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Reviewer/Program Committee
- NLP Venues: NAACL 2021, COLING 2020, SustaiNLP 2020, EMNLP 2020, ACL 2020
- ML/AI Venues: ICLR 2021, ICML 2020, AAAI 2017, NIPS 2016
Presentations
- Invited Talk at Korea University, 27 Nov 2020
- Lecture at DEVIEW, Efficient BERT Inference, 25 Nov 2020
- Invited Talk at Lomin, Recent Trends in Natural Language Processing, 26 Oct 2020
- Guest Lecture at Yonsei University, Pretrained Language Models for Natural Language Processing, 14 Oct 2020
Teaching
- Teaching Assistant, Machine Learning, Seoul National University, Spring 2016
- Tutor, Programming Methodology, Seoul National University, Spring 2014
- Problem Setter, Korean Olympiad in Informatics (KOI), 2010 – 2014
- Student Coach, Training Camp for International Olympiad in Informatics (IOI), 2010 – 2014
Mentor
Github Slot Filling Tool
- Soyoung Yoon, Undergraduate at KAIST, Jul 2020 – Present
- Jungsoo Park, PhD Student at Korea University, Jul 2020 – Present
- Sungbin Kim, MS Student at Inha University, Feb 2020 – Present
- Tae-Hwan Jung, Undergraduate at Kyung Hee University, Dec 2019 – Jun 2020
- Bumju Kwak, Undergraduate at Seoul National University, Apr 2019 – Aug 2019
- Kyungwoo Song, PhD Student at KAIST, Oct 2018 – Dec 2018
Intent Detection and Slot Filling is the task of interpreting user commands/queries by extracting the intent and the relevant slots.
Example (from ATIS):
ATIS
ATIS (Air Travel Information System) (Hemphill et al.) is a dataset by Microsoft CNTK. Available from the github page. The slots are labeled in the BIO (Inside Outside Beginning) format (similar to NER). This dataset contains only air travel related commands. Most of the ATIS results are based on the work here.
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Github Slot Filling Software
| Model | Slot F1 Score | Intent Accuracy | Paper / Source | Code |
|---|---|---|---|---|
| Bi-model with decoder | 96.89 | 98.99 | A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling | |
| Stack-Propagation + BERT | 96.10 | 97.50 | A Stack-Propagation Framework with Token-level Intent Detection for Spoken Language Understanding | Official |
| Stack-Propagation | 95.90 | 96.90 | A Stack-Propagation Framework with Token-level Intent Detection for Spoken Language Understanding | Official |
| Attention Encoder-Decoder NN | 95.87 | 98.43 | Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling | |
| SF-ID (BLSTM) network | 95.80 | 97.76 | A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling | Official |
| Context Encoder | 95.80 | NA | Improving Slot Filling by Utilizing Contextual Information | |
| Capsule-NLU | 95.20 | 95.00 | Joint Slot Filling and Intent Detection via Capsule Neural Networks | Official |
| Joint GRU model(W) | 95.49 | 98.10 | A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding | |
| Slot-Gated BLSTM with Attension | 95.20 | 94.10 | Slot-Gated Modeling for Joint Slot Filling and Intent Prediction | Official |
| Joint model with recurrent slot label context | 94.64 | 98.40 | Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks | Official |
| Recursive NN | 93.96 | 95.40 | JOINT SEMANTIC UTTERANCE CLASSIFICATION AND SLOT FILLING WITH RECURSIVE NEURAL NETWORKS | |
| Encoder-labeler Deep LSTM | 95.66 | NA | Leveraging Sentence-level Information with Encoder LSTM for Natural Language Understanding | |
| RNN with Label Sampling | 94.89 | NA | Recurrent Neural Network Structured Output Prediction for Spoken Language Understanding | |
| Hybrid RNN | 95.06 | NA | Using recurrent neural networks for slot filling in spoken language understanding. | |
| RNN-EM | 95.25 | NA | Recurrent neural networks with external memory for language understanding | |
| CNN-CRF | 94.35 | NA | Convolutional neural network based triangular crf for joint intent detection and slot filling |
SNIPS
Github Slot Filling Machine
- Invited Talk at Korea University, 27 Nov 2020
- Lecture at DEVIEW, Efficient BERT Inference, 25 Nov 2020
- Invited Talk at Lomin, Recent Trends in Natural Language Processing, 26 Oct 2020
- Guest Lecture at Yonsei University, Pretrained Language Models for Natural Language Processing, 14 Oct 2020
Teaching
- Teaching Assistant, Machine Learning, Seoul National University, Spring 2016
- Tutor, Programming Methodology, Seoul National University, Spring 2014
- Problem Setter, Korean Olympiad in Informatics (KOI), 2010 – 2014
- Student Coach, Training Camp for International Olympiad in Informatics (IOI), 2010 – 2014
Mentor
Github Slot Filling Tool
- Soyoung Yoon, Undergraduate at KAIST, Jul 2020 – Present
- Jungsoo Park, PhD Student at Korea University, Jul 2020 – Present
- Sungbin Kim, MS Student at Inha University, Feb 2020 – Present
- Tae-Hwan Jung, Undergraduate at Kyung Hee University, Dec 2019 – Jun 2020
- Bumju Kwak, Undergraduate at Seoul National University, Apr 2019 – Aug 2019
- Kyungwoo Song, PhD Student at KAIST, Oct 2018 – Dec 2018
Intent Detection and Slot Filling is the task of interpreting user commands/queries by extracting the intent and the relevant slots.
