wehub-resource-sync
111 lines
2.5 KiB
Markdown
111 lines
2.5 KiB
Markdown
# Input-guided Aggressive Decoding
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Codes (originally from https://github.com/AutoTemp/Shallow-Aggressive-Decoding) for Input-guided Aggressive Decoding (IAD) that is originally proposed in the paper "Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding" (ACL-IJCNLP 2021)
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## Results
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<table>
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<caption> The performance and online inference efficiency evaluation of baseline and our approach in CoNLL-14. </caption>
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<tr>
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<th> Model </th>
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<th> P </th>
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<th> R </th>
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<th> F<sub>0.5</sub> </th>
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<th> Speedup </th>
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</tr>
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<tr>
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<th> Transformer-big (beam=5) </th>
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<th> 73.0 </th>
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<th> 38.1 </th>
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<th> 61.6 </th>
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<th> 1.0x </th>
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</tr>
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<tr>
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<th> Our approach (9+3) </th>
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<th> 73.3 </th>
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<th> 41.3 </th>
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<th> 63.5 </th>
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<th> 10.3x </th>
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</tr>
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<tr>
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<th> Our approach (12+2 BART-Init) </th>
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<th> 71.0 </th>
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<th> 52.8 </th>
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<th> 66.4 </th>
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<th> 9.6x </th>
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</tr>
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</table>
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<table>
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<caption> For reference, the beam=1 and beam=5 results of the state-of-the-art 12+2 (BART-Init) are: </caption>
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<thead>
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<tr>
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<th>12+2 BART-Init</th>
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<th colspan="3">CoNLL-14</th>
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<th colspan="3">BEA-19</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>Beam</th>
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<th>P</th>
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<th>R</th>
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<th>F<sub>0.5</sub></th>
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<th>P</th>
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<th>R</th>
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<th>F<sub>0.5</sub></th>
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</tr>
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<tr>
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<th>1</td>
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<th>71.0</th>
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<th>52.8</th>
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<th>66.4</th>
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<th>74.7</th>
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<th>66.4</th>
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<th>72.9</th>
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</tr>
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<tr>
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<th>5</th>
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<th>71.4</td>
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<th>52.8</td>
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<th>66.7</td>
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<th>75.8</td>
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<th>66.3</td>
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<th>73.7</td>
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</tr>
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</tbody>
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</table>
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The above models are all single models without ensemble.
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## Installation
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```
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conda create -n IAD python=3.6
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conda activate IAD
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conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=10.2 -c pytorch
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cd fairseq
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pip install --editable .
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```
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## Usage
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This section explains how to decode in different ways.
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```
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PTPATH=/to/path/checkpoint*.pt # path to model file
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BINDIR=/to/path/bin_data # directory containing src and tgt dictionaries
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INPPATH=/to/path/conll*.bpe.txt # path to eval file
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OUTPATH=/to/path/conll*.out.txt # path to output file
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BATCH=xxx
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BEAM=xxx
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```
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## Directly use fairseq's interactive.py to decode:
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```
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bash interactive.sh $PTPATH $BATCH $BEAM $INPPATH $BINDIR $OUTPATH
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```
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## use Input-guided Aggressive Decoding:
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```
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python inference.py --checkpoint-path $PTPATH --bin-data $BINDIR --input-path $INPPATH --output-path $OUTPATH --aggressive
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```
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