ACE

The code is for our ACL-IJCNLP 2021 paper: Automated Concatenation of Embeddings for Structured Prediction

ACE is a framework for automatically searching a good embedding concatenation for structured prediction tasks and achieving state-of-the-art accuracy. The code is based on flair version 0.4.3 with a lot of modifications.

News

2022-11: AdaSeq: An all-in-one and easy-to-use library for developing sequence understanding models is released.
2022-03: Our newest state-of-the-art NER system KB-NER is released!
2021-07: New versions of document-level SOTA NER models are released, see Instructions for Reproducing Results for more details.

Comparison with State-of-the-Art

Task	Language	Dataset	ACE	Previous best
Named Entity Recognition	English	CoNLL 03 (document-level)	94.6 (F1)	94.3 (Yamada et al., 2020)
Named Entity Recognition	German	CoNLL 03 (document-level)	88.3 (F1)	86.4 (Yu et al., 2020)
Named Entity Recognition	German	CoNLL 03 (06 Revision) (document-level)	91.7 (F1)	90.3 (Yu et al., 2020)
Named Entity Recognition	Dutch	CoNLL 02 (document-level)	95.7 (F1)	93.7 (Yu et al., 2020)
Named Entity Recognition	Spanish	CoNLL 02 (document-level)	90.4 (F1)	90.3 (Yu et al., 2020)
Named Entity Recognition	English	CoNLL 03 (sentence-level)	93.6 (F1)	93.5 (Baevski et al., 2019)
Named Entity Recognition	German	CoNLL 03 (sentence-level)	87.0 (F1)	86.4 (Yu et al., 2020)
Named Entity Recognition	German	CoNLL 03 (06 Revision) (sentence-level)	90.5 (F1)	90.3 (Yu et al., 2020)
Named Entity Recognition	Dutch	CoNLL 02 (sentence-level)	94.6 (F1)	93.7 (Yu et al., 2020)
Named Entity Recognition	Spanish	CoNLL 02 (sentence-level)	89.1 (F1)	90.3 (Yu et al., 2020)
POS Tagging	English	Ritter's	93.4 (Acc)	90.1 (Nguyen et al., 2020)
POS Tagging	English	Ark	94.4 (Acc)	94.1 (Nguyen et al., 2020)
POS Tagging	English	TweeBank v2	95.8 (Acc)	95.2 (Nguyen et al., 2020)
Aspect Extraction	English	SemEval 2014 Laptop	87.4 (F1)	84.3 (Xu et al., 2019)
Aspect Extraction	English	SemEval 2014 Restaurant	92.0 (F1)	87.1 (Wei et al., 2020)
Aspect Extraction	English	SemEval 2015 Restaurant	80.3 (F1)	72.7 (Wei et al., 2020)
Aspect Extraction	English	SemEval 2016 Restaurant	81.3 (F1)	78.0 (Xu et al., 2019)
Dependency Parsing	English	PTB	95.7 (LAS)	95.3 (Wang et al., 2020)
Semantic Dependency Parsing	English	DM ID	95.6 (LF1)	94.4 (Fernández-González and Gómez-Rodríguez, 2020)
Semantic Dependency Parsing	English	DM OOD	92.6 (LF1)	91.0 (Fernández-González and Gómez-Rodríguez, 2020)
Semantic Dependency Parsing	English	PAS ID	95.8 (LF1)	95.1 (Fernández-González and Gómez-Rodríguez, 2020)
Semantic Dependency Parsing	English	PAS OOD	94.6 (LF1)	93.4 (Fernández-González and Gómez-Rodríguez, 2020)
Semantic Dependency Parsing	English	PSD ID	83.8 (LF1)	82.6 (Fernández-González and Gómez-Rodríguez, 2020)
Semantic Dependency Parsing	English	PSD OOD	83.4 (LF1)	82.0 (Fernández-González and Gómez-Rodríguez, 2020)