Example (from ATIS):
ATIS
ATIS (Air Travel Information System) (Hemphill et al.) is a dataset by Microsoft CNTK. Available from the github page. The slots are labeled in the BIO (Inside Outside Beginning) format (similar to NER). This dataset contains only air travel related commands. Most of the ATIS results are based on the work here.
A jackpot in the big fish casino is the biggest win possible on a slot machine with a single spin. The game offers almost 35k jackpots every single day to win from with a possibility to win up to 16 million chips. Big Fish Casino Cheats, an excellent way out for training, before going to a real casino, or simply spending time with great interest. You have to play for virtual money, which you can win in unlimited quantities. Big fish casino facebook.
Github Slot Filling Software
| Model | Slot F1 Score | Intent Accuracy | Paper / Source | Code |
|---|---|---|---|---|
| Bi-model with decoder | 96.89 | 98.99 | A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling | |
| Stack-Propagation + BERT | 96.10 | 97.50 | A Stack-Propagation Framework with Token-level Intent Detection for Spoken Language Understanding | Official |
| Stack-Propagation | 95.90 | 96.90 | A Stack-Propagation Framework with Token-level Intent Detection for Spoken Language Understanding | Official |
| Attention Encoder-Decoder NN | 95.87 | 98.43 | Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling | |
| SF-ID (BLSTM) network | 95.80 | 97.76 | A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling | Official |
| Context Encoder | 95.80 | NA | Improving Slot Filling by Utilizing Contextual Information | |
| Capsule-NLU | 95.20 | 95.00 | Joint Slot Filling and Intent Detection via Capsule Neural Networks | Official |
| Joint GRU model(W) | 95.49 | 98.10 | A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding | |
| Slot-Gated BLSTM with Attension | 95.20 | 94.10 | Slot-Gated Modeling for Joint Slot Filling and Intent Prediction | Official |
| Joint model with recurrent slot label context | 94.64 | 98.40 | Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks | Official |
| Recursive NN | 93.96 | 95.40 | JOINT SEMANTIC UTTERANCE CLASSIFICATION AND SLOT FILLING WITH RECURSIVE NEURAL NETWORKS | |
| Encoder-labeler Deep LSTM | 95.66 | NA | Leveraging Sentence-level Information with Encoder LSTM for Natural Language Understanding | |
| RNN with Label Sampling | 94.89 | NA | Recurrent Neural Network Structured Output Prediction for Spoken Language Understanding | |
| Hybrid RNN | 95.06 | NA | Using recurrent neural networks for slot filling in spoken language understanding. | |
| RNN-EM | 95.25 | NA | Recurrent neural networks with external memory for language understanding | |
| CNN-CRF | 94.35 | NA | Convolutional neural network based triangular crf for joint intent detection and slot filling |
SNIPS
Github Slot Filling Machine
SNIPS is a dataset by Snips.ai for Intent Detection and Slot Filling benchmarking. Available from the github page. This dataset contains several day to day user command categories (e.g. play a song, book a restaurant).
Slot Filling Github
| Model | Slot F1 Score | Intent Accuracy | Paper / Source | Code |
|---|---|---|---|---|
| Stack-Propagation + BERT | 97.00 | 99.00 | A Stack-Propagation Framework with Token-level Intent Detection for Spoken Language Understanding | Official |
| Stack-Propagation | 94.20 | 98.00 | A Stack-Propagation Framework with Token-level Intent Detection for Spoken Language Understanding | Official |
| Context Encoder | 93.60 | NA | Improving Slot Filling by Utilizing Contextual Information | |
| SF-ID (BLSTM) network | 92.23 | 97.43 | A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling | Official |
| Capsule-NLU | 91.80 | 97.70 | Joint Slot Filling and Intent Detection via Capsule Neural Networks | Official |
| Slot-Gated BLSTM with Attension | 88.80 | 97.00 | Slot-Gated Modeling for Joint Slot Filling and Intent Prediction | Official |
