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