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README.md

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# A Pytorch implementation of the BIST Parsers (for graph based parser only)
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The DyNet implementation by the author's of the original paper [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://www.transacl.org/ojs/index.php/tacl/article/viewFile/885/198) is [here](https://github.com/elikip/bist-parser)
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To be more accurate, this implementation is just a line-by-line translation from the DyNet implementation that can be found [here](https://github.com/elikip/bist-parser). The techniques behind the parser are described in the paper [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://www.transacl.org/ojs/index.php/tacl/article/viewFile/885/198).
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#### Required software
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python src/parser.py --predict --outdir [results directory] --test test.conll [--extrn extrn.vectors] --model [trained model file] --params [param file generate during training]
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The parser will store the resulting conll file in the out directory (`--outdir`).
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#### Difference from the DyNet implementation
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1. The multiple roots checking of the evaluation script is turned off (See [here](https://github.com/wddabc/bist-parser/blob/pytorch/bmstparser/src/utils/evaluation_script/conll17_ud_eval.py#L168-L172)) as it might generate trees with multiple roots. (See the discussion [here](https://github.com/elikip/bist-parser/issues/10))
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2. This version has not yet supports deep LSTM as the DyNet version does. It means `--lstmlayer` is no larger than 1.

bmstparser/src/parser.py

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parser.add_option("--predict", action="store_true", dest="predictFlag", default=False)
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parser.add_option("--bibi-lstm", action="store_true", dest="bibiFlag", default=False)
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parser.add_option("--disablecostaug", action="store_false", dest="costaugFlag", default=True)
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parser.add_option("--dynet-seed", type="int", dest="seed", default=0)
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parser.add_option("--dynet-mem", type="int", dest="mem", default=0)
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(options, args) = parser.parse_args()
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