ZGen

Introduction

ZGen is a linearization system that constructs natural language sentences from bags of words, given optional input syntactic constraints. Depending on the amount of input constraints, ZGen can perform free ordering, partial tree linearization and full tree linearization.

Compiling

Prerequisites

ZGen is developed with boost and compiled by cmake, along with some C++ 0x features. To compile ZGen, the following software packages is required:

• gcc 4.5 (or above): for C++ 0x lambdas
• boost 1.57 (or above)
• cmake 2.8.10

Make

The compiling commands is

Run the following commands to compile on unix.

./configure
make

After successfully compiling, you will get 2 executable files under the bin folder. They are:

• ./bin/ZGen_SR: The transition-based linearizer.
• ./bin/unittest: The unittest suites.

Usage

Input Data

ZGen accepts different amount of syntactic constraints. These constraints includes the Postagging and (partial) Dependency parse structure. Constraints are input in a 4 columns data format, which means forms, postag, head, deprel. If certain slot (postag, head, deprel) is filled in the input data, it will be used to constrain the decoding process. The following example give an illustration to the input without any syntactic constrain:

__Ms._Haag__ _ -1 _
plays _ -1 _
__Elianti__ _ -1 _
. _ -1 _

And this example is an illustration for the input with partial postag and dependency constraint.

__Ms._Haag__ _ 1 N
plays _ -1 N
__Elianti__ NP -1 _
. . 1 N

which means, the token __Ms._Haag__ should be the modifier of __Elianti__, and __Elianti__ should be tagged as NP in the final output.

NOTE: if the input contains syntactic constraints, you need to specify the constraint type by the --type command line options.

Reference Data (for training.)

If you want to train your own linearizing model with zgen, you need to prepare your reference data. The reference data should be a well-formed 4 columns dependency tree, as shown below.

__Ms._Haag__ NP 1 N
plays VBZ -1 N
__Elianti__ NP 1 N
. . 1 N

Noun Phrases

ZGen accepts the prerecognized noun phrases. A special prefix __ and suffix __ should be added to the words in the phrase.

Postag Dictionary

ZGen accepts another form of constraint which is postag dictionary. The dictionary is used to indicate the possible tags for a certain word. Postag dictionary is in the form of [word] [postag]

Does NNP
Does VBZ
hanging VBG       

Postag dictionary is an optional resource to constrain the postag for each token, but it's highly recommanded because it will reduce the search space by magnitude, thus speed up the decoding.

Model

Before performing sentence linearization, you need to get the corresponding model. We have provide several pretrained model on English (PennTreebank). You can download it from here.

Running ZGen

After preparing the data and model file (we suppose you put the model file in /path/to/your/model), you can run ZGen in following commands:

./bin/ZGen_SR test \
        --type  none \
        --model /path/to/your/model \
        --input /path/to/your/input_file \
        --posdict /path/to/your/postag_dict

The option --type is used to specify the constraint type during the decoding. If your input constraint don't contains dependency relations, please use none; If your input contains part of but not full dependency relations, you need to use partial; If your input constrains contains the full dependency relations, please use full.

For details decription of the command line options, please run:

./bin/ZGen_SR test --help

Sample scripts to train and test ZGen are available at /run/train-unlabeled-nopos-nodep.sh

Combine N-gram model

If you obtain a large N-gram language model, which can be trained by KenLM tool, you can run ZGen in following commands:

./bin/ZGen_SR test \
	--type none \
	--model /path/to/your/model \
	--ngram /path/to/your/ngram/model \
	--input /path/to/your/input_file \
	--posdict /path/to/your/postag_dict

Author

• Yue Zhang (yue_zhang@sutd.edu.sg) developed the prototype for ZGen best-first and transition-based linearizer.
• Linfeng Song developed the joint morphlogy and generation system on ZGen:BestFrist linearizer and developed the initial version of ZGen:BestFrist linearizer.
• Yijia Liu (oneplus.lau@gmail.com) refine the transition-based linearizer and formalized the codes.
• Jiangming Liu (jmliunlp@gmail.com) combine the N-gram model into the system.
• Ratish Puduppully(ratish.surendran@research.iiit.ac.in) added lookahead features and implemented Deep Input Linearization
• Current maintainers for ZGen are Jiangming Liu and Ratish Puduppully.

Publication

• Ratish Puduppully, Yue Zhang and Manish Shrivastava. 2017. Transition-Based Deep Input Linearization. In Proceedings of EACL 2017 (To Appear). valencia, Spain, April.
• Ratish Puduppully, Yue Zhang and Manish Shrivastava. 2016. Transition-Based Syntactic Linearization with Lookahead Features. In Proceedings of NAACL 2016. San Diego, USA, June.
• Jiangmign Liu. 2015. An Empirical Comparision between N-gram and Syntax Language Model for Word Ordering. In Proceedings of EMNLP2015. Lisbon, Portugal, September.
• Yijia Liu, Yue Zhang, Wanxiang Che, and Ting Liu. 2015. Transition-Based Syntactic Linearization. In Proceedings of NAACL2015. Denver, Colorado, USA, May.
• Linfeng Song, Yue Zhang, Kai Song and Qun Liu. 2014. Joint Morphological Generation and Syntactic Linearization. In Proceedings of AAAI 2014. Quebec City, Canada, July.

Licences

Please refer the LICENSES file.