Macaw: An Extensible Conversational Information Seeking Platform

Conversational information seeking (CIS) has been recognized as a major emerging research area in information retrieval. Such research will require data and tools, to allow the implementation and study of conversational systems. Macaw is an open-source framework with a modular architecture for CIS research. Macaw supports multi-turn, multi-modal, and mixed-initiative interactions, for tasks such as document retrieval, question answering, recommendation, and structured data exploration. It has a modular design to encourage the study of new CIS algorithms, which can be evaluated in batch mode. It can also integrate with a user interface, which allows user studies and data collection in an interactive mode, where the back end can be fully algorithmic or a wizard of oz setup.

Macaw could be of interest to the researchers and practitioners working on information retrieval, natural language processing, and dialogue systems.

For more information on Macaw, please refer to this paper.

Table of content:

Macaw Architecture
Installation
Running Macaw
Bug Report and Feature Request
Citation
License
Contribution

Macaw Architecture

Macaw has a modular architecture, which allows further development and extension. The high-level architecture of Macaw is presented below:

For more information on each module in Macaw, refer to this paper.

Interfaces

Macaw supports the following interfaces:

Standard IO: For development purposes
File IO: For batch experiments (see the examples in the data folder for input and output file formats)
Telegram bot: For interaction with real users

Here is an example of the Telegram interface for Macaw. It supports multi-modal interactions (text, speech, click, etc).

Retrieval

Macaw features the following search engines:

Indri: an open-source search engine that can be used for any arbitrary text collection.
Bing web search API: sending a request to the Bing API and getting the results.

Answer Selection and Generation

For question answering, Macaw only features the DrQA model in its current version.

Installation

Macaw requires Python >= 3.6 and pip3. If you don't have setuptools, run sudo pip3 install setuptools. To install Macaw, first clone macaw from this repo and then follow the following installation steps. The mentioned installation commands can be executed on Ubuntu. You can use the same or similar commands on other Linux distribution. If you are using Windows 10, we recommend installing Macaw and all the required packages on Windows Subsystem for Linux.

Step 1: Installing MongoDB server

Macaw uses MongoDB for storing and retrieving user interactions (conversations). To install MongoDB server, run the following command:

sudo apt-get install mongodb-server-core

Step 2: Installing Indri and Pyndri

Indri is an open-source search engine for information retrieval research, implemented as part of the Lemur Project. Pyndri is a python interface to Indri. Macaw uses Indri for retrieving documents from an arbitrary text collection. To install Indri, first download Indri from https://sourceforge.net/projects/lemur/files/lemur/. As suggested by pyndri, we have used Indri-5.11. This Indri version can be installed as follows:

# download indri-5.11.tar.gz
sudo apt install g++ zlib1g-dev
tar xzvf indri-5.11.tar.gz
rm indri-5.11.tar.gz
cd indri-5.11
./configure CXX="g++ -D_GLIBCXX_USE_CXX11_ABI=0"
make
sudo make install

Then, clone the pyndri repository from https://github.com/cvangysel/pyndri and run the following command:

python3 setup.py install

At this step, you can make sure your installation is complete by running the pyndri tests.

Step 3: Installing Stanford Core NLP

Stanford Core NLP can be used for tokenization and most importantly for co-reference resolution. If you do not need co-reference resolution, you can ignore this step. Stanford Core NLP requires java. Get it by following these commands:

wget -O "stanford-corenlp-full-2017-06-09.zip" "http://nlp.stanford.edu/software/stanford-corenlp-full-2017-06-09.zip"
sudo apt-get install unzip
unzip "stanford-corenlp-full-2017-06-09.zip"
rm "stanford-corenlp-full-2017-06-09.zip"

If you don't have java, install it using:

sudo apt-get install default-jre

Step 4: Installing DrQA

Macaw also supports answer extraction / generation for user queries from retrieved documents. For this purpose, it features DrQA. If you do not need this functionality, ignore this step (you can also install this later). To install DrQA, run the following commands:

git clone https://github.com/facebookresearch/DrQA.git
cd DrQA
pip3 install -r requirements.txt
pip3 install torch
sudo python3 setup.py develop

To use pre-trained DrQA model, use the following command.

./download.sh

This downloads a 7.5GB (compressed) file and requires 25GB (uncompressed) space. This may take a while!

Step 5: Installing FFmpeg

To support speech interactions with users, Macaw requires FFmpeg for some multimedia processing steps. If you don't need a speech support from Macaw, you can skip this step. To install FFmpeg, run the following command:

sudo apt-get install

Step 6: Installing Macaw

After cloning Macaw, use the following commands for installation:

cd macaw
sudo pip3 install -r requirements.txt
sudo python3 setup.py install

Running Macaw

If you run macaw with interactive (or live) mode, you should first run MongoDB server using the following command:

sudo mongod

Note that this command uses the default database directory (/data/db) for storing the data. You may need to create this directory if you haven't. You can also use other locations using the --dbpath argument.

We provide three different main scripts (i.e., app):

live_main.py: An interactive conversational search and question answering system. It can use both STDIO and Telegram interfaces.
batch_ext_main.py: A model for running experiments on a reusable dataset. This main script uses FILEIO as the interface.
wizard_of_oz_main.py: A main script for Wizard of Oz experiments.

After selecting the desired main script, open the python file and provide the required parameters. For example, you need to use your Bing subscription key (if using Bing), the path to Indri index (if using Indri), Telegram bot token (if using Telegram interface), etc. in order to run the live_main.py script. You can further run the favorite main script as below:

python3 live_main.py

Bug Report and Feature Request

For bug report and feature request, you can open an issue in github, or send an email to Hamed Zamani at hazamani@microsoft.com.

Citation

If you found Macaw useful, you can cite the following article:

Hamed Zamani and Nick Craswell, "Macaw: An Extensible Conversational Information Seeking System", arxiv pre-print.

bibtex:

@article{macaw,
  title={Macaw: An Extensible Conversational Information Seeking Platform},
  author={Zamani, Hamed and Craswell, Nick},
  journal={arXiv preprint arXiv:1912.08904},
  year={2019},
}

License

Macaw is distributed under the MIT License. See the LICENSE file for more information.

Contribution

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

catherine-qi/macaw