Papers with task-oriented dialogue system
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning (2022.acl-short)
Copied to clipboard
| Challenge: | Existing methods to find out out-of-domain (OOD) intents do not take prior knowledge of in-domain data into account. |
| Approach: | They propose a disentangled knowledge transfer method to bridge the gap between IND pre-training and OOD clustering by using a unified multi-head contrastive learning framework. |
| Outcome: | The proposed method is able to group new unknown intents into different clusters, enabling future development of the system. |
OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding (2023.acl-demo)
Copied to clipboard
| Challenge: | Spoken Language Understanding (SLU) is a task-oriented dialogue system . open-source toolkit provides a unified, modularized, and extensible toolkit for SLU . |
| Approach: | They introduce an open-source toolkit to provide a unified toolkit for spoken language understanding. |
| Outcome: | The proposed toolkit unifies 10 models for both single-intent and multi-intention scenarios. |
Dynamic Dialogue Policy for Continual Reinforcement Learning (2022.coling-1)
Copied to clipboard
Christian Geishauser, Carel van Niekerk, Hsien-chin Lin, Nurul Lubis, Michael Heck, Shutong Feng, Milica Gašić
| Challenge: | Continual reinforcement learning of the dialogue policy has remained unaddressed . lack of a framework with training protocols, baseline models and suitable metrics has hindered research in this direction. |
| Approach: | They propose a continual learning algorithm, baseline architectures and metrics for assessing continual reinforcement learning models. |
| Outcome: | The proposed architecture can integrate new knowledge seamlessly and achieve significant zero-shot performance when exposed to unseen domains. |
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent studies show that fine-tuning pre-trained language models with a small set of labeled utterances in a supervised manner is helpful, but it yields an anisotropic feature space, which may suppress the expressive power of the semantic representations. |
| Approach: | They propose to regularize supervised pre-training towards isotropy by contrastive learning and correlation matrix regularizers. |
| Outcome: | The proposed methods improve supervised pre-training by regularizing the feature space towards isotropy. |
OutFlip: Generating Examples for Unknown Intent Detection with Natural Language Attack (2021.findings-acl)
Copied to clipboard
| Challenge: | Out-of-domain (OOD) input detection is vital in task-oriented dialogue systems . accepted OOD inputs lead to incorrect response of the system . |
| Approach: | They propose a method to generate out-of-domain samples from in-domain training datasets using OutFlip. |
| Outcome: | The proposed method significantly improves an intent classification model's out-of-domain detection performance. |
Adjusting Image Attributes of Localized Regions with Low-level Dialogue (2020.lrec-1)
Copied to clipboard
| Challenge: | Image editing is time-consuming and requires a wide assortment of features and combinations of these features to achieve a desired effect. |
| Approach: | They propose a task-oriented dialogue system to investigate low-level instructions for NLIE . 25% of users found the system easy-to-use, resonating with their motivation . |
| Outcome: | The proposed system is easy-to-use and user-friendly. |
SPM: A Split-Parsing Method for Joint Multi-Intent Detection and Slot Filling (2023.acl-industry)
Copied to clipboard
| Challenge: | Existing studies focus on utterances with a single intent, but lack the ability to assign slots to each corresponding intent. |
| Approach: | They propose a split-parsing method for joint intent detection and slot filling . they split an input sentence into multiple sub-sentences which contain a single-intent . |
| Outcome: | The proposed method improves on three multi-intent datasets on multi-tasks. |
Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking (2021.acl-short)
Copied to clipboard
| Challenge: | Existing methods to learn consecutive tasks without forgetting how to perform previously trained problems are lacking. |
| Approach: | They propose a continual learning method which preserves performance on previously encountered tasks while accelerating learning progress on subsequent tasks. |
| Outcome: | The proposed method preserves performance on previously encountered tasks while accelerating learning progress on subsequent tasks. |
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning (D18-1)
Copied to clipboard
| Challenge: | Existing dialog datasets rely on human labeling, which is expensive, limited in size, and in low coverage. |
| Approach: | They propose a framework to automatically cluster dialogue intents and slots . they collect context features, leverage an autoencoder for feature assembly, and adapt a dynamic hierarchical clustering method for intent and slot labeling. |
| Outcome: | The proposed framework can promote human labeling cost to a great extent and achieve good intent clustering accuracy (84.1%) it also provides reasonable and instructive slot labeling results. |
AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation (2021.acl-long)
Copied to clipboard
| Challenge: | Large-scale conversational systems typically generate unnatural, robotic responses using template-based approaches. |
| Approach: | They propose a data augmentation approach that combines a self-trained neural retrieval model with a few-shot learned NLU model to automatically create MR-to-Text data from open-domain texts. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on the FewshotWOZ data in both BLEU and Slot Error Rate. |
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding (C18-1)
Copied to clipboard
