Generalized Zero-shot Intent Detection via Commonsense Knowledge

• URL: http://arxiv.org/abs/2102.02925v1
• Date: Thu, 4 Feb 2021 23:36:41 GMT
• Title: Generalized Zero-shot Intent Detection via Commonsense Knowledge
• Authors: A.B. Siddique, Fuad Jamour, Luxun Xu, Vagelis Hristidis
• Abstract summary: We propose RIDE: an intent detection model that leverages commonsense knowledge in an unsupervised fashion to overcome the issue of training data scarcity. RIDE computes robust and generalizable relationship meta-features that capture deep semantic relationships between utterances and intent labels. Our extensive experimental analysis on three widely-used intent detection benchmarks shows that relationship meta-features significantly increase the accuracy of detecting both seen and unseen intents.
• Score: 5.398580049917152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying user intents from natural language utterances is a crucial step in conversational systems that has been extensively studied as a supervised classification problem. However, in practice, new intents emerge after deploying an intent detection model. Thus, these models should seamlessly adapt and classify utterances with both seen and unseen intents -- unseen intents emerge after deployment and they do not have training data. The few existing models that target this setting rely heavily on the scarcely available training data and overfit to seen intents data, resulting in a bias to misclassify utterances with unseen intents into seen ones. We propose RIDE: an intent detection model that leverages commonsense knowledge in an unsupervised fashion to overcome the issue of training data scarcity. RIDE computes robust and generalizable relationship meta-features that capture deep semantic relationships between utterances and intent labels; these features are computed by considering how the concepts in an utterance are linked to those in an intent label via commonsense knowledge. Our extensive experimental analysis on three widely-used intent detection benchmarks shows that relationship meta-features significantly increase the accuracy of detecting both seen and unseen intents and that RIDE outperforms the state-of-the-art model for unseen intents.

Related papers

• Decoupling the Class Label and the Target Concept in Machine Unlearning [81.69857244976123]
  Machine unlearning aims to adjust a trained model to approximate a retrained one that excludes a portion of training data. Previous studies showed that class-wise unlearning is successful in forgetting the knowledge of a target class. We propose a general framework, namely, TARget-aware Forgetting (TARF)
  arXiv  Detail & Related papers  (2024-06-12T14:53:30Z)
• Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders [24.42199777529863]
  Large Language Models (LLMs) enable embeddings allowing one to adjust semantics over the embedding space using prompts. Traditional evaluation benchmarks rely solely on task metrics that don't particularly measure gaps related to semantic understanding. We propose an intent semantic toolkit that gives a more holistic view of intent embedding models.
  arXiv  Detail & Related papers  (2024-03-07T08:32:17Z)
• Memory Consistency Guided Divide-and-Conquer Learning for Generalized Category Discovery [56.172872410834664]
  Generalized category discovery (GCD) aims at addressing a more realistic and challenging setting of semi-supervised learning. We propose a Memory Consistency guided Divide-and-conquer Learning framework (MCDL) Our method outperforms state-of-the-art models by a large margin on both seen and unseen classes of the generic image recognition.
  arXiv  Detail & Related papers  (2024-01-24T09:39:45Z)
• Contextual information integration for stance detection via cross-attention [59.662413798388485]
  Stance detection deals with identifying an author's stance towards a target. Most existing stance detection models are limited because they do not consider relevant contextual information. We propose an approach to integrate contextual information as text.
  arXiv  Detail & Related papers  (2022-11-03T15:04:29Z)
• Discovering New Intents Using Latent Variables [51.50374666602328]
  We propose a probabilistic framework for discovering intents where intent assignments are treated as latent variables. In E-step, we conduct discovering intents and explore the intrinsic structure of unlabeled data by the posterior of intent assignments. In M-step, we alleviate the forgetting of prior knowledge transferred from known intents by optimizing the discrimination of labeled data.
  arXiv  Detail & Related papers  (2022-10-21T08:29:45Z)
• Semantic Novelty Detection via Relational Reasoning [17.660958043781154]
  We propose a novel representation learning paradigm based on relational reasoning. Our experiments show that this knowledge is directly transferable to a wide range of scenarios. It can be exploited as a plug-and-play module to convert closed-set recognition models into reliable open-set ones.
  arXiv  Detail & Related papers  (2022-07-18T15:49:27Z)
• Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes [51.65308857232767]
  Vision systems that deploy Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples. Recent research has shown that checking the intrinsic consistencies in the input data is a promising way to detect adversarial attacks. We develop a novel approach to perform context consistency checks using language models.
  arXiv  Detail & Related papers  (2021-08-19T00:52:10Z)
• Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection [93.40525251094071]
  We first point out the importance of in-domain out-of-scope detection in few-shot intent recognition tasks. We then illustrate the vulnerability of pretrained Transformer-based models against samples that are in-domain but out-of-scope (ID-OOS)
  arXiv  Detail & Related papers  (2021-06-08T17:51:12Z)
• Benchmarking Intent Detection for Task-Oriented Dialog Systems [6.54201796167054]
  Intent detection is a key component of modern goal-oriented dialog systems that accomplish a user task by predicting the intent of users' text input. There are three primary challenges in designing robust and accurate intent detection models. Our results show that Watson Assistant's intent detection model outperforms other commercial solutions.
  arXiv  Detail & Related papers  (2020-12-07T18:58:57Z)
• Learning Disentangled Intent Representations for Zero-shot Intent Detection [13.19024497857648]
  We propose a class-transductive framework that utilizes unseen class labels to learn Disentangled Representations (DIR) Under this framework, we introduce a multi-task learning objective, which encourages the model to learn the distinctions among intents. Experiments on two real-world datasets show that the proposed framework brings consistent improvement to the baseline systems.
  arXiv  Detail & Related papers  (2020-12-03T06:41:09Z)

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.