| Challenge: | Existing approaches to intent detection have limited data annotated for new domains or languages. |
| Approach: | They propose to train a set of pretraining intent detection models on wikiHow which can predict a broad range of intended goals from many actions. |
| Outcome: | The proposed models achieve state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue dataset, and all 3 languages of the Facebook multilingual dialog datasets. |
Similar Papers
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations (2021.naacl-industry)
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| Challenge: | Intent detection models require large amounts of labeled data to achieve high accuracy, and in practical scenarios it is more common to find small, unbalanced, and noisy datasets. |
| Approach: | They benchmark intent detection methods on a variety of datasets and found that Watson Assistant's model outperforms other commercial solutions. |
| Outcome: | The proposed model outperforms pretrained language models on a variety of datasets while requiring only a fraction of computational resources and training data. |
From Discrimination to Generation: Low-Resource Intent Detection with Language Model Instruction Tuning (2024.findings-acl)
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| Challenge: | Existing studies fine-tune discriminative models on specific defined intent classes, preventing them from being directly adopted to new intent domains. |
| Approach: | They propose to use a pre-trained generative intent model to detect new intents from different domains with no parameter updates. |
| Outcome: | The proposed model outperforms baselines that need further fine-tuning or domain-specific samples. |
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning (2021.emnlp-main)
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Jianguo Zhang, Trung Bui, Seunghyun Yoon, Xiang Chen, Zhiwei Liu, Congying Xia, Quan Hung Tran, Walter Chang, Philip Yu
| Challenge: | Existing methods address few-shot intent detection tasks from two perspectives: data augmentation and task-adaptive training with pre-trained models. |
| Approach: | They propose a few-shot intent detection schema using contrastive pre-training and fine-tuning. |
| Outcome: | The proposed method achieves state-of-the-art performance on three challenging intent detection datasets under 5-shot and 10-shot settings. |
Example-Driven Intent Prediction with Observers (2021.naacl-main)
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| Challenge: | Prior work has shown that BERT-like models attribute a significant amount of attention to the [CLS] token, which results in diluted representations. |
| Approach: | They propose two approaches to improve generalizability of dialog system intent classification models by using observers and example-driven training. |
| Outcome: | The proposed models achieve state-of-the-art on three intent prediction datasets in both the full data and few-shot settings. |
PCMID: Multi-Intent Detection through Supervised Prototypical Contrastive Learning (2023.findings-emnlp)
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| Challenge: | Existing approaches to intent detection assume that each utterance represents only a single intent. |
| Approach: | They propose a framework for intent detection that can learn multiple representations of a given user utterance under the context of different intent labels in an optimized semantic space. |
| Outcome: | The proposed framework achieves state-of-the-art on multiple public benchmark datasets and a private real-world dataset for the multi-intent detection task. |
Multilingual and Cross-Lingual Intent Detection from Spoken Data (2021.emnlp-main)
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Daniela Gerz, Pei-Hao Su, Razvan Kusztos, Avishek Mondal, Michał Lis, Eshan Singhal, Nikola Mrkšić, Tsung-Hsien Wen, Ivan Vulić
| Challenge: | a systematic study on multilingual and cross-lingual intent detection from spoken data is presented . current work on intent detection is limited to English, and standard benchmarks exist only in English. |
| Approach: | They present a systematic study on multilingual and cross-lingual intent detection from spoken data. |
| Outcome: | The proposed resource is called MInDS-14, and it provides strong intent detection in most target languages. |
Intent Features for Rich Natural Language Understanding (2021.naacl-industry)
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| Challenge: | generic dialog systems, or chatbots, are increasingly popular, but most industrial dialog systems are built for specific clients and use cases. |
| Approach: | They propose a new neural network architecture that allows for domain and topic agnostic properties of intents that can be learnt from syntactic cues only. |
| Outcome: | The proposed model improves on baselines for identifying intent features in a deployed, multi-intent natural language understanding module. |
Reimagining Intent Prediction: Insights from Graph-Based Dialogue Modeling and Sentence Encoders (2024.lrec-main)
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| Challenge: | Existing approaches to intent prediction are limited in highly specialized fields, such as closed-domain dialogue systems, where context comprehension is of paramount importance. |
| Approach: | They propose a method that uses scenario dialog graphs to model dialogues as sequences of transitions between intents, representing distinct goals or requests. |
| Outcome: | The proposed method significantly advances the field of dialogue systems, providing valuable insights into the effectiveness and potential limitations of the proposed approaches. |
Evaluating Intention Detection Capability of Large Language Models in Persuasive Dialogues (2024.acl-long)
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| Challenge: | Existing studies measure the intention detection capability of machine learning models without considering the conversational history. |
| Approach: | They modified existing persuasive conversation datasets and created a dataset using a multiple-choice paradigm to evaluate LLMs' intention detection capability. |
| Outcome: | The proposed model can detect speakers' intentions well in persuasive multi-turn dialogs using the largest available Large Language Models (LLMs). |
Effectiveness of Pre-training for Few-shot Intent Classification (2021.findings-emnlp)
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| Challenge: | Existing paradigms further pre-train language models such as BERT on vast amount of unlabeled corpus, but we find it highly effective and efficient to simply fine-tune BERT with roughly 1,000 labeled utterances from public datasets. |
| Approach: | They propose to fine-tune BERT with a small set of labeled utterances from public datasets to achieve a pre-trained model based on a set of 1,000 labeles. |
| Outcome: | The proposed model can outperform existing models on domains with very different semantics on novel domains. |