tianleimin/emocauses

Emotion causal relationship in conversation

Intro

This repo contains public datasets and associated source codes to run experiments for my Honour thesis project at Monash.

Notes of methods, experiments, and results are also recorded along the way.

Setup

(Optional) Intel Math Kernel Library (MKL)

If you decide to use this lib, it should be installed first, as all subsequent libs install might reference this

This boosts linear algebra computation for both R and Python numerical computing packages.

• Installation guide http://dirk.eddelbuettel.com/blog/2018/04/15/#018_mkl_for_debian_ubuntu

• Reference this list for latest MLK build https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo

• Installed version is intel-mkl-64bit-2019.4-070

R

• Add CRAN repo to Ubuntu, then install latest R package https://cran.rstudio.com/

• Installed version is 3.6.1

Inference Algorithms

Please see R code for further package installation

Python

• Install from source

  • Download latest release from https://www.python.org/downloads/. Installed version is 3.8.0
  • (Optional) Remove existing python3 installation, be careful to not delete Ubuntu system python3
  • Build and install from source, be careful to not override Ubuntu system python3 https://linuxize.com/post/how-to-install-python-3-7-on-ubuntu-18-04/

• Best to build new release and use Python3.8 directly. As the default Python3 installed via Ubuntu apt has a few custom scripts important for the system https://www.itsupportwale.com/blog/how-to-upgrade-to-python-3-7-on-ubuntu-18-10/

• Setup a Python venv https://docs.python.org/3/library/venv.html

python3.8 -m venv .venv
source .venv/bin/activate

SpaCy

• Install SpaCy via pip https://spacy.io/usage

source .venv/bin/activate
pip install -U spacy
pip install -U spacy-lookups-data
python -m spacy download en_core_web_sm

Python Packages

cython, numpy, scipy, scikit-learn, spaCy, pandas

(Optional) Build numpy with MKL-enabled

This post details how to compile numpy from source with MKL-enabled. https://www.elliottforney.com/blog/npspmkl/

pandas, scipy, and scikit-learn use numpy.

If you decide to go this route for Python performance boost, remove existing installation of these libraries first, install numpy with MKL, then re-build and install them from source

Dialogue Act Tagger

The folder tagger is forked from this project. It is a Git submodule reference the forked project. The fork contains my own change to speed up the project and adapt to my research.

Original

Fork

Install scikit-learn, spaCy

Install spaCy en-core model

Since the tagger folder is a child folder, it shares the same set of Python libraries as the main repo

Datasets

Dyadic Conversation

MELD Friends

Dialogue Act Tagger

AMI Oasis BT (official license expires in 2009, dataset download link is still accessible) Switchboard Maptask

Links for dataset download is in the original repo https://github.com/ColingPaper2018/DialogueAct-Tagger

Steps

1. Download 3 datasets for tagger
2. Train tagger model as instructed in the author's original repo
3. Run nlp script to model conversation features
4. Run R script for inference

References

MELD dataset

Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., & Mihalcea, R. (2018). Meld: A multimodal multi-party dataset for emotion recognition in conversations. ​ACL (57).

ISO independent dialogue tagger

Mezza, S., Cervone, A., Tortoreto, G., Stepanov, E. A., & Riccardi, G. (2018). ISO-standard domain-independent dialogue act tagging for conversational agents. ​ COLING, (27) ​ .

pcalg

Kalisch, M., Hauser, A., Maechler, M., Colombo, D., Entner, D., Hoyer, P., ..., Eigenmann, M. (2019). pcalg: Methods for Graphical Models and Causal Inference (version 2.6-7) [Computer Software]. Retrieved from ​https://cran.r-project.org/package=pcalg

Causal inference theory behind PC and FCI

Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search. Adaptive computation and machine learning.

InvariantCausalPrediction

Meinshausen, N. (2018). InvariantCausalPrediction: Invariant Causal Prediction (version 0.7-2) [Computer Software]. Retrieved from https://cran.r-project.org/package=InvariantCausalPrediction

Theory behind InvariantCausalInference

Peters, J., Bühlmann, P., & Meinshausen, N. (2016). Causal inference by using invariant prediction: identification and confidence intervals. ​ Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78 ​ (5), 947-1012.