/Inference-Masked-Loss

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Inference-Masked Loss for Deep Structured Output Learning

Quan Guo, Hossein Rajaby Faghihi, Yue Zhang, Andrzej Uszok, Parisa Kordjamshidi

This repository contains the code for the experiments of our research paper "Inference-Masked Loss for Deep Structured Output Learning" accepted by IJCAI 2020.

Abstract

Structured learning algorithms usually involve an inference phase that selects the best global output variables assignments based on the local scores of all possible assignments. We extend deep neural networks with structured learning to combine the power of learning representations and leveraging the use of domain knowledge in the form of output constraints during training. Introducing a non-differentiable inference module to gradient-based training is a critical challenge. Compared to using conventional loss functions that penalize every local error independently, we propose an inference-masked loss that takes into account the effect of inference and does not penalize the local errors that can be corrected by the inference. We empirically show the inference-masked loss combined with the negative log-likelihood loss improves the performance on different tasks, namely entity relation recognition on CoNLL04 and ACE2005 corpora, and spatial role labeling on CLEF 2017 mSpRL dataset. We show the proposed approach helps to achieve better generalizability, particularly in the low-data regime.

Cite

If you find this work useful, please also consider cite our paper:

Quan Guo, Hossein Rajaby Faghihi, Yue Zhang, Andrzej Uszok, Parisa Kordjamshidi, Inference-Masked Loss for Deep Structured Output Learning, in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI 2020), 2020

@inproceedings{guo2020inference,
   Author = {Quan Guo and Hossein Rajaby Faghihi and Yue Zhang and Andrzej Uszok and Parisa Kordjamshidi},
   Title = {Inference-Masked Loss for Deep Structured Output Learning},
   Booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI 2020)},
   Year = {2020}
}

Code

Requirement

Dependencies

Our code is tested on Ubuntu 18.04 and with specific version of software mentioned above. It should work with other versions of Ubuntu, however we did not tested.

Please install above listed dependencies and install other dependencies by

python -m pip install -r requirements.txt

Setup

Add project root to python module search path, for example, by adding it to environment variable PYTHONPATH.

cd path/to/project/
export PYTHONPATH=$(pwd):$PYTHONPATH

Experiments

We evaluate the proposed approach with several structured learning tasks: Two different entity relation extraction (ER) tasks and spatial role labeling (SpRL) task. We investigate the entity and relation recognition corpora (CoNLL04) and ACE 2005 Corpus (ACE2005) for ER task. The two datasets contain different types of entities and relationships. For SpRL task, CLEF 2017 mSpRL dataset (SpRL2017) is investigated.

CoNLL04

CoNLL04 is a publicly available corpus for ER. The task is to recognize four types of entities among tokens in a sentence and classify five types of relations between entities. This corpus contains 5,516 sentences, 11,182 entities, and 2,048 relations. The applied hard constraints are between the types of relations and the types of their two entities.

ACE05 / ACE05 (w/ hierarchy)

The ACE dataset contains documents with annotations defined for several tasks, including Named Entity Recognition, Relation Extraction, and Event Detection and Recognition. The dataset contains seven types of entities and 45 sub-entity types. We use the same data split used in previous work. The training set contains 10,360 sentences each of which includes at least one entity. The test set contains 2,637 sentences some of which may not contain any entities. The total number of entities within the sentences of the training set is 47,406, while the testing set contains 10,675 of them.

SpRL2017

The SpRL task is to identify and classify the spatial arguments of the spatial expression in a sentence. To be specific, we identify spatial roles, including "Trajector", "Spatial Indicator", and "Landmark", and detect their spatial triplet relation. We evaluated with CLEF 2017 mSpRL dataset, which has 600 sentences in the training set and 613 sentences in the testing set. The dataset is more challenging because of the complicated triplet relations and fewer examples compared to other tasks.

Files

Steps

Please follow the steps in requirement to setup an environment for the experiments.

And then, please refer to specific example directory and and corresponding section in our paper for steps to run the experiments.