Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing frameworks for fast and accurate intent classification for task-oriented dialogue systems do not provide a clear definition of the true intent. |
| Approach: | They propose to augment the framework for out-of-scope detection by disambiguating between a small number of likely intents. |
| Outcome: | The proposed framework generates small clarification questions and is capable of out-of-scope detection. |
Similar Papers
Scaling Intent Understanding: A Framework for Classification with Clarification using Lightweight LLMs (2026.eacl-industry)
Copied to clipboard
| Challenge: | Proprietary large-language models (LLMs) assign intents to user utterances without addressing ambiguity. |
| Approach: | They propose a domain-agnostic framework that equips open-source LLMs with the ability to perform intent classification and generate clarification questions in case of ambiguity. |
| Outcome: | The proposed framework performs intent classification and generates clarification questions in case of ambiguity. |
Generalized Intent Discovery: Learning from Open World Dialogue System (2022.coling-1)
Copied to clipboard
| Challenge: | Existing intent classification models rely on a pre-defined intent set and supervised labels, which is limited in some practical scenarios. |
| Approach: | They propose to extend an IND intent classifier to an open-world intent set including IND and OOD intents. |
| Outcome: | The proposed task can classify IND and OOD intents while discovering new unlabeled OOD types incrementally. |
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction (D19-1)
Copied to clipboard
Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, Jason Mars
| Challenge: | Task-oriented dialog systems need to know when a query falls outside their range of supported intents. |
| Approach: | They propose a dataset that includes queries that are out-of-scope and 150 intent classes over 10 domains. |
| Outcome: | The proposed dataset includes queries that are out-of-scope, i.e., queries that do not fall into any of the system’s supported intents. |
Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for out-of-scope intent detection rely on strong assumptions on data distribution and confidence threshold selection. |
| Approach: | They propose a method to train an out-of-scope intent classifier in a fully end-to-end manner by simulating the test scenario in training. |
| Outcome: | The proposed method improves on four benchmark dialogue datasets and improves over state-of-the-art methods. |
Intent Detection with WikiHow (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing approaches to intent detection have limited data annotated for new domains or languages. |
| Approach: | They propose to train a set of pretraining intent detection models on wikiHow which can predict a broad range of intended goals from many actions. |
| Outcome: | The proposed models achieve state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue dataset, and all 3 languages of the Facebook multilingual dialog datasets. |
A Survey on Multi-modal Intent Recognition: Recent Advances and New Frontiers (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Multi-modal intent recognition (MIR) requires integrating non-verbal cues from real-world contexts to enhance human intention understanding. |
| Approach: | They present a comprehensive review of multi-modal intent recognition . they provide a survey of the field covering textual, visual, and acoustic signals . |
| Outcome: | The present survey summarises the current state of multi-modal intent recognition . it includes a comprehensive taxonomy and advanced methods . |
Intent Detection in the Age of LLMs (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Traditional approaches to intent detection struggle with out-of-scope (OOS) detection. |
| Approach: | They propose to use adaptive in-context learning and chain-of-thought prompting to detect intent in SOTA LLMs. |
| Outcome: | The proposed system achieves 2% of native accuracy with 50% less latency. |
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey (2020.coling-main)
Copied to clipboard
| Challenge: | In recent years, neural-network based models have been used for a wide range of tasks, including slot filling and intent classification. |
| Approach: | They propose three neural architectures to model slot filling and intent classification . they propose independent models, joint models and transfer learning models that exploit the mutual benefit of the two tasks simultaneously and scale the model to new domains. |
| Outcome: | The proposed models model SF and IC separately, exploit mutual benefit of the two tasks simultaneously and scale the model to new domains. |
A Probabilistic Framework for Discovering New Intents (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for detecting unknown intents do not explore the intrinsic structure of unlabeled data. |
| Approach: | They propose a probabilistic framework for discovering intents where intent assignments are treated as latent variables. |
| Outcome: | The proposed framework can be used to discover intents with latent variables . it can be applied to three challenging real-world datasets . |
Going beyond research datasets: Novel intent discovery in the industry setting (2023.findings-eacl)
Copied to clipboard
| Challenge: | Novel intent discovery automates grouping of similar messages to identify previously unknown intents. |
| Approach: | They propose to use question-only data to improve the intent discovery pipeline . they propose to utilize conversational structure of real-life datasets for clustering . |
| Outcome: | The proposed method gives 33pp performance boost over state-of-the-art model for question only . it also gives 13pp performance increase over the naive baseline model . |