***** Top 3 single model on CoQA leaderboard as of Sep 13th, 2019*****
***** Top 3 single model on QuAC leaderboard as of Nov 1st, 2019*****
XLNet is a generalized autoregressive pretraining method proposed by CMU & Google Brain, which outperforms BERT on 20 NLP tasks ranging from question answering, natural language inference, sentiment analysis, and document ranking. XLNet is inspired by the pros and cons of auto-regressive and auto-encoding methods to overcome limitation of both sides, which uses a permutation language modeling objective to learn bidirectional context and integrates ideas from Transformer-XL into model architecture. This project is aiming to provide extensions built on top of current XLNet and bring power of XLNet to other NLP tasks like NER and NLU.
Figure 1: Illustrations of fine-tuning XLNet on different tasks
- Python 3.6.7
- Tensorflow 1.13.1
- NumPy 1.13.3
- SentencePiece 0.1.82
- CoNLL2003 is a multi-task dataset, which contains 3 sub-tasks, POS tagging, syntactic chunking and NER. For NER sub-task, it contains 4 types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups.
- ATIS (Airline Travel Information System) is NLU dataset in airline travel domain. The dataset contains 4978 train and 893 test utterances classified into one of 26 intents, and each token in utterance is labeled with tags from 128 slot filling tags in IOB format.
- SQuAD is a reading comprehension dataset, consisting of questions posed by crowd-workers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
- CoQA a large-scale dataset for building Conversational Question Answering systems. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation. CoQA is pronounced as coca
- QuAC is a dataset for modeling, understanding, and participating in information seeking dialog. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context.
- Preprocess data
python prepro/prepro_conll.py \
--data_format json \
--input_file data/ner/conll2003/raw/eng.xxx \
--output_file data/ner/conll2003/xxx-conll2003/xxx-conll2003.json- Run experiment
CUDA_VISIBLE_DEVICES=0 python run_ner.py \
--spiece_model_file=model/cased_L-24_H-1024_A-16/spiece.model \
--model_config_path=model/cased_L-24_H-1024_A-16/xlnet_config.json \
--init_checkpoint=model/cased_L-24_H-1024_A-16/xlnet_model.ckpt \
--task_name=conll2003 \
--random_seed=100 \
--predict_tag=xxxxx \
--data_dir=data/ner/conll2003 \
--output_dir=output/ner/conll2003/data \
--model_dir=output/ner/conll2003/checkpoint \
--export_dir=output/ner/conll2003/export \
--max_seq_length=128 \
--train_batch_size=32 \
--num_hosts=1 \
--num_core_per_host=1 \
--learning_rate=2e-5 \
--train_steps=2500 \
--warmup_steps=100 \
--save_steps=500 \
--do_train=true \
--do_eval=true \
--do_predict=true \
--do_export=true- Visualize summary
tensorboard --logdir=output/ner/conll2003- Setup service
docker run -p 8500:8500 \
-v output/ner/conll2003/export/xxxxx:models/ner \
-e MODEL_NAME=ner \
-t tensorflow/serving
Figure 2: Illustrations of fine-tuning XLNet on CoNLL2003-NER task
| CoNLL2003 - NER | Avg. (5-run) | Best |
|---|---|---|
| Precision | 91.36 ± 0.50 | 92.14 |
| Recall | 92.95 ± 0.24 | 93.20 |
| F1 Score | 92.15 ± 0.35 | 92.67 |
Table 1: The test set performance of XLNet-large finetuned model on CoNLL2003-NER task with setting: batch size = 16, max length = 128, learning rate = 2e-5, num steps = 4,000
Figure 3: Illustrations of fine-tuning XLNet on ATIS-NLU task
| ATIS - NLU | Avg. (5-run) | Best |
|---|---|---|
| Accuracy - Intent | 97.51 ± 0.09 | 97.54 |
| F1 Score - Slot | 95.48 ± 0.30 | 95.73 |
Table 2: The test set performance of XLNet-large finetuned model on ATIS-NLU task with setting: batch size = 16, max length = 128, learning rate = 5e-5, num steps = 2,000
Figure 4: Illustrations of fine-tuning XLNet on SQuAD v1.1 task
| SQuAD v1.1 | Avg. (5-run) | Best |
|---|---|---|
| Exact Match | xx.xx ± x.xx | 88.61 |
| F1 Score | xx.xx ± x.xx | 94.28 |
Table 3: The test set performance of XLNet-large finetuned model on SQuAD v1.1 task with setting: batch size = 48, max sequence length = 512, max question length = 64, learning rate = 3e-5, num steps = 8,000
Figure 5: Illustrations of fine-tuning XLNet on SQuAD v2.0 task
| SQuAD v2.0 | Avg. (5-run) | Best |
|---|---|---|
| Exact Match | xx.xx ± x.xx | 85.72 |
| F1 Score | xx.xx ± x.xx | 88.36 |
Table 4: The test set performance of XLNet-large finetuned model on SQuAD v2.0 task with setting: batch size = 48, max sequence length = 512, max question length = 64, learning rate = 3e-5, num steps = 8,000
Figure 6: Illustrations of fine-tuning XLNet on CoQA v1.0 task
| CoQA v1.0 | Avg. (5-run) | Best |
|---|---|---|
| Exact Match | xx.xx ± x.xx | 81.8 |
| F1 Score | xx.xx ± x.xx | 89.4 |
Table 5: The test set performance of XLNet-large finetuned model on CoQA v1.0 task with setting: batch size = 48, max sequence length = 512, max question length = 128, learning rate = 3e-5, num steps = 6,000
Figure 7: Illustrations of fine-tuning XLNet on QuAC v0.2 task
| QuAC v0.2 | Avg. (5-run) | Best |
|---|---|---|
| F1 Score | xx.xx ± x.xx | 71.5 |
| HEQQ | xx.xx ± x.xx | 68.0 |
| HEQD | xx.xx ± x.xx | 11.1 |
Table 6: The test set performance of XLNet-large finetuned model on QuAC v0.2 task with setting: batch size = 48, max sequence length = 512, max question length = 128, learning rate = 2e-5, num steps = 8,000
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