A survey of joint intent detection and slot-filling models in natural language understanding

• URL: http://arxiv.org/abs/2101.08091v3
• Date: Mon, 22 Feb 2021 03:25:17 GMT
• Title: A survey of joint intent detection and slot-filling models in natural language understanding
• Authors: H. Weld, X. Huang, S. Long, J. Poon, S. C. Han (School of Computer Science, The University of Sydney)
• Abstract summary: This article is a compilation of past work in natural language understanding, especially joint intent classification and slot filling. In this article, we describe trends, approaches, issues, data sets, evaluation metrics in intent classification and slot filling.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks have been deemed to proceed independently. However, more recently, joint models for intent classification and slot filling have achieved state-of-the-art performance, and have proved that there exists a strong relationship between the two tasks. This article is a compilation of past work in natural language understanding, especially joint intent classification and slot filling. We observe three milestones in this research so far: Intent detection to identify the speaker's intention, slot filling to label each word token in the speech/text, and finally, joint intent classification and slot filling tasks. In this article, we describe trends, approaches, issues, data sets, evaluation metrics in intent classification and slot filling. We also discuss representative performance values, describe shared tasks, and provide pointers to future work, as given in prior works. To interpret the state-of-the-art trends, we provide multiple tables that describe and summarise past research along different dimensions, including the types of features, base approaches, and dataset domain used.

Related papers

• IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery [12.905097743551774]
  We propose IntenDD, a unified approach leveraging a shared utterance encoding backbone. IntenDD uses an entirely unsupervised contrastive learning strategy for representation learning. We find that our approach consistently outperforms competitive baselines across all three tasks.
  arXiv  Detail & Related papers  (2023-10-25T16:50:24Z)
• Towards Open Vocabulary Learning: A Survey [146.90188069113213]
  Deep neural networks have made impressive advancements in various core tasks like segmentation, tracking, and detection. Recently, open vocabulary settings were proposed due to the rapid progress of vision language pre-training. This paper provides a thorough review of open vocabulary learning, summarizing and analyzing recent developments in the field.
  arXiv  Detail & Related papers  (2023-06-28T02:33:06Z)
• Association Graph Learning for Multi-Task Classification with Category Shifts [68.58829338426712]
  We focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously. We learn an association graph to transfer knowledge among tasks for missing classes. Our method consistently performs better than representative baselines.
  arXiv  Detail & Related papers  (2022-10-10T12:37:41Z)
• A Unified Framework for Multi-intent Spoken Language Understanding with prompting [14.17726194025463]
  We describe a Prompt-based Spoken Language Understanding (PromptSLU) framework, to intuitively unify two sub-tasks into the same form. In detail, ID and SF are completed by concisely filling the utterance into task-specific prompt templates as input, and sharing output formats of key-value pairs sequence. Experiment results show that our framework outperforms several state-of-the-art baselines on two public datasets.
  arXiv  Detail & Related papers  (2022-10-07T05:58:05Z)
• Multi-grained Label Refinement Network with Dependency Structures for Joint Intent Detection and Slot Filling [13.963083174197164]
  intent and semantic components of a utterance are dependent on the syntactic elements of a sentence. In this paper, we investigate a multi-grained label refinement network, which utilizes dependency structures and label semantic embeddings. Considering to enhance syntactic representations, we introduce the dependency structures of sentences into our model by graph attention layer.
  arXiv  Detail & Related papers  (2022-09-09T07:27:38Z)
• Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding [51.31622274823167]
  We propose a hierarchical framework with a coarse-to-fine paradigm, with the bottom level shared to all the tasks, the mid-level divided to different groups, and the top-level assigned to each of the tasks. This allows our model to learn basic language properties from all tasks, boost performance on relevant tasks, and reduce the negative impact from irrelevant tasks.
  arXiv  Detail & Related papers  (2022-08-19T02:46:20Z)
• Bi-directional Joint Neural Networks for Intent Classification and Slot Filling [5.3361357265365035]
  We propose a bi-directional joint model for intent classification and slot filling. Our model achieves state-of-the-art results on intent classification accuracy, slot filling F1, and significantly improves sentence-level semantic frame accuracy.
  arXiv  Detail & Related papers  (2022-02-26T06:35:21Z)
• Analyzing the Limits of Self-Supervision in Handling Bias in Language [52.26068057260399]
  We evaluate how well language models capture the semantics of four tasks for bias: diagnosis, identification, extraction and rephrasing. Our analyses indicate that language models are capable of performing these tasks to widely varying degrees across different bias dimensions, such as gender and political affiliation.
  arXiv  Detail & Related papers  (2021-12-16T05:36:08Z)
• Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition [60.36540008537054]
  In this work, we excavate the implicit task, character counting within the traditional text recognition, without additional labor annotation cost. We design a two-branch reciprocal feature learning framework in order to adequately utilize the features from both the tasks. Experiments on 7 benchmarks show the advantages of the proposed methods in both text recognition and the new-built character counting tasks.
  arXiv  Detail & Related papers  (2021-05-13T12:27:35Z)
• Metric Learning vs Classification for Disentangled Music Representation Learning [36.74680586571013]
  We present a single representation learning framework that elucidates the relationship between metric learning, classification, and disentanglement in a holistic manner. We find that classification-based models are generally advantageous for training time, similarity retrieval, and auto-tagging, while deep metric learning exhibits better performance for triplet-prediction.
  arXiv  Detail & Related papers  (2020-08-09T13:53:12Z)
• AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling [69.59096090788125]
  In this paper, we propose an Adaptive Graph-Interactive Framework (AGIF) for joint multiple intent detection and slot filling. We introduce an intent-slot graph interaction layer to model the strong correlation between the slot and intents. Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information.
  arXiv  Detail & Related papers  (2020-04-21T15:07:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.