Papers with Intent classification
STIL - Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++ (2020.aacl-main)
Copied to clipboard
| Challenge: | Slot-filling, Translation, Intent classification, and Language identification (STIL) are tasks for multilingual Natural Language Understanding (NLU) . |
| Approach: | They propose to perform simultaneous slot filling and translation into a single output language (English in this case). |
| Outcome: | The proposed task performs better than the current state-of-the-art system for the languages tested, but with lower intent classification accuracy and lower slot F1 . |
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Intent classification and slot filling are key building blocks in task-oriented dialogue systems. |
| Approach: | They propose an explicit-joint and supervised-contrastive learning framework for few-shot intent classification and slot filling. |
| Outcome: | The proposed model extracts intent and slot representations via bidirectional interactions and extends prototypical network to achieve explicit-joint learning. |
Enhancing the generalization for Intent Classification and Out-of-Domain Detection in SLU (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for intent classification are expensive to collect and train . evaluators have shown that the ability to detect out-of-domain utterances is limited . |
| Approach: | They propose to train a model with only IND data while supporting both intent classification and OOD detection. |
| Outcome: | The proposed model improves on existing models and strong baselines on four datasets. |
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages (2023.acl-long)
Copied to clipboard
Jack FitzGerald, Christopher Hench, Charith Peris, Scott Mackie, Kay Rottmann, Ana Sanchez, Aaron Nash, Liam Urbach, Vishesh Kakarala, Richa Singh, Swetha Ranganath, Laurie Crist, Misha Britan, Wouter Leeuwis, Gokhan Tur, Prem Natarajan
| Challenge: | We present the MASSIVE dataset–Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant evaluation. |
| Approach: | They present a 1M-example dataset of Amazon Slu utterances . they localize the dataset into 50 typologically diverse languages . |
| Outcome: | The proposed model includes exact match accuracy, intent classification accuracy, and slot-filling F1 score. |
Reconstructing Capsule Networks for Zero-shot Intent Classification (D19-1)
Copied to clipboard
| Challenge: | Existing methods for intent classification are limited due to fast-emerging intents . a recent study shows that existing methods are not effective in recognizing unseen intents. |
| Approach: | They propose to reconstruct capsule networks for zero-shot intent classification by using latent information from labeled utterances. |
| Outcome: | The proposed method outperforms existing methods on two task-oriented dialogue datasets in different languages. |
Main Predicate and Their Arguments as Explanation Signals For Intent Classification (2025.naacl-long)
Copied to clipboard
| Challenge: | Intent classification is crucial for conversational agents, and deep learning models perform well in this area due to the lack of suitable benchmark data. |
| Approach: | They propose a technique to augment text samples from intent classification datasets with word-level explanations by marking main predicates and their arguments as explanation signals. |
| Outcome: | The proposed method augments text samples from intent classification datasets with word-level explanations. |
Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification (2023.emnlp-main)
Copied to clipboard
Mujeen Sung, James Gung, Elman Mansimov, Nikolaos Pappas, Raphael Shu, Salvatore Romeo, Yi Zhang, Vittorio Castelli
| Challenge: | Existing methods for IC training do not provide sufficient examples for each intent . a novel pre-training method is proposed to provide a better understanding of intents . |
| Approach: | They propose a method that uses contrastive learning with intent psuedo-labels to produce embeddings that are well-suited for IC tasks. |
| Outcome: | The proposed method achieves 5.4% and 4.0% higher accuracy than the current state-of-the-art method on four IC datasets. |
New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark (2024.lrec-main)
Copied to clipboard
| Challenge: | Intent classification and slot-filling tasks are essential tasks of Spoken Language Understanding (SLU). |
| Approach: | They propose to use a MEDIA SLU dataset to train a multilingual model to achieve both tasks jointly. |
| Outcome: | The proposed model can be trained on multiple datasets including the MEDIA dataset and extends to more tasks and use cases. |