Guide

Requirements
Pretrained Models
- Instructions for Reproducing Results
Download Embeddings
Training
Parse files
Config File
TODO
Citing Us
Contact

Requirements

The project is based on PyTorch 1.1+ and Python 3.6+. To run our code, install:

pip install -r requirements.txt

The following requirements should be satisfied:

transformers: 3.0.0

Download Embeddings

In our code, most of the embeddings can be downloaded automatically (except ELMo for non-English languages). You can also download the embeddings manually. The embeddings we used in the paper can be downloaded here:

Name	Link
GloVe	nlp.stanford.edu/projects/glove
fastText	github.com/facebookresearch/fastText
ELMo	github.com/allenai/allennlp
ELMo (Other languages)	github.com/TalSchuster/CrossLingualContextualEmb
BERT	huggingface.co/bert-base-cased
M-BERT	huggingface.co/bert-base-multilingual-cased
BERT (Dutch)	huggingface.co/wietsedv/bert-base-dutch-cased
BERT (German)	huggingface.co/bert-base-german-dbmdz-cased
BERT (Spanish)	huggingface.co/dccuchile/bert-base-spanish-wwm-cased
BERT (Turkish)	huggingface.co/dbmdz/bert-base-turkish-cased
XLM-R	huggingface.co/xlm-roberta-large
XLM-R (CoNLL 02 Dutch)	huggingface.co/xlm-roberta-large-finetuned-conll02-dutch
XLM-R (CoNLL 02 Spanish)	huggingface.co/xlm-roberta-large-finetuned-conll02-spanish
XLM-R (CoNLL 03 English)	huggingface.co/xlm-roberta-large-finetuned-conll03-english
XLM-R (CoNLL 03 German)	huggingface.co/xlm-roberta-large-finetuned-conll03-german
XLNet	huggingface.co/xlnet-large-cased

After the embeddings are downloaded, you need to set the path of embeddings in the config file manually. For example in config/conll_03_english.yaml:

TransformerWordEmbeddings-1:
    model: your/embedding/path 
    layers: -1,-2,-3,-4
    pooling_operation: mean

Pretrained Models

We provide pretrained models for Named Entity Recognition (Sentence-/Document-Level) and Dependency Parsing (PTB) on OneDrive. You can find the corresponding config file in config/. For the zip files named with doc*.zip, you need to extract document-level embeddings at first. Please check (Optional) Extract Document Features for BERT Embeddings.

Download models
unzip the zip file
Put the directory in the resources/taggers/

To check the accuracy of the model, run:

CUDA_VISIBLE_DEVICES=0 python train.py --config config/conll_03_english.yaml --test

where --config $config_file is setting the configureation file. Here we take CoNLL 2003 English NER as an example. The $config_file is config/conll_03_english.yaml.

Instructions for Reproducing Results

Currently, we give an instruction for reproducing the results of our NER models is in named_entity_recognition.md. Other tasks can simply follow the guide of named_entity_recognition.md to reproduce the results.

Training

Training ACE Models

To train the model, run:

CUDA_VISIBLE_DEVICES=0 python train.py --config $config_file

Train on Your Own Dataset

To set the dataset manully, you can set the dataset in the $confile_file by:

Sequence Labeling:

targets: ner
ner:
  Corpus: ColumnCorpus-1
  ColumnCorpus-1: 
    data_folder: datasets/conll_03_new
    column_format:
      0: text
      1: pos
      2: chunk
      3: ner
    tag_to_bioes: ner
  tag_dictionary: resources/taggers/your_ner_tags.pkl

Parsing:

targets: dependency
dependency:
  Corpus: UniversalDependenciesCorpus-1
  UniversalDependenciesCorpus-1:
    data_folder: datasets/ptb
    add_root: True
  tag_dictionary: resources/taggers/your_parsing_tags.pkl

The tag_dictionary is a path to the tag dictionary for the task. If the path does not exist, the code will generate a tag dictionary at the path automatically. The dataset format is: Corpus: $CorpusClassName-$id, where $id is the name of datasets (anything you like). You can train multiple datasets jointly. For example:

Corpus: ColumnCorpus-1:ColumnCorpus-2:ColumnCorpus-3

ColumnCorpus-1:
  data_folder: ...
  column_format: ...
  tag_to_bioes: ...