| Challenge: | Existing work which augments an utterance without considering its relation with other utterrances, however, has failed to improve the language understanding module. |
| Approach: | They propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance’s same semantic alternatives in the training data. |
| Outcome: | The proposed framework achieves 6.38 and 10.04 F-scores on the Airline Travel Information System dataset and a newly created semantic frame annotation on the Stanford Multi-turn, Multi-domain Dialogue Dataset. |
A Sequence-to-Sequence Approach to Dialogue State Tracking (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for dialogue state tracking are still challenging, but they are improving . a new approach to dialogue state monitoring is proposed, called Seq2Seq-DU . |
| Approach: | They propose a new dialogue state tracking module that formalizes DST as a sequence-to-sequence problem. |
| Outcome: | The proposed method outperforms existing methods on benchmark datasets in different settings. |
DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Recent research focused on knowledge distillation methods where the underlying relationship between the facts in a knowledge base is not effectively captured. |
| Approach: | They propose a novel task-oriented dialogue system that effectively incorporates knowledge into a language model by using structural information of a knowledge graph. |
| Outcome: | The proposed system views relational knowledge as a knowledge graph and introduces (1) a structure-aware knowledge embedding technique, and (2) a Knowledge graph-weighted attention masking strategy to facilitate the system selecting relevant information during the dialogue generation. |
APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection (2023.findings-emnlp)
Copied to clipboard
Pei Wang, Keqing He, Yutao Mou, Xiaoshuai Song, Yanan Wu, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
| Challenge: | Existing methods for detecting out-of-domain (OOD) intents are hard to label . previous studies use labeled in-domain data to learn intent representations . |
| Approach: | They propose a prototypical pseudo-labeling method for few-shot OOD detection . they propose 'protoOOD' framework and adaptive pseudo-labeled method . |
| Outcome: | The proposed method is able to detect out-of-domain (OOD) intents from user queries. |
Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System (2021.acl-long)
Copied to clipboard
| Challenge: | Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set. |
| Approach: | They introduce a task, Novel Slot Detection, in the task-oriented dialogue system. |
| Outcome: | The proposed task is based on two public NSD datasets and proposes strong baselines . it aims to identify a sequence of tokens and extract semantic constituents from user queries . |
Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation (C18-1)
Copied to clipboard
| Challenge: | Existing pipeline models for task-oriented dialogue system require explicit modeling of dialogue states and hand-crafted action spaces to query domain-specific knowledge base. |
| Approach: | They propose a framework that leverages the advantages of classic pipeline and sequence-to-sequence models. |
| Outcome: | The proposed framework outperforms baseline models on automatic and human evaluation on a Stanford Multi-turn Multi-domain task-oriented dialogue dataset. |
Interactive Evaluation for Medical LLMs via Task-oriented Dialogue System (2025.coling-main)
Copied to clipboard
| Challenge: | In typical medical scenarios, doctors often ask a set of questions to gain a comprehensive understanding of patients’ conditions. |
| Approach: | They propose to use multi-turn medical dialogue evaluation to evaluate proactive communication and diagnostic capabilities of medical Large Language Models (LLMs) . |
| Outcome: | The proposed model outperforms existing models on multi-turn question-answering datasets and is therefore cost-effective. |
HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to train task-oriented dialogue systems in monolingual datasets are expensive to build. |
| Approach: | They propose a hierarchical framework to classify intents in high-level and slot filling in low-level . they incorporate sentence-level alignment among different languages to enhance intent detection . |
| Outcome: | The proposed framework achieves the performance on a public task-oriented dialog dataset. |
Continual Learning in Task-Oriented Dialogue Systems (2021.emnlp-main)
Copied to clipboard
Andrea Madotto, Zhaojiang Lin, Zhenpeng Zhou, Seungwhan Moon, Paul Crook, Bing Liu, Zhou Yu, Eunjoon Cho, Pascale Fung, Zhiguang Wang
| Challenge: | Existing continuous learning systems are not designed to add new domains and functionalities through time without incurring the high cost of retraining the whole system. |
| Approach: | They propose a first-ever continual learning benchmark for task-oriented dialogue systems . they propose 'architecture' method based on residual adapters to implement continual training . |
| Outcome: | The proposed architectural method performs better than multitask learning while being 20X faster in learning new domains. |
DSPM-NLG: A Dual Supervised Pre-trained Model for Few-shot Natural Language Generation in Task-oriented Dialogue System (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing models for few-shot natural language generation are based on a dual correlation between NLG and SLU from the perspective of probability. |
| Approach: | They propose a dual supervised pre-trained model to regularize the pre-training process . they use a probabilistic approach to learn the dual correlation between NLG and SLU . |
| Outcome: | The proposed model outperforms the previous state-of-the-art models on a few-shot dataset. |