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Joint Intent Classification and Slot Labeling by GluonNLP


Intent classification and slot labeling are two essential problems in Natural Language Understanding (NLU). In intent classification, the agent needs to detect the intention that the speaker's utterance conveys. For example, when the speaker says "Book a flight from Long Beach to Seattle", the intention is to book a flight ticket. In slot labeling, the agent needs to extract the semantic entities that are related to the intent. In our previous example, "Long Beach" and "Seattle" are two semantic constituents related to the flight, i.e., the origin and the destination.

Essentially, intent classification can be viewed as a sequence classification problem and slot labeling can be viewed as a sequence tagging problem similar to Named-entity Recognition (NER). Due to their inner correlation, these two tasks are usually trained jointly with a multi-task objective function.

Here's one example of the ATIS dataset

Sentence Tags Intent Label
are O atis_flight
there O
any O
flights O
from O
long B-fromloc.city_name
beach I-fromloc.city_name
to O
columbus B-toloc.city_name
on O
wednesday B-depart_date.day_name
april B-depart_date.month_name
sixty B-depart_date.day_number

In this example, we demonstrate how to use GluonNLP to build a model to perform joint intent classification and slot labeling. We choose to finetune a pretrained BERT model. We use two datasets ATIS and SNIPS.



You may use pip or other tools to install these packages


For the ATIS dataset, use the following command to run the experiment:

python --gpu 0 --dataset atis

It produces the final slot labeling F1 = 95.83% and intent classification accuracy = 98.66%

For the SNIPS dataset, use the following command to run the experiment:

python --gpu 0 --dataset snips

It produces the final slot labeling F1 = 95.76% and intent classification accuracy = 98.71%

Also, we train the models with three random seeds and report the mean/std


Models Intent Acc (%) Slot F1 (%)
Intent Gating & self-attention, EMNLP 2018 98.77 96.52
BLSTM-CRF + ELMo, AAAI 2019 97.42 95.62
Joint BERT, Arxiv 2019 97.5 96.1
Ours 98.66±0.00 95.88±0.04


Models Intent Acc (%) Slot F1 (%)
BLSTM-CRF + ELMo, AAAI 2019 99.29 93.90
Joint BERT, Arxiv 2019 98.60 97.00
Ours 98.81±0.13 95.94±0.10


Demonstrate how to use GluonNLP to perform intent detection and slot filling



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