Knowledge Adapter Set for Multilingual Language Model Enhancement

Source code for "Adapters for Enhanced Modeling of Multilingual Knowledge and Text"

We provide pretrained knowledge adapter sets for mBERT, XLMR-base and XLMR-large. Users could find them via AdapterHub and HuggingFace.

Below is a figure to describe functions of our adapters in the knowledge adapter set.

Each adapter has a unique function, users could select appropriate one for the usage, or select a fusion mechanism to use them in a compositional way.

• a) Adapter-EP can enhance the phrase-level cross-lingual entity alignment in language models, which is suitable for knowledge graph tasks;
• b) Adapter-TP can enhance the phrase-level factual triple knowledge in language models, which is suitable for knowledge graph tasks;
• c) Adapter-ES can enhance the sentence-level cross-lingual entity alignment in language models, which is suitable for language modeling tasks;
• d) Adapter-TS can enhance the sentence-level factual triple knowledge in language models, which is suitable for language modeling tasks;

Usage of Our Pretrained Knowledge Adapter Set

We pretrained our 4 adapters on Wikidata (Adapter-EP/TP) and Wikipedia (Adapter-ES) across 84 languages, and T-REx (Adapter-TS) for English.

Using our provided adapters are fairly easy, only 2 steps below.

1. Requirements:

The AdapterHub should be installed before usage:

$ pip install adapter-transformers

2. Download and Load Adapters:

Users can download them from HuggingFace for different pretrained language models.

If you use pretrained language models via HuggingFace, simply add one line after you load the pretrained language model. For example, for the Adapter-EP, it can be

model.load_adapter(adapter_path, "ep"))

If you want to use the adapter set with a fusion mechanism, for example using AdapterFusion, you can:

import transformers.adapters.composition as ac
model.load_adapter(adapterep_path, "ep"))
model.load_adapter(adapteres_path, "es"))
model.load_adapter(adaptertp_path, "tp"))
model.load_adapter(adapterts_path, "ts"))
model.add_adapter_fusion(["ep", "tp", "es", "ts"])
model.active_adapters = ac.Fuse("ep", "tp", "es", "ts")

After these adapters are loaded, the language model becomes an enhanced one and can be used as usual.

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Train your own Knowledge Adapter Set

We provide the source code for adapter training in ./src. We also provide the processed datasets (Wikidata, Wikipedia, and T-REx), users can easily get them here (kicorpus.tar.gz). Besides, in that folder users can find our extended multilingual knowledge graph completion dataset (entity alignment and knowledge graph completion) across 22 languages.

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Reproduce the results in the paper

Please enter ./exp folder for more details.

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Cite

If you use our adapters or the code, welcome to cite our paper:

@article{hou2022adapters,
  title={Adapters for Enhanced Modeling of Multilingual Knowledge and Text},
  author={Hou, Yifan and Jiao, Wenxiang and Liu, Meizhen and Allen, Carl and Tu, Zhaopeng and Sachan, Mrinmaya},
  journal={arXiv preprint arXiv:2210.13617},
  year={2022}
}

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Contact

Feel free to open an issue or send me ([email protected]) an email if you have any questions!