Few-Shot-Intent-Detection includes popular challenging intent detection datasets with/without OOS queries and state-of-the-art baselines and results.

Overview

Few-Shot-Intent-Detection

Few-Shot-Intent-Detection is a repository designed for few-shot intent detection with/without Out-of-Scope (OOS) intents. It includes popular challenging intent detection datasets and baselines. For more details of the new released OOS datasets, please check our paper.

Intent detection datasets

We process data based on previous published resources, all the data are in the same format as DNNC.

Dataset Description #Train #Valid #Test Processed Data Link
BANKING77 one banking domain with 77 intents 8622 1540 3080 Link
CLINC150 10 domains and 150 intents 15000 3000 4500 Link
HWU64 personal assistant with 64 intents and several domains 8954 1076 1076 Link
SNIPS snips voice platform with 7 intents 13084 700 700 Link
ATIS airline travel information system 4478 500 893 Link

Intent detection datasets with OOS queries

What is OOS queires:

OOD-OOS: i.e., out-of-domain OOS. General out-of-scope queries which are not supported by the dialog systems, also called out-of-domain OOS. For instance, requesting an online NBA/TV show service in a banking system.

ID-OOS: i.e., in-domain OOS. Out-of-scope queries which are more related to the in-scope intents, which makes the intent detection task more challenging. For instance, requesting a banking service that is not supported by the banking system.

Dataset Description #Train #Valid #Test #OOD-OOS-Train #OOD-OOS-Valid #OOD-OOS-Test #ID-OOS-Train #ID-OOS-Valid #ID-OOS-Test Processed Data Link
CLINC150 A dataset with general OOS-OOS queries 15000 3000 4500 100 100 1000 - - - Link
CLINC-Single-Domain-OOS Two domains with both general OOS-OOS queries and ID-OOS queries 500 500 500 - 200 1000 - 400 350 Link
BANKING77-OOS One banking domain with both general OOS-OOS queries and ID-OOS queries 5905 1506 2000 - 200 1000 2062 530 1080 Link

Data structure:

Datasets/
├── BANKING77
│   ├── train
│   ├── train_10
│   ├── train_5
│   ├── valid
│   └── test
├── CLINC150
│   ├── train
│   ├── train_10
│   ├── train_5
│   ├── valid
│   ├── test
│   ├── oos
│       ├──train
│       ├──valid
│       └──test
├── HWU64
│   ├── train
│   ├── train_10
│   ├── train_5
│   ├── valid
│   └── test
├── SNIPS
│   ├── train
│   ├── valid
│   └── test
├── ATIS
│   ├── train
│   ├── valid
│   └── test
├── BANKING77-OOS
│   ├── train
│   ├── valid
│   ├── test
│   ├── id-oos
│   │   ├──train
│   │   ├──valid
│   │   └──test
│   ├── ood-oos
│       ├──valid
│       └──test
├── CLINC-Single-Domain-OOS
│   ├── banking
│   │   ├── train
│   │   ├── valid
│   │   ├── test
│   │   ├── id-oos
│   │   │   ├──valid
│   │   │   └──test
│   │   ├── ood-oos
│   │       ├──valid
│   │       └──test
│   ├── credit_cards
│   │   ├── train
│   │   ├── valid
│   │   ├── test
│   │   ├── id-oos
│   │   │   ├──valid
│   │   │   └──test
│   │   ├── ood-oos
│   │       ├──valid
└── └──     └──test

Briefly describe the BANKING77-OOS dataset.

  • A dataset with a single banking domain, includes both general Out-of-Scope (OOD-OOS) queries and In-Domain but Out-of-Scope (ID-OOS) queries, where ID-OOS queries are semantically similar intents/queries with in-scope intents. BANKING77 originally includes 77 intents. BANKING77-OOS includes 50 in-scope intents in this dataset, and the ID-OOS queries are built up based on 27 held-out semantically similar in-scope intents.

Briefly describe the CLINC-Single-Domain-OOS dataset.

  • A dataset with two separate domains, i.e., the "Banking'' domain and the "Credit cards'' domain with both general Out-of-Scope (OOD-OOS) queries and In-Domain but Out-of-Scope (ID-OOS) queries, where ID-OOS queries are semantically similar intents/queries with in-scope intents. Each domain in CLINC150 originally includes 15 intents. Each domain in the new dataset includes ten in-scope intents in this dataset, and the ID-OOS queries are built up based on five held-out semantically similar in-scope intents.

