57 строки
2.3 KiB
Markdown
57 строки
2.3 KiB
Markdown
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## Introduction
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This paper introduces [UniSAr](https://arxiv.org/pdf/2203.07781.pdf), which extends existing autoregressive language models to incorporate three non-invasive extensions to make them structure-aware:
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(1) adding structure mark to encode database schema, conversation context, and their relationships;
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(2) constrained decoding to decode well structured SQL for a given database schema; and
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(3) SQL completion to complete potential missing JOIN relationships in SQL based on database schema.
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[//]: # (On seven well-known text-to-SQL datasets covering multi-domain, multi-table and multi-turn, UniSAr demonstrates highly comparable or better performance to the most advanced specifically-designed text-to-SQL models.)
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## Dataset and Model
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[Spider](https://github.com/taoyds/spider) -> `./data/spider`
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[Fine-tuned BART model](https://huggingface.co/dreamerdeo/mark-bart/tree/main) -> `./models/spider_sl`
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(Please download this model by `git-lfs` to avoid the [issue](https://github.com/DreamerDeo/UniSAr_text2sql/issues/1).)
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```angular2html
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sudo apt-get install git-lfs
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git lfs install
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git clone https://huggingface.co/dreamerdeo/mark-bart
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```
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## Main dependencies
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* Python version >= 3.6
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* PyTorch version >= 1.5.0
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* `pip install -r requirements.txt`
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* fairseq is going though changing without backward compatibility. Install `fairseq` from source and use [this](https://github.com/nicola-decao/fairseq/tree/fixing_prefix_allowed_tokens_fn) commit for reproducibilty. See [here](https://github.com/pytorch/fairseq/pull/3276) for the current PR that should fix `fairseq/master`.
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## Evaluation Pipeline
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Step 1: Preprocess via adding schema-linking and value-linking tag.
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`python step1_schema_linking.py`
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Step 2: Building the input and output for BART.
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`python step2_serialization.py`
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Step 3: Evaluation Script with/without constrained decoding.
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`python step3_evaluate.py --constrain`
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## Results
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Prediction: `69.34`
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Prediction with Constrain Decoding: `70.02`
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## Interactive
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`python interactive.py --logdir ./models/spider-sl --db_id student_1 --db-path ./data/spider/database --schema-path ./data/spider/tables.json`
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## Reference Code
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`https://github.com/ryanzhumich/editsql`
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`https://github.com/benbogin/spider-schema-gnn-global`
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`https://github.com/ElementAI/duorat`
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`https://github.com/facebookresearch/GENRE`
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