https://github.com/applenob/rnn-for-joint-nlu
Tensorflow implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling" (https://arxiv.org/abs/1609.01454)
https://github.com/applenob/rnn-for-joint-nlu
nlu seq2seq sequence-labeling slot-filling tensorflow
Last synced: 6 months ago
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Tensorflow implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling" (https://arxiv.org/abs/1609.01454)
- Host: GitHub
- URL: https://github.com/applenob/rnn-for-joint-nlu
- Owner: applenob
- Created: 2017-10-19T02:53:53.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2018-01-05T01:56:55.000Z (almost 8 years ago)
- Last Synced: 2025-04-09T15:06:08.111Z (6 months ago)
- Topics: nlu, seq2seq, sequence-labeling, slot-filling, tensorflow
- Language: Jupyter Notebook
- Size: 402 KB
- Stars: 281
- Watchers: 17
- Forks: 96
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# RNN-for-Joint-NLU
## 模型介绍
使用tensorflow r1.3 api，Encoder使用`tf.nn.bidirectional_dynamic_rnn`实现，Decoder使用`tf.contrib.seq2seq.CustomHelper`和`tf.contrib.seq2seq.dynamic_decode`实现。
[原作者Bing Liu的Tensorflow实现](https://github.com/HadoopIt/rnn-nlu)
我的实现相对比较简单，用于学习目的。
## 使用
```
python main.py
```
输出：
```
[Epoch 27] Average train loss: 0.0
Input Sentence : ['what', 'are', 'the', 'flights', 'and', 'prices', 'from', 'la', 'to', 'charlotte', 'for', 'monday', 'morning']
Slot Truth : ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-fromloc.city_name', 'O', 'B-toloc.city_name', 'O', 'B-depart_date.day_name', 'B-depart_time.period_of_day']
Slot Prediction : ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-fromloc.city_name', 'O', 'B-toloc.city_name', 'O', 'B-depart_date.day_name', 'B-depart_time.period_of_day']
Intent Truth : atis_flight
Intent Prediction : atis_flight#atis_airfare
Intent accuracy for epoch 27: 0.969758064516129
Slot accuracy for epoch 27: 0.9782146713160718
Slot F1 score for epoch 27: 0.977950943062074
[Epoch 28] Average train loss: 0.0
Input Sentence : ['show', 'me', 'the', 'last', 'flight', 'from', 'love', 'field']
Slot Truth : ['O', 'O', 'O', 'B-flight_mod', 'O', 'O', 'B-fromloc.airport_name', 'I-fromloc.airport_name']
Slot Prediction : ['O', 'O', 'O', 'B-flight_mod', 'O', 'O', 'B-fromloc.airport_name', 'I-fromloc.airport_name']
Intent Truth : atis_flight
Intent Prediction : atis_flight
Intent accuracy for epoch 28: 0.9717741935483871
Slot accuracy for epoch 28: 0.9794670271393975
Slot F1 score for epoch 28: 0.9792847025495751
```
## 细节
博客文章：
- [Tensorflow动态seq2seq使用总结（r1.3）](https://github.com/applenob/RNN-for-Joint-NLU/blob/master/tensorflow_dynamic_seq2seq.md)