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arg-tech / amf_intent_recognition

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Default Intent Recognition using RoBERTa-large

This service, part of the Argument Mining Framework (AMF), classifies illocutionary forces in locutions as either Agreeing, Arguing, Asserting, Assertive Questioning, Challenging, Default Illocuting, Disagreeing, Pure Questioning, Restating, Rhetorical Questioning. It leverages a pre-trained LLM fine-tuned on the QT30, US2016, MM123, and MM2012 dataset. It can be integrated into the argument mining pipeline alongside other AMF components for further analysis and processing.

Table of Contents

Brief Overview of the Architecture/Method

This application leverages a pruned and quantized RoBERTa-large model, fine-tuned on the AIFdb datasets, to perform communicative intent recognition in text-classification task settings. By pruning and quantizing the model, we achieve faster inference times without significantly compromising accuracy. The system maps illocutionary forces to their respective locutions in discourse. This approach ensures the model remains lightweight and suitable for real-time applications within AMF.

Endpoints

/amf_nts

Details

GET Method

POST Method

Input and Output Formats

Input Format

Output Format

The YA text values are updated with the values from the model's predictions.

{
  "AIF": {
    "descriptorfulfillments": null,
    "edges": [
      {
        "edgeID": 0,
        "fromID": 0,
        "toID": 4
      },
      {
        "edgeID": 1,
        "fromID": 4,
        "toID": 3
      },
      {
        "edgeID": 2,
        "fromID": 1,
        "toID": 6
      },
      {
        "edgeID": 3,
        "fromID": 6,
        "toID": 5
      },
      {
        "edgeID": 4,
        "fromID": 2,
        "toID": 8
      },
      {
        "edgeID": 5,
        "fromID": 8,
        "toID": 7
      },
      {
        "edgeID": 6,
        "fromID": 3,
        "toID": 9
      },
      {
        "edgeID": 7,
        "fromID": 9,
        "toID": 7
      }
    ],
    "locutions": [
      {
        "nodeID": 0,
        "personID": 0
      },
      {
        "nodeID": 1,
        "personID": 1
      },
      {
        "nodeID": 2,
        "personID": 2
      }
    ],
    "nodes": [
      {
        "nodeID": 0,
        "text": "disagreements between party members are entirely to be expected.",
        "type": "L"
      },
      {
        "nodeID": 1,
        "text": "the SNP has disagreements.",
        "type": "L"
      },
      {
        "nodeID": 2,
        "text": "it's not uncommon for there to be disagreements between party members.",
        "type": "L"
      },
      {
        "nodeID": 3,
        "text": "disagreements between party members are entirely to be expected.",
        "type": "I"
      },
      {
        "nodeID": 4,
        "text": "Asserting",
        "type": "YA"
      },
      {
        "nodeID": 5,
        "text": "the SNP has disagreements.",
        "type": "I"
      },
      {
        "nodeID": 6,
        "text": "Arguing",
        "type": "YA"
      },
      {
        "nodeID": 7,
        "text": "it's not uncommon for there to be disagreements between party members.",
        "type": "I"
      },
      {
        "nodeID": 8,
        "text": "Disagreeing",
        "type": "YA"
      },
      {
        "nodeID": 9,
        "text": "Default Inference",
        "type": "RA"
      }
    ],
    "participants": [
      {
        "firstname": "Speaker",
        "participantID": 0,
        "surname": "1"
      },
      {
        "firstname": "Speaker",
        "participantID": 1,
        "surname": "2"
      }
    ],
    "schemefulfillments": null
  },
  "dialog": true,
  "ova": [],
  "text": {
    "txt": " Speaker 1 <span class=\"highlighted\" id=\"0\">disagreements between party members are entirely to be expected.</span>.<br><br> Speaker 2 <span class=\"highlighted\" id=\"1\">the SNP has disagreements.</span>.<br><br> Speaker 1 <span class=\"highlighted\" id=\"2\">it's not uncommon for there to be disagreements between party members. </span>.<br><br>"
  }
}

Installation

Requirements for Installation

Installation Setup

Using Docker Container

  1. Clone the Repository:

    git clone https://github.com/arg-tech/amf_intent_recognition.git

  2. Navigate to the Project Root Directory:

  3. Make Required Changes:

    • Edit the Dockerfile, and docker-compose.yml files to specify the container name, port number, and other settings as needed.

  4. Build and Run the Docker Container:

    docker-compose up --build

Usage

Using Programming Interface

Example Python Code Snippet

import requests
import json

url = 'http://your-server-url/amf_nts'
input_file_path = 'example_xAIF.json'

with open(input_file_path, 'r', encoding='utf-8') as file:

    files = {'file': (input_file_path, file, 'application/json')}

response = requests.post(url, files=files)

if response.status_code == 200:

    output_file_path = 'output_xAIF.json'

    with open(output_file_path, 'w', encoding='utf-8') as output_file:

        json.dump(response.json(), output_file, ensure_ascii=False, indent=4)

    print(f'Response saved to {output_file_path}')

else:

    print(f'Failed to make a POST request. Status code: {response.status_code}')

    print(response.text)

Using cURL

curl -X POST \
  -F "file=@example_xAIF.json" \
  http://your-server-url/amf_nts

Using Web Interface

The service can also be used to create a pipeline on our n8n interface. The service can also be used to create a pipeline on our n8n interface.

  1. Create an HTTP node
  2. Configure the node
    • Specify the URL of the service
    • Include the parameter (file)
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