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CARTON w/ Named Entity Recognition

Architecture

CARTONNER

Premise

Expand CARTON beyond static knowledge and allow it to work with dynamic Knolwedge Graphs. Retrain with new dataset and new Insert class (logical form action type), which will update the underlying Knowledge Graph with new RDF triples based on information from the user input sentence.

Below is a work in progress Readme adapted from the original repository.

Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs

Re-implementation using code base and grammar from here. Original implementation was made by joanPlepi.

Neural semantic parsing approaches have been widely used for Question Answering (QA) systems over knowledge graphs. Such methods provide the flexibility to handle QA datasets with complex queries and a large number of entities. In this work, we propose a novel framework named CARTON, which performs multi-task semantic parsing for handling the problem of conversational question answering over a large-scale knowledge graph. Our framework consists of a stack of pointer networks as an extension of a context transformer model for parsing the input question and the dialog history. The framework generates a sequence of actions that can be executed on the knowledge graph. We evaluate CARTON on a standard dataset for complex sequential question answering on which CARTON outperforms all baselines. Specifically, we observe performance improvements in F1-score on eight out of ten question types compared to the previous state of the art. For logical reasoning questions, an improvement of 11 absolute points is reached.

CARTON

CARTON (Context Transformer with Stacked Pointer Networks architecture) architecture. It consists of three modules: 1) A Transformer-based contextual encoder which produces the representation of the current context of the dialogue. 2) A logical form decoder that generates the pattern of actions defined by our proposed grammar. 3) The stacked pointer networks that initialize the KG items to fetch the correct answer.

Requirements and Setup

Python version >= 3.9 PyTorch version >= 1.12.0

# clone the repository
git clone https://github.com/vejvarm/CARTONNER.git
cd CARTONNER
pip install -r requirements.txt

Dataset

CSQA

The original framework was evaluated on CSQA dataset. You can download the dataset from here.

CSQA-D2T

We expand the CSQA dataset with a new Insert action class entries. CSQA-D2T:

Wikidata Knowlegde Graph

Since CSQA is based on Wikidata Knowlegde Graph, the authors provide a preproccesed version of it which can be used when working with the dataset. You can download the preprocessed files from here. After dowloading you will need to move them under the knowledge_graph directory.

Prepare Wikidata Knowlegde Graph Files

We prefer to merge some JSON files from the preprocessed Wikidata, for accelerating the process of reading all the knowledge graph files. In particular, we create three new JSON files using the script prepare_data.py. Please execute the script as below.

# prepare knowlegde graph files
python scripts/prepare_data.py

Annotate Dataset

Next, using the preproccesed Wikidata files we can annotate CSQA dataset with our proposed grammar.

# annotate CSQA dataset with proposed grammar
python annotate_csqa/preprocess.py --partition train --annotation_task actions --read_folder /path/to/CSQA --write_folder /path/to/write

Create BERT embeddings

Before training the framework, we need to create BERT embeddings for all the knowledge graph entities. You can do that by running.

# create bert embeddings
python scripts/bert_embeddings.py

Train Framework

For training you will need to adjust the paths in args file. At the same file you can also modify and experiment with different model settings.

# train framework
python train.py

Inference Framework

Calculates accuracy and recall on test split

  • accuracy averaging: 'micro'
  • recal averaging: 'macro'
python inference.py --name "00_csqa_on_merged" --batch-size 40 --model-path experiments/models/CARTONNER_csqa_e10_v0.0102_multitask.pth.tar --data-path data/csqa-merged --cache-path .cache/merged/

will save metric results as JSON files into ROOT_PATH/args.path_inference/args.name folder.

Generate Actions

After the model has finished training we perform the inference in 2 steps. First, we generate the actions and save them in JSON file using the trained model.

# generate actions for a specific question type
python test.py --question_type Clarification

Execute Actions

Second, we execute the actions and get the results from Wikidata files.

# execute actions for a specific question type
python action_executor/run.py --file_path /path/to/actions.json --question_type Clarification

Changes

Generating BERT embeddings on the fly for new labels

New file embeddings.py and new class EmbeddingGenerator to manage embeddings generated by BERT.

eg = EmbeddingGenerator([path_to_embedding_database_file])
id, label, emb = eg.add_entry(label)  # -> (id: str, label: str, emb: np.ndarray)

if label exists in database_file: return the existing (id, label, emb) tuple
else: generate new id and emb, add it to database_file and return (id, label, emb)

License

The repository is under MIT License.

Cite

CARTON

@InProceedings{10.1007/978-3-030-77385-4_21,
author="Plepi, Joan
and Kacupaj, Endri
and Singh, Kuldeep
and Thakkar, Harsh
and Lehmann, Jens",
editor="Verborgh, Ruben
and Hose, Katja
and Paulheim, Heiko
and Champin, Pierre-Antoine
and Maleshkova, Maria
and Corcho, Oscar
and Ristoski, Petar
and Alam, Mehwish",
title="Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs",
booktitle="The Semantic Web",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="356--371",
abstract="Neural semantic parsing approaches have been widely used for Question Answering (QA) systems over knowledge graphs. Such methods provide the flexibility to handle QA datasets with complex queries and a large number of entities. In this work, we propose a novel framework named CARTON (Context trAnsformeR sTacked pOinter Networks), which performs multi-task semantic parsing for handling the problem of conversational question answering over a large-scale knowledge graph. Our framework consists of a stack of pointer networks as an extension of a context transformer model for parsing the input question and the dialog history. The framework generates a sequence of actions that can be executed on the knowledge graph. We evaluate CARTON on a standard dataset for complex sequential question answering on which CARTON outperforms all baselines. Specifically, we observe performance improvements in F1-score on eight out of ten question types compared to the previous state of the art. For logical reasoning questions, an improvement of 11 absolute points is reached.",
isbn="978-3-030-77385-4"
}