ColumnCorpus-2:
  data_folder: ...
  column_format: ...
  tag_to_bioes: ...

ColumnCorpus-3:
  data_folder: ...
  column_format: ...
  tag_to_bioes: ...

Please refer to Config File for more details.

Set the Embeddings

You need to modifiy the embedding paths in the $config_file to change the embeddings for concatenation. For example, you need to add bert-large-cased in the config/conll_03_english.yaml

embeddings:
  TransformerWordEmbeddings-0:
    layers: '-1'
    pooling_operation: first
    model: xlm-roberta-large-finetuned-conll03-english

  TransformerWordEmbeddings-1:
    model: bert-base-cased
    layers: -1,-2,-3,-4
    pooling_operation: mean

  TransformerWordEmbeddings-2:
    model: bert-base-multilingual-cased
    layers: -1,-2,-3,-4
    pooling_operation: mean

  TransformerWordEmbeddings-3: # New embeddings
    model: bert-large-cased
    layers: -1,-2,-3,-4
    pooling_operation: mean
  ...

(Optional) Fine-tune Transformer-based Embeddings

To archieve state-of-the-art accuracy, one optional approach is fine-tuning the transformer-based embeddings over the task. We use fine-tuned embeddings in huggingface for NER tasks while embeddings in other tasks are fine-tuned by ourselves. Then take the embeddings as an embedding candidate of ACE. Taking fine-tuning BERT model on PTB parsing as an example, run:

CUDA_VISIBLE_DEVICES=0 python train.py --config config/en-bert-finetune-ptb.yaml

After the model is fine-tuned, you will find a tuned BERT model at

ls resources/taggers/en-bert_10epoch_0.5inter_2000batch_0.00005lr_20lrrate_ptb_monolingual_nocrf_fast_warmup_freezing_beta_weightdecay_finetune_saving_nodev_dependency16/bert-base-cased

Then, replace bert-base-cased with resources/taggers/en-bert_10epoch_0.5inter_2000batch_0.00005lr_20lrrate_ptb_monolingual_nocrf_fast_warmup_freezing_beta_weightdecay_finetune_saving_nodev_dependency16/bert-base-cased in the $config_file of the ACE model (for example, config/ptb_parsing_model.yaml).

The config config/en-bert-finetune-ptb.yaml can be applied to fine-tuning other embeddings in parsing tasks. Here is an example config for fine-tuning NER (sequence labeling tasks): config/en-bert-finetune-ner.yaml

(Optional) Extract Document Features for BERT Embeddings

To archieve state-of-the-art accuracy of NER, one optional approach is extracting the document-level features from the BERT embeddings (for RoBERTa, XLM-R and XLNET, we feed the model with the whole document, if you are interested in this part, see embeddings.py). Then take the features as an embedding candidate of ACE. We follow the embedding extraction approach of Yu et al., 2020. We use the sentences with a single word -DOCSTART- to split the documents. For CoNLL 2002 Spanish, there is not -DOCSTART- sentences. Therefore, we add a -DOCSTART- sentence for every 25 sentences. For CoNLL 2002 Dutch, the -DOCSTART- is in the first sentence of the document, please split the -DOCSTART- token into a single sentence. For example:

-DOCSTART- -DOCSTART- O

De Art O
tekst N O
van Prep O
het Art O
arrest N O
is V O
nog Adv O
niet Adv O
schriftelijk Adj O
beschikbaar Adj O
maar Conj O
het Art O
bericht N O
werd V O
alvast Adv O
bekendgemaakt V O
door Prep O
een Art O
communicatiebureau N O
dat Conj O
Floralux N B-ORG
inhuurde V O
. Punc O

...

