Neural AMR

Torch implementation of sequence-to-sequence models for AMR parsing and generation based on the Harvard NLP framework. We provide the code for pre-processing, anonymizing, de-anonymizing, training and predicting from and to AMR. We are also including pre-trained models on 20M sentences from Gigaword and fine-tuned on the AMR LDC2015E86: DEFT Phase 2 AMR Annotation R1 Corpus. You can find all the details in the following paper:

• Neural AMR: Sequence-to-Sequence Models for Parsing and Generation. (Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, Luke Zettlemoyer. ACL 2017)

Requirements

The pre-trained models only run on GPUs, so you will need to have the following installed:

• Latest NVIDIA driver
• CUDA 8 Toolkit
• cuDNN (The NVIDIA CUDA Deep Neural Network library)
• Torch

Installation

• Install the following packages for Torch using luarocks:

nn nngraph cutorch cunn cudnn

• Install the Deepmind version of torch-hdf5 from here.

(Only for training models)

• Install cudnn.torch from here.
• Install the following packages for Python 2.7:

pip install numpy h5py

(Only for downloading the pretrained models)

• Download and unzip the models from here

• Export the cuDNN library path (you can add it to your .bashrc or .profile):

export CUDNN_PATH="path_to_cudnn/lib64/libcudnn.so"

• Or instead of the previous step you can copy the cuDNN library files into /usr/local/cuda/lib64/ or to the corresponding folders in the CUDA directory.

Quickstart (AMR Generation)

You can generate text from AMR graphs using our pre-trained model on 20M sentences from Gigaword, in two different ways:

• By running an interactive tool that reads input from stdin:

./generate_amr_single.sh [stripped|full|anonymized]

• By running the prediction on a single file, which contains an AMR graph per line:

./generate_amr.sh input_file [stripped|full|anonymized]

You can optionally provide an argument that tells the system to accept either full AMR as described in the annotation guidelines, or a stripped version, which removes variables, senses, parentheses from leaves, and assumes a simpler markup for Named Entities, date mentions, and numbers. You can also provide the input in anonymized format, i.e., similar to stripped but with Named Entities, date mentions, and numbers anonymized.

An example using the full format:

(h / hold-04 :ARG0 (p2 / person :ARG0-of (h2 / have-org-role-91 :ARG1 (c2 / country :name (n3 / name :op1 “United" op2: “States”)) :ARG2 (o / official)))  :ARG1 (m / meet-03 :ARG0 (p / person  :ARG1-of (e / expert-01) :ARG2-of (g / group-01))) :time (d2 / date-entity :year 2002 :month 1) :location (c / city  :name (n / name :op1 “New" :op2 “York”)))

The same example using the stripped format:

hold :ARG0 ( person :ARG0-of ( have-org-role :ARG1 (country :name "United States") :ARG2 official)) :ARG1 (meet :ARG0 (person  :ARG1-of expert :ARG2-of  group)) :time (date-entity :year 2002 :month 1) :location (city :name "New York" )

The same example using the anonymized format:

hold :ARG0 ( person :ARG0-of ( have-org-role :ARG1 location_name_0 :ARG2 official ) ) :ARG1 ( meet :ARG0 ( person :ARG1-of expert :ARG2-of group ) ) :time ( date-entity year_date-entity_0 month_date-entity_0 ) :location location_name_1

For full details and more examples, see here.

Details

Generation Options (generate_amr.sh, generate_amr_single.sh)

• interactive_mode [0,1]: Set 0 for generating from a file, or 1 to generate from stdin.
• model [str]: The path to the trained model.
• input_type [stripped|full]: Set full for standard AMR graph input, or stripped which expects AMR graphs with no variables, senses, parentheses from leaves, and assumes a simpler markup for Named Entities (for more details and examples, see here).
• src_file [str]: The path to the input file that contains AMR graphs, one per line.
• gpuid [int]: The GPU id number.
• src_dict, targ_dict [str]: Path to source and target dictionaries. These are usually generated during preprocessing of the corpus. ==Note==: src_dict and targ_dict paths need to be reversed when generating text or parsing to AMR.
• beam [int]: The beam size of the decoder (default is 5).
• replace_unk [0,1]: Replace unknown words with either the input token that has the highest attention weight, or the word that maps to the input token as provided in srctarg_dict.
• srctarg_dict [str]: Path to source-target dictionary to replace unknown tokens. Each line should be a source token and its corresponding target token, separated by ||| (see resources/training-amr-nl-alignments.txt).
• max_sent_l [str]: Maximum sentence length (default is 507, i.e., the longest input AMR graph in number of tokens from the dev set). If any of the sequences in src_file are longer than this it will error out.