.5% and 4.5% (absolute increase), respectively.

为了改进聊天机器人中的矛盾检测，本文提出了一种通过重写聊天机器人发言来还原先行词和省略号的方法，并在相应的数据集上建立了模型，实证结果表明该模型能够显著提高矛盾检测性能。

通过话语改写提高机器人响应矛盾检测的效果

Improving Bot Response Contradiction Detection via Utterance Rewriting

Utterance rewriting aims to recover coreferences and omitted information from
the latest turn of a multi-turn dialogue. Recently, methods that tag rather
than linearly generate sequences have proven stronger in both in- and
out-of-domain rewriting settings. This is due to a tagger's smaller search
space as it can only copy tokens from the dialogue context. However, these
methods may suffer from low coverage when phrases that must be added to a
source utterance cannot be covered by a single context span. This can occur in
languages like English that introduce tokens such as prepositions into the
rewrite for grammaticality. We propose a hierarchical context tagger (HCT) that
mitigates this issue by predicting slotted rules (e.g., "besides_") whose slots
are later filled with context spans. HCT (i) tags the source string with
token-level edit actions and slotted rules and (ii) fills in the resulting rule
slots with spans from the dialogue context. This rule tagging allows HCT to add
out-of-context tokens and multiple spans at once; we further cluster the rules
to truncate the long tail of the rule distribution. Experiments on several
benchmarks show that HCT can outperform state-of-the-art rewriting systems by
~2 BLEU points.

本文提出了一种基于分层上文标注的标注器，其中预测到的数个槽最终被上下文信息填充来重新编写话语，使其能够在涉及英语等会出现介词的语言时添加超出上下文范围的标记和多个跨度，并通过集群规则来截断规则分布的长尾。各种基准测试表明，使用本方法时，BLEU 点数高于最佳模型约 2 点。

层次化上下文标记用于话语重写

Hierarchical Context Tagging for Utterance Rewriting

Recent research has made impressive progress in single-turn dialogue
modelling. In the multi-turn setting, however, current models are still far
from satisfactory. One major challenge is the frequently occurred coreference
and information omission in our daily conversation, making it hard for machines
to understand the real intention. In this paper, we propose rewriting the human
utterance as a pre-process to help multi-turn dialgoue modelling. Each
utterance is first rewritten to recover all coreferred and omitted information.
The next processing steps are then performed based on the rewritten utterance.
To properly train the utterance rewriter, we collect a new dataset with human
annotations and introduce a Transformer-based utterance rewriting architecture
using the pointer network. We show the proposed architecture achieves
remarkably good performance on the utterance rewriting task. The trained
utterance rewriter can be easily integrated into online chatbots and brings
general improvement over different domains.

该研究提出了一种将人类话语重写作为预处理方法，以帮助多轮对话建模。使用指针网络提出的基于 Transformer 的话语重写架构训练的话语重写器可以轻松地集成到在线聊天机器人中，并在不同领域带来一般性能的提升。