A neural parser for QA-SRL.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
configs
data/wiktionary
demo
nrl
scripts
.gitignore
README.md

README.md

nrl-qasrl

This repository contains the code for the paper: "Large-Scale QA-SRL Parsing" by Nicholas FitzGerald, Julian Michael, Luheng He and Luke Zettlemoyer.

Data can be found at: http://qasrl.org

Prerequisites

This library requires AllenNLP.

In order to use the models below, you must also:

  • Download the pretrained GloVe embeddings and copy the file to data/glove/glove.6B.100d.txt.gz in the repository directory.
  • Download the pretrained ElMO word representations and copy the files to data/elmo/elmo_2x4096_512_2048cnn_2xhighway_options.json and data/elmo/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5

Pretrained model

Clone this repository and make sure you have installed prerequisites as listed above.

Download and unpack the pretrained model in the data folder from the command line :

cd scripts
./download_pretrained.sh

Alternatively, you can download the compressed archive manually from here and uncompress it in the data/qasrl_parser_elmo folder.

This model uses 8 LSTM-layers for both span detection and question generation, and a 4-layer LSTM generator. It also uses ELMo deep contextualized word representations. This model achieves 85.0% F1 span detection (with t=0.5) and 47.6% question prediction accuracy on the dense dev set (compared to 81.3 and 47.2 for the model reported in our paper).

To run predictions with this model on new text, prepare a JSON-lines document with one line for each sentence with the following format:

{"sentence": "John went to the store."}
{"sentence": "The man ate the burrito and threw the trash in the garbage"}

From the root of the repository, run prediction with the following command:

python -m allennlp.run predict ./data/qasrl_parser_elmo {$INPUT_FILE} --include-package nrl --predictor qasrl_parser --output-file {$OUTPUT_FILE}

Which will produce the following output:

{"words": ["John", "went", "to", "the", "store"], "verbs": [{"verb": "went", "qa_pairs": [{"question": "Who went somewhere?", "spans": ["John"]}, {"question": "Where did someone go?", "spans": ["to the store"]}], "index": 1}]}
{"words": ["The", "man", "ate", "the", "burrito", "and", "threw", "the", "trash", "in", "the", "garbage", "bin", "."], "verbs": [{"verb": "ate", "qa_pairs": [{"question": "Who ate something?", "spans": ["The man"]}, {"question": "What did someone eat?", "spans": ["the burrito"]}], "index": 2}, {"verb": "threw", "qa_pairs": [{"question": "Who threw something?", "spans": ["The man"]}, {"question": "What did someone throw?", "spans": ["the trash"]}, {"question": "Where did someone throw something?", "spans": ["in the garbage bin", "the garbage bin"]}], "index": 6}]}

Training Models

Training the QA-SRL parser consists of three stages. The Span Detection and Question Prediction models are trained seperately. Then, a script is run in order to combine these two models into one.

Training Span Detector

To train the Span Detection models, run:

python -m allennlp.run train {$CONFIG_FILE} --include-package nrl -s {$SAVE_DIRECTORY}

Two config files are included with this repository. These must be modified in order to point the *_data_path fields to your data directory.

  1. configs/train_span.json - which reproduces the Span-based model from our paper.
  2. configs/train_span_elmo.json - which is the same model but includes ELMo word representations.

Training Question Detector

Training the Question Detector uses the same command as above but with these config files:

  1. configs/train_question.json - which reproduces the Sequence model from our paper.
  2. configs/train_question_elmo - which, like above, includes the ELMo word representation.

Combining models

To combine these two trained models into one model which can then be run for prediction, run the following script:

python scripts/combine_models.py --span {$PATH_TO_SPAN_MODEL_DIRECTORY} --ques {$PATH_TO_QUESTION_MODEL_DIRECTORY} --out {$OUTPUT_TAR_GZ_FILE}

Evaluating on the dense dev set

To evaluate on the densely labeled Dev set described in our paper, run the command:

env CUDA_VISIBLE_DEVICES=1 python -m allennlp.run evaluate {$MODEL_DIRECTORY}/model.tar.gz --evaluation-data-file {$DATA_DIRECTORY}/dense/dev.jsonl.gz --cuda-device 0 --include-package nrl --overrides '{"dataset_reader": {"question_sources": "turk", "min_answers": 6, "min_valid_answers": 5}}'