Both datasets can be used to conduct intent detection with and without OOD-OOS and ID-OOS queries

You can easily load the processed data:

class IntentExample:
    def __init__(self, text, label, do_lower_case):
        self.original_text = text
        self.text = text
        self.label = label

        if do_lower_case:
            self.text = self.text.lower()
        
def load_intent_examples(file_path, do_lower_case=True):
    examples = []

    with open('{}/seq.in'.format(file_path), 'r', encoding="utf-8") as f_text, open('{}/label'.format(file_path), 'r', encoding="utf-8") as f_label:
        for text, label in zip(f_text, f_label):
            e = IntentExample(text.strip(), label.strip(), do_lower_case)
            examples.append(e)

    return examples

More details can check code for load data and do random sampling for few-shot learning.

State-of-the art models and baselines

DNNC

Download pre-trained RoBERTa NLI checkpoint:

wget https://storage.googleapis.com/sfr-dnnc-few-shot-intent/roberta_nli.zip

Access to public code: Link

CONVERT

Download pre-trained checkpoint:

wget https://github.com/connorbrinton/polyai-models/releases/download/v1.0/model.tar.gz

Access to public code:

wget https://github.com/connorbrinton/polyai-models/archive/refs/tags/v1.0.zip

CONVBERT

Download pre-trained checkpoints:

Step-1: install AWS CL2: e.g., install MacOS PKG

Step-2:

aws s3 cp s3://dialoglue/ --no-sign-request `Your_folder_name` --recursive

Then the checkpoints are downloaded into Your_folder_name

Few-shot intent detection baselines/leaderboard:

5-shot learning

Model BANKING77 CLICN150 HWU64
RoBERTa+Classifier (EMNLP 2020) 74.04 87.99 75.56
USE (ACL 2020 NLP4ConvAI) 76.29 87.82 77.79
CONVERT (ACL 2020 NLP4ConvAI) 75.32 89.22 76.95
USE+CONVERT (ACL 2020 NLP4ConvAI) 77.75 90.49 80.01
CONVBERT+MLM+Example+Observers (NAACL 2021) - - -
DNNC (EMNLP 2020) 80.40 91.02 80.46
CPFT (EMNLP 2021) 80.86 92.34 82.03

10-shot learning

Model BANKING77 CLICN150 HWU64
RoBERTa+Classifier (EMNLP 2020) 84.27 91.55 82.90
USE (ACL 2020 NLP4ConvAI) 84.23 90.85 83.75
CONVERT(ACL 2020 NLP4ConvAI) 83.32 92.62 82.65
USE+CONVERT (ACL 2020 NLP4ConvAI) 85.19 93.26 85.83
CONVBERT (ArXiv 2020) 83.63 92.10 83.77
CONVBERT+MLM (ArXiv 2020) 83.99 92.75 84.52
CONVBERT+MLM+Example+Observers (NAACL 2021) 85.95 93.97 86.28
DNNC (EMNLP 2020) 86.71 93.76 84.72
CPFT (EMNLP 2021) 87.20 94.18 87.13

Note: the 5-shot learning results of RoBERTa+Classifier, DNNC and CPFT, and the 10-shot learning results of all the models are reported by the paper authors.

Citation

Please cite our paper if you use above resources in your work:

@article{zhang2020discriminative,
  title={Discriminative nearest neighbor few-shot intent detection by transferring natural language inference},
  author={Zhang, Jian-Guo and Hashimoto, Kazuma and Liu, Wenhao and Wu, Chien-Sheng and Wan, Yao and Yu, Philip S and Socher, Richard and Xiong, Caiming},
  journal={EMNLP},
  pages={5064--5082},
  year={2020}
}

@article{zhang2021pretrained,
  title={Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection},
  author={Zhang, Jian-Guo and Hashimoto, Kazuma and Wan, Yao and Liu, Ye and Xiong, Caiming and Yu, Philip S},
  journal={arXiv preprint arXiv:2106.04564},
  year={2021}
}

@article{zhang2021few,
  title={Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning},
  author={Zhang, Jianguo and Bui, Trung and Yoon, Seunghyun and Chen, Xiang and Liu, Zhiwei and Xia, Congying and Tran, Quan Hung and Chang, Walter and Yu, Philip},
  journal={EMNLP},
  year={2021}
}
Owner
Jian-Guo Zhang
Jian-Guo Zhang
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