Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference

• URL: http://arxiv.org/abs/2010.13009v1
• Date: Sun, 25 Oct 2020 00:39:32 GMT
• Title: Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference
• Authors: Jian-Guo Zhang, Kazuma Hashimoto, Wenhao Liu, Chien-Sheng Wu, Yao Wan, Philip S. Yu, Richard Socher, Caiming Xiong
• Abstract summary: Few-shot learning is attracting much attention to mitigate data scarcity. We present a discriminative nearest neighbor classification with deep self-attention. We propose to boost the discriminative ability by transferring a natural language inference (NLI) model.
• Score: 150.07326223077405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is attracting much attention to mitigate data scarcity, but OOS detection becomes even more challenging. In this paper, we present a simple yet effective approach, discriminative nearest neighbor classification with deep self-attention. Unlike softmax classifiers, we leverage BERT-style pairwise encoding to train a binary classifier that estimates the best matched training example for a user input. We propose to boost the discriminative ability by transferring a natural language inference (NLI) model. Our extensive experiments on a large-scale multi-domain intent detection task show that our method achieves more stable and accurate in-domain and OOS detection accuracy than RoBERTa-based classifiers and embedding-based nearest neighbor approaches. More notably, the NLI transfer enables our 10-shot model to perform competitively with 50-shot or even full-shot classifiers, while we can keep the inference time constant by leveraging a faster embedding retrieval model.

Related papers

• Intent Detection in the Age of LLMs [3.755082744150185]
  Intent detection is a critical component of task-oriented dialogue systems (TODS) Traditional approaches relied on computationally efficient supervised sentence transformer encoder models. The emergence of generative large language models (LLMs) with intrinsic world knowledge presents new opportunities to address these challenges.
  arXiv  Detail & Related papers  (2024-10-02T15:01:55Z)
• Diversity-grounded Channel Prototypical Learning for Out-of-Distribution Intent Detection [18.275098909064127]
  This study presents a novel fine-tuning framework for large language models (LLMs) We construct semantic prototypes for each ID class using a diversity-grounded prompt tuning approach. For a thorough assessment, we benchmark our method against the prevalent fine-tuning approaches.
  arXiv  Detail & Related papers  (2024-09-17T12:07:17Z)
• Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching [53.05954114863596]
  We propose a brand-new Deep Boosting Learning (DBL) algorithm for image-text matching. An anchor branch is first trained to provide insights into the data properties. A target branch is concurrently tasked with more adaptive margin constraints to further enlarge the relative distance between matched and unmatched samples.
  arXiv  Detail & Related papers  (2024-04-28T08:44:28Z)
• Large Class Separation is not what you need for Relational Reasoning-based OOD Detection [12.578844450586]
  Out-Of-Distribution (OOD) detection methods provide a solution by identifying semantic novelty. Most of these methods leverage a learning stage on the known data, which means training (or fine-tuning) a model to capture the concept of normality. A viable alternative is that of evaluating similarities in the embedding space produced by large pre-trained models without any further learning effort.
  arXiv  Detail & Related papers  (2023-07-12T14:10:15Z)
• Zero-Shot Temporal Action Detection via Vision-Language Prompting [134.26292288193298]
  We propose a novel zero-Shot Temporal Action detection model via Vision-LanguagE prompting (STALE) Our model significantly outperforms state-of-the-art alternatives. Our model also yields superior results on supervised TAD over recent strong competitors.
  arXiv  Detail & Related papers  (2022-07-17T13:59:46Z)
• Compare learning: bi-attention network for few-shot learning [6.559037166322981]
  One of the Few-shot learning methods called metric learning addresses this challenge by first learning a deep distance metric to determine whether a pair of images belong to the same category. In this paper, we propose a novel approach named Bi-attention network to compare the instances, which can measure the similarity between embeddings of instances precisely, globally and efficiently.
  arXiv  Detail & Related papers  (2022-03-25T07:39:10Z)
• Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning [101.28281124670647]
  Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data. We propose a novel training mechanism that could effectively exploit the presence of OOD data for enhanced feature learning. Our approach substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2021-08-12T09:14:44Z)
• Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training [20.242645823965145]
  Out-of-scope intent detection is of practical importance in task-oriented dialogue systems. We 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. We evaluate our method extensively on four benchmark dialogue datasets and observe significant improvements over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-06-16T08:17:18Z)
• Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference [59.62779187457773]
  We propose a generative classifier for natural language inference (NLI) We compare it to five baselines, including discriminative models and large-scale pretrained language representation models like BERT. Experiments show that GenNLI outperforms both discriminative and pretrained baselines across several challenging NLI experimental settings.
  arXiv  Detail & Related papers  (2020-10-08T04:44:00Z)
• Scope Head for Accurate Localization in Object Detection [135.9979405835606]
  We propose a novel detector coined as ScopeNet, which models anchors of each location as a mutually dependent relationship. With our concise and effective design, the proposed ScopeNet achieves state-of-the-art results on COCO.
  arXiv  Detail & Related papers  (2020-05-11T04:00:09Z)

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.