Align measures the correspondence of information between two pieces of text, which is introduced in
Text Alignment Is An Efficient Unified Model for Massive NLP Tasks
Yuheng Zha*, Yichi Yang*, Ruichen Li and Zhiting Hu
NeurIPS 2023

For a given text pair $(x_1, x_2)$, text information alignment is to decide if all information in $x_2$ is supported by information in $x_1$. For example, given a text $x_1$: I have been in Kentucky, Kirby, text I have been in the US is aligned with $x_1$. However, either text I have been in Europe or text Kentucky has the best fried chicken is not aligned with $x_1$.

Text alignment is applicable to a wide range of downstream tasks, e.g., Natural Language Inference, Paraphrase Detection, Fact Verification, Semantic Textual Similarity, Question Answering, Coreference Resolution and Information Retrieval.

We recommend to install Align in a conda environment.

First clone this repo:

git clone https://github.com/yuh-zha/Align.git
cd Align

Create a virtual conda environment:

conda create -n Align python=3.9
conda activate Align
pip install -e .

Install the required spaCy model

python -m spacy download en_core_web_sm

We provide two versions of Align checkpoints: Align-base and Align-large. The -base model is based on RoBERTa-base and has 125M parameters. The -large model is based on RoBERTa-large and has 355M parameters.

Align-base: https://huggingface.co/yzha/Align/resolve/main/Align-base.ckpt

Align-large: https://huggingface.co/yzha/Align/resolve/main/Align-large.ckpt

To get the alignment score of the text pairs (text_a and text_b), use the scorer function of Align:

from align import Align

text_a = ["Your text here"]
text_b = ["Your text here"]

scorer = Align(model="roberta-large", batch_size=32, device="cuda", ckpt_path="path/to/ckpt")
score = scorer(contexts=text_a, claims=text_b)

model: The backbone model of Align. It can be roberta-base or roberta-large
batch_size: The batch size of inference
ckpt_path: The path to the checkpoint

If you find this work helpful, please consider cite:

@inproceedings{
zha2023text,
title={Text Alignment Is An Efficient Unified Model for Massive {NLP} Tasks},
author={Yuheng Zha and Yichi Yang and Ruichen Li and Zhiting Hu},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=xkkBFePoFn}
}