/neural-argument-generation

Project page for "Neural Argument Generation Augmented with Externally Retrieved Evidence"

Primary LanguagePythonOtherNOASSERTION

About

This repository holds the code for Neural Argument Generation project developed at Northeastern NLP. For details about the framework please read our ACL 2018 paper:

Usage

Requirement

  • python 3.5
  • tensorflow 1.4.0
  • numpy 1.14

Data

Update: This dataset has been updated on 2018/08/23. This change solves some tokenization errors exist in previous version.

Please download the dataset from here.

The dataset consists of the following 5 parts:

  1. cmv_processed: filtered OP posts and root replies used to create the core dataset

  2. wikipedia_retrieval: wikipedia article titles retrieved as evidence source for OP and root replies

  3. reranked_evidence: selected evidence sentences and extracted keyphrases for OP and root replies

  4. trainable: directly trainable dataset

  5. test: test set we used for evaluation

(Detailed readme file can be found here.)

File structure

Please download the corresponding data and put them under dat/ folder. If the folder does not exist please create by hand.

mkdir dat/log
mkdir -p dat/trainable/bin

neural-argument-generation/
 ├── src/
 │   ├── arggen.py
 │   ├── attention.py
 │   ├── base_model.py
 │   ├── beam_search.py
 │   ├── data_loader.py
 │   ├── decode.py
 │   ├── sep_dec_model.py
 │   ├── shd_dec_model.py
 │   ├── utils.py
 │   └── vanilla_model.py
 │
 ├── scripts/
 │   ├──  preprocess.py
 │   └──  evaluation.py (coming soon)
 │
 └── dat/
     ├── vocab.src
     ├── vocab.tgt
     ├── trainable/
     │    ├── train_core_sample3.src
     │    ├── train_core_sample3_arg.tgt
     │    ├── train_core_sample3_kp.tgt
     │    ├── valid_core_sample3.src
     │    ├── valid_core_sample3_arg.tgt
     │    ├── valid_core_sample3_kp.tgt
     │    └── bin/
     └── log/

Preprocessing

This step binarizes the plain text data. Please make sure the plain text data files are in order.

python3 scripts/preprocess.py

Training and concurrent validation

Train the model by assigning --mode=train. While the model is training, start another thread by assigning --mode=eval for concurrent validation. The summaries on loss will be logged into the same exp folder. These results can be visualized by tensorboard.

python3 src/arggen.py [--mode={train,eval}] [--model={vanilla,seq_dec,shd_dec}] \
                      [--data_path=PATH_TO_BIN_DATA] \
                      [--model_path=PATH_TO_STORE_MODEL] \
                      [--exp_name=EXP_NAME] \
                      [--batch_size=BS] \
                      [--src_vocab_path=PATH_TO_SRC_VOCAB] \
                      [--tgt_vocab_path=PATH_TO_TGT_VOCAB] \

Inference

After the model is trained, decode on binarized data using the following command. Note that the default for --ckpt_id is -1, which indicates the newest (not necessarily the best) checkpoint.

python3 src/arggen.py [--mode=decode] [--model={vanilla,seq_dec,shd_dec}] \
                      [--data_path=PATH_TO_BIN_DATA] \
                      [--model_path=PATH_TO_STORE_MODEL] \
                      [--exp_name=EXP_NAME] \
                      [--ckpt_id=CKPT_ID] \
                      [--beam_size=BS] \
                      [--src_vocab_path=PATH_TO_SRC_VOCAB] \
                      [--tgt_vocab_path=PATH_TO_TGT_VOCAB] \

Evaluation

[coming soon]

Support or Contact

Please contact Xinyu Hua (hua.x@husky.neu.edu) for any questions about this repository.

Acknowledgement

Part of this codebase is based on Pointer-generator. The dual attention implementation is adapted from Lisa Fan.