SOTAVerified

Intent Detection

Intent Detection is a task of determining the underlying purpose or goal behind a user's search query given a context. The task plays a significant role in search and recommendations. A traditional approach for intent detection implies using an intent detector model to classify user search query into predefined intent categories, given a context. One of the key challenges of the task implies identifying user intents for cold-start sessions, i.e., search sessions initiated by a non-logged-in or unrecognized user.

Source: Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers

Papers

Showing 101125 of 330 papers

TitleStatusHype
Discerning the Chaos: Detecting Adversarial Perturbations while Disentangling Intentional from Unintentional Noises0
Discovering Customer-Service Dialog System with Semi-Supervised Learning and Coarse-to-Fine Intent Detection0
ConvFiT: Conversational Fine-Tuning of Pretrained Language Models0
A Streaming End-to-End Framework For Spoken Language Understanding0
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing0
Diversity-grounded Channel Prototypical Learning for Out-of-Distribution Intent Detection0
An Interdisciplinary Review of Commonsense Reasoning and Intent Detection0
dzFinNlp at AraFinNLP: Improving Intent Detection in Financial Conversational Agents0
Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling0
Generalized Multiple Intent Conditioned Slot Filling0
Continuous representations of intents for dialogue systems0
Continual Learning with Dirichlet Generative-based Rehearsal0
Embedding Grammars0
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models0
A Graph-to-Sequence Model for Joint Intent Detection and Slot Filling in Task-Oriented Dialogue Systems0
Continual Few-shot Intent Detection0
Adapting Task-Oriented Dialogue Models for Email Conversations0
Enhanced Urdu Intent Detection with Large Language Models and Prototype-Informed Predictive Pipelines0
Enhancing Slot Tagging with Intent Features for Task Oriented Natural Language Understanding using BERT0
Enriched Pre-trained Transformers for Joint Slot Filling and Intent Detection0
ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation0
Estimating Soft Labels for Out-of-Domain Intent Detection0
Evaluating Pixel Language Models on Non-Standardized Languages0
Expanding the Text Classification Toolbox with Cross-Lingual Embeddings0
A Semi Supervised Dialog Act Tagging for Telugu0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Bi-model with decoderAccuracy98.99Unverified
2Transformer-CapsuleAccuracy98.89Unverified
3Attention Encoder-Decoder NNAccuracy98.43Unverified
4Joint model with recurrent slot label contextAccuracy98.4Unverified
5CTRANAccuracy98.07Unverified
6Joint BERT + CRFAccuracy97.9Unverified
7SF-ID (BLSTM) networkAccuracy97.76Unverified
8SF-IDAccuracy97.76Unverified
9JointBERT-CAEAccuracy97.5Unverified
10Stack-Propagation (+BERT)Accuracy97.5Unverified
#ModelMetricClaimedVerifiedStatus
1SSRANAccuracy98.4Unverified
2BiSLUAccuracy97.8Unverified
3DGIFAccuracy97.8Unverified
4TFMNAccuracy97.7Unverified
5Co-guiding NetAccuracy97.7Unverified
6TFMN (PACL)Accuracy97.4Unverified
7MISCAAccuracy97.3Unverified
8Uni-MISAccuracy97.2Unverified
9SLIMAccuracy97.2Unverified
10SLIM (PACL)Accuracy96.9Unverified
#ModelMetricClaimedVerifiedStatus
1DGIFAccuracy83.3Unverified
2UGENAccuracy83Unverified
3TFMN (PACL)Accuracy82.9Unverified
4SLIM (PACL)Accuracy81.9Unverified
5BiSLUAccuracy81.5Unverified
6TFMNAccuracy79.8Unverified
7Co-guiding NetAccuracy79.1Unverified
8RoBERTa (PACL)Accuracy79.1Unverified
9Uni-MISAccuracy78.5Unverified
10SLIMAccuracy78.3Unverified
#ModelMetricClaimedVerifiedStatus
1CTRANAccuracy99.42Unverified
2Stack-Propagation (+BERT)Accuracy99Unverified
3JointBERT-CAEAccuracy98.3Unverified
4AGIFAccuracy98.1Unverified
5LIDSNetAccuracy98Unverified
6Stack-PropagationAccuracy98Unverified
7SF-IDAccuracy97.43Unverified
8SF-ID (BLSTM) networkAccuracy97.43Unverified
9Capsule-NLUAccuracy97.3Unverified
10Slot-Gated BLSTM with AttensionAccuracy97Unverified
#ModelMetricClaimedVerifiedStatus
1plain-LSTMF10.89Unverified
2linear-NgramsF10.87Unverified
3glove-LSTMF10.86Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)94.42Unverified
2OCaTS (kNN-GPT-4)Accuracy (%)82.7Unverified
#ModelMetricClaimedVerifiedStatus
1JointBERT-CAEIntent Accuracy97.7Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)89.79Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)84.01Unverified
#ModelMetricClaimedVerifiedStatus
1CM-NetAcc94.56Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)97.12Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)94.84Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)92.62Unverified
#ModelMetricClaimedVerifiedStatus
1MIDASAccuracy94.27Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)92.57Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)87.41Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)82.45Unverified
#ModelMetricClaimedVerifiedStatus
1MIDASAccuarcy85.02Unverified
#ModelMetricClaimedVerifiedStatus
1General SLU Model w/ ProfileAccuracy0.85Unverified