Multi-turn response selection is a challenging task due to its high demands
on efficient extraction of the matching features from abundant information
provided by context utterances. Since incorporating syntactic information like
dependency structures into neural models can promote a better understanding of
the sentences, such a method has been widely used in NLP tasks. Though
syntactic information helps models achieved pleasing results, its application
in retrieval-based dialogue systems has not been fully explored. Meanwhile,
previous works focus on intra-sentence syntax alone, which is far from
satisfactory for the task of multi-turn response where dialogues usually
contain multiple sentences. To this end, we propose SIA, Syntax-Informed
Attention, considering both intra- and inter-sentence syntax information. While
the former restricts attention scope to only between tokens and corresponding
dependents in the syntax tree, the latter allows attention in cross-utterance
pairs for those syntactically important tokens. We evaluate our method on three
widely used benchmarks and experimental results demonstrate the general
superiority of our method on dialogue response selection.

本研究提出了一种结合了句法信息的注意力机制，被命名为 SIA，考虑到了多轮对话中的句内和句间结构信息，实验结果表明该方法在对话响应匹配中表现出更好的性能。

基于句法知识的注意力机制在检索式对话系统中的应用

Improve Retrieval-based Dialogue System via Syntax-Informed Attention

In retrieval-based dialogue systems, a response selection model acts as a
ranker to select the most appropriate response among several candidates.
However, such selection models tend to rely on context-response content
similarity, which makes models vulnerable to adversarial responses that are
semantically similar but not relevant to the dialogue context. Recent studies
have shown that leveraging these adversarial responses as negative training
samples is useful for improving the discriminating power of the selection
model. Nevertheless, collecting human-written adversarial responses is
expensive, and existing synthesizing methods often have limited scalability. To
overcome these limitations, this paper proposes a simple but efficient method
for generating adversarial negative responses leveraging a large-scale language
model. Experimental results on dialogue selection tasks show that our method
outperforms other methods of synthesizing adversarial negative responses. These
results suggest that our method can be an effective alternative to human
annotators in generating adversarial responses. Our dataset and generation code
is available at this https URL.

本研究提出了一种使用大规模语言模型生成对抗性负面响应的简单而高效的方法，用于提高在检索式对话系统中响应选择模型的识别能力，并证明其在对话选择任务中优于其他对抗性负面响应合成方法。