Textual-Coherence

Dataset

The datasets used are:

The dataset is annotated into classes 1/2/3 where 3 denotes the most coherent paragraph. Instructions regarding the dataset can be found here

The dataset consists of a set of coherent sentences along with respective replacements to make them incoherent. Additional details can be found here

The data is present in the data folder
The code used to preprocess the data is present in the preprocessing folder
We have used three models, each one is represented in respective notebooks. (LSTM.ipynb, GRU.ipynb, RNN.ipynb)

The datasets can be downloaded from the above given links. Preprocessed zip folders are present in the data folder of the repository.
Steps to run the model are present in the respective notebooks.

The following approaches were used on the GCDC corpus to fine tune the models:

Some of the testing data was used for training as we realised that training data was insufficient for an accurate prediction.
The corpus had data annotated to 1/2/3 depicting the levels of coherence, so we initially implemented a three way classifier. Later we converted it into a binary classifier.This lead to a considerable increase in the accurace.
We used cosine similarity between adjacent sentences of the paragraphs as a parameter to calculate coherence. At first we used average similarity of the paragraph which we then changed to minimum similarity.

The above methods were tried on different training and testing datas from the GCDC corpus and the best model was saved.

The best model in each method was used on the Wikipedia-CNN Corpus to observe the accuracies.

Without using similarity as a parameter
- 3000 training data, 600 testing data, three way classifier: approx 30%
- 4600 training data, Yahoo test data, three way classifier: approx 36.5%
- 4600 training data, Yahoo test data, binary classifier: approx 55%
- 4600 training data, Clinton test data, binary classifier: approx 64.99%
Using Average Similarity as a parameter
- 4600 training data, Clinton test data, three way classifier: approx 34%
Using Minimum Similarity as a parameter
- 4600 training data, Clinton test data, three way classifier: approx 39.5%
- 4600 training data, Clinton test data, binary classifier: approx 67%
We observe different results with different test datas, thus we compared the performance of all the test data available in the GCDC corpus using that LSTM model, binary classification with minimum similarity as a parameter.
- Results Obtained:
  - Clinton: approx 61%
  - Enron: approx 66%
  - Yahoo: approx 54.5%
  - Yelp: approx 66.5%
- It is fair to assume that Enron has best performance as it is more closed domained than the rest. Similarly, the Yahoo Question-Answer corpus is the most open domained.

Binary classifier performed significantly better that three way multi classifier, thus we used only binary classifiers for the Wikipedia-CNN corpus.

Without using similarity as a parameter
- 4600 training data, 200 Clinton test data, three way classifier: approx 41.99%
- 4600 training data, 200 Clinton test data, binary classifier: approx 57.99%
Using Minimum Similarity as a parameter
- 4600 training data, 200 Clinton test data, binary classifier: approx 63.99%

Without using similarity as a parameter
- 4600 training data, 200 Clinton test data, three way classifier: approx 32.49%
- 4600 training data, 200 Clinton test data, binary classifier: approx 55.5%
Using Minimum Similarity as a parameter
- 4600 training data, 200 Clinton test data, binary classifier: approx 53.5%

In depth analysis can be found in the Report.pdf