In this paper, we study the task of instructional dialogue and focus on the
cooking domain. Analyzing the generated output of the GPT-J model, we reveal
that the primary challenge for a recipe-grounded dialog system is how to
provide the instructions in the correct order. We hypothesize that this is due
to the model's lack of understanding of user intent and inability to track the
instruction state (i.e., which step was last instructed). Therefore, we propose
to explore two auxiliary subtasks, namely User Intent Detection and Instruction
State Tracking, to support Response Generation with improved instruction
grounding. Experimenting with our newly collected dataset, ChattyChef, shows
that incorporating user intent and instruction state information helps the
response generation model mitigate the incorrect order issue. Furthermore, to
investigate whether ChatGPT has completely solved this task, we analyze its
outputs and find that it also makes mistakes (10.7% of the responses), about
half of which are out-of-order instructions. We will release ChattyChef to
facilitate further research in this area at:
this https URL

通过探索辅助子任务：用户意图检测和指令状态追踪，以支持响应生成，从而提高指令基础的对话系统的性能，实验结果表明它能够缓解生成错误顺序的问题，并在分析 ChatGPT 的输出时发现其仍存在错误。

食谱驱动对话中改善指令顺序

Improved Instruction Ordering in Recipe-Grounded Conversation

User intent detection plays a critical role in question-answering and dialog
systems. Most previous works treat intent detection as a classification problem
where utterances are labeled with predefined intents. However, it is
labor-intensive and time-consuming to label users' utterances as intents are
diversely expressed and novel intents will continually be involved. Instead, we
study the zero-shot intent detection problem, which aims to detect emerging
user intents where no labeled utterances are currently available. We propose
two capsule-based architectures: INTENT-CAPSNET that extracts semantic features
from utterances and aggregates them to discriminate existing intents, and
INTENTCAPSNET-ZSL which gives INTENTCAPSNET the zero-shot learning ability to
discriminate emerging intents via knowledge transfer from existing intents.
Experiments on two real-world datasets show that our model not only can better
discriminate diversely expressed existing intents, but is also able to
discriminate emerging intents when no labeled utterances are available.

本文提出了两种基于胶囊网络的架构，分别为 INTENT-CAPSNET 和 INTENTCAPSNET-ZSL，可用于用户意图检测问题。使用这些模型可以在没有标记的语句的情况下检测出新兴的用户意图。在两个真实世界数据集上的实验证明了该模型的有效性。

基于胶囊神经网络的零样本用户意图识别

Zero-shot User Intent Detection via Capsule Neural Networks

The Internet has revolutionized healthcare by offering medical information
ubiquitously to patients via web search. The healthcare status, complex medical
information needs of patients are expressed diversely and implicitly in their
medical text queries. Aiming to better capture a focused picture of user's
medical-related information search and shed insights on their healthcare
information access strategies, it is challenging yet rewarding to detect
structured user intentions from their diversely expressed medical text queries.
We introduce a graph-based formulation to explore structured concept
transitions for effective user intent detection in medical queries, where each
node represents a medical concept mention and each directed edge indicates a
medical concept transition. A deep model based on multi-task learning is
introduced to extract structured semantic transitions from user queries, where
the model extracts word-level medical concept mentions as well as
sentence-level concept transitions collectively. A customized graph-based
mutual transfer loss function is designed to impose explicit constraints and
further exploit the contribution of mentioning a medical concept word to the
implication of a semantic transition. We observe an 8% relative improvement in
AUC and 23% relative reduction in coverage error by comparing the proposed
model with the best baseline model for the concept transition inference task on
real-world medical text queries.

通过基于图形的医疗概念转换探索用户查询目的，引入多任务学习的深度模型提取用户查询中的语义转换关键词和句子。使用自定义函数进一步探索称为医疗概念词的影响。通过比较最佳基线模型，观察到 8％的相对改进和 23％的相对覆盖误差降低率。