With the recent surge of NLP technologies in the financial domain, banks and
other financial entities have adopted virtual agents (VA) to assist customers.
A challenging problem for VAs in this domain is determining a user's reason or
intent for contacting the VA, especially when the intent was unseen or open
during the VA's training. One method for handling open intents is adaptive
decision boundary (ADB) post-processing, which learns tight decision boundaries
from intent representations to separate known and open intents. We propose
incorporating two methods for supervised pre-training of intent
representations: prefix-tuning and fine-tuning just the last layer of a large
language model (LLM). With this proposal, our accuracy is 1.63% - 2.07% higher
than the prior state-of-the-art ADB method for open intent classification on
the banking77 benchmark amongst others. Notably, we only supplement the
original ADB model with 0.1% additional trainable parameters. Ablation studies
also determine that our method yields better results than full fine-tuning the
entire model. We hypothesize that our findings could stimulate a new optimal
method of downstream tuning that combines parameter efficient tuning modules
with fine-tuning a subset of the base model's layers.

本文探讨了金融机构采用虚拟代理人辅助客户等领域中自然语言处理技术的最新进展，提出一种用于开放式任务意图分类的自适应决策边界后处理方法并将其与前缀调整和仅对大型语言模型的最后一层进行微调的有监督预训练方法相结合，比先前研究取得更高的准确率。

金融开放意图分类的更好意图表示学习

Learning Better Intent Representations for Financial Open Intent Classification

Conversational agents are usually designed for closed-world environments.
Unfortunately, users can behave unexpectedly. Based on the open-world
environment, we often encounter the situation that the training and test data
are sampled from different distributions. Then, data from different
distributions are called out-of-domain (OOD). A robust conversational agent
needs to react to these OOD utterances adequately. Thus, the importance of
robust OOD detection is emphasized. Unfortunately, collecting OOD data is a
challenging task. We have designed an OOD detection algorithm independent of
OOD data that outperforms a wide range of current state-of-the-art algorithms
on publicly available datasets. Our algorithm is based on a simple but
efficient approach of combining metric learning with adaptive decision
boundary. Furthermore, compared to other algorithms, we have found that our
proposed algorithm has significantly improved OOD performance in a scenario
with a lower number of classes while preserving the accuracy for in-domain
(IND) classes.

设计了一种基于度量学习和自适应决策边界的 OOD 检测算法，相较于其他算法，在类别数量较少的情况下显著提高 OOD 性能，保持了 IND 类别的准确性。

度量学习与自适应边界用于域外检测

Metric Learning and Adaptive Boundary for Out-of-Domain Detection

Open intent classification is a challenging task in dialogue systems. On the
one hand, it should ensure the quality of known intent identification. On the
other hand, it needs to detect the open (unknown) intent without prior
knowledge. Current models are limited in finding the appropriate decision
boundary to balance the performances of both known intents and the open intent.
In this paper, we propose a post-processing method to learn the adaptive
decision boundary (ADB) for open intent classification. We first utilize the
labeled known intent samples to pre-train the model. Then, we automatically
learn the adaptive spherical decision boundary for each known class with the
aid of well-trained features. Specifically, we propose a new loss function to
balance both the empirical risk and the open space risk. Our method does not
need open intent samples and is free from modifying the model architecture.
Moreover, our approach is surprisingly insensitive with less labeled data and
fewer known intents. Extensive experiments on three benchmark datasets show
that our method yields significant improvements compared with the
state-of-the-art methods. The codes are released at
this https URL

本文提出了一种后处理方法，通过利用已标记的已知意图样本对模型进行预训练，自动学习每个已知类别的自适应球形决策边界，从而均衡已知意图和未知意图之间的性能差异。实验证明，该方法在不需要未知意图样本和修改模型架构的情况下，依然能在三个基准数据集上显著提高分类精度。