Taking English BERT model on CoNLL English NER as an example, run:

CUDA_VISIBLE_DEVICES=0 python extract_features.py --config config/en-bert-extract.yaml --batch_size 32

Parse files

If you want to parse a certain file, add train in the file name and put the file in a certain $dir (for example, parse_file_dir/train.your_file_name). Run:

CUDA_VISIBLE_DEVICES=0 python train.py --config $config_file --parse --target_dir $dir --keep_order

The format of the file should be column_format={0: 'text', 1:'ner'} for sequence labeling or you can modifiy line 337 in train.py. The parsed results will be in outputs/. Note that you may need to preprocess your file with the dummy tags for prediction, please check this issue for more details.

Config File

The config files are based on yaml format.

targets: The target task
- ner: named entity recognition
- upos: part-of-speech tagging
- chunk: chunking
- ast: abstract extraction
- dependency: dependency parsing
- enhancedud: semantic dependency parsing/enhanced universal dependency parsing
ner: An example for the targets. If targets: ner, then the code will read the values with the key of ner.
- Corpus: The training corpora for the model, use : to split different corpora.
- tag_dictionary: A path to the tag dictionary for the task. If the path does not exist, the code will generate a tag dictionary at the path automatically.
target_dir: Save directory.
model_name: The trained models will be save in $target_dir/$model_name.
model: The model to train, depending on the task.
- FastSequenceTagger: Sequence labeling model. The values are the parameters.
- SemanticDependencyParser: Syntactic/semantic dependency parsing model. The values are the parameters.
embeddings: The embeddings for the model, each key is the class name of the embedding and the values of the key are the parameters, see flair/embeddings.py for more details. For each embedding, use $classname-$id to represent the class. For example, if you want to use BERT and M-BERT for a single model, you can name: TransformerWordEmbeddings-0, TransformerWordEmbeddings-1.
trainer: The trainer class.
- ModelFinetuner: The trainer for fine-tuning embeddings or simply train a task model without ACE.
- ReinforcementTrainer: The trainer for training ACE.
train: the parameters for the train function in trainer (for example, ReinforcementTrainer.train()).

TODO

Knowledge Distillation with ACE: Wang et al., 2020

Citing Us

If you feel the code helpful, please cite:

@inproceedings{wang2020automated,
    title = "{{Automated Concatenation of Embeddings for Structured Prediction}}",
    author = "Wang, Xinyu and Jiang, Yong and Bach, Nguyen and Wang, Tao and Huang, Zhongqiang and Huang, Fei and Tu, Kewei",
    booktitle = "{the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (\textbf{ACL-IJCNLP 2021})}",
    month = aug,
    year = "2021",
    publisher = "Association for Computational Linguistics",
    
}

Contact

Feel free to email your questions or comments to issues or to Xinyu Wang.

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README.md

README.md

ACE

News

Comparison with State-of-the-Art

Guide

Requirements

Download Embeddings

Pretrained Models

Instructions for Reproducing Results

Training

Training ACE Models

Train on Your Own Dataset

Set the Embeddings

(Optional) Fine-tune Transformer-based Embeddings

(Optional) Extract Document Features for BERT Embeddings

Parse files

Config File

TODO

Citing Us

Contact

Files

README.md

Latest commit

History

README.md

File metadata and controls

ACE

News

Comparison with State-of-the-Art

Guide

Requirements

Download Embeddings

Pretrained Models

Instructions for Reproducing Results

Training

Training ACE Models

Train on Your Own Dataset

Set the Embeddings

(Optional) Fine-tune Transformer-based Embeddings

(Optional) Extract Document Features for BERT Embeddings

Parse files

Config File

TODO

Citing Us

Contact