Developed a document retrieval system to return titles of scientific papers containing the answer to a given user question. I used the first version of the COVID-19 Open Research Dataset (CORD-19)
| Notebook | Link to jupyter nbviewer | Link to Colab |
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| SBERT_CORD19_Preprocess.ipynb |  |
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| SBERT_CORD19_QA_CrossEncoders.ipynb | |
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| SBERT_CORD19_QA_Doc2Vec.ipynb | |
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| SBERT_CORD19_QA_InferSent.ipynb | |
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| SBERT_CORD19_QA_Roberta.ipynb | |
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Articles in the folder comm_use_subset.
| Question examples | Possible Answers |
|---|---|
| What are the coronoviruses? | Coronaviruses (CoVs) are common human and animal pathogens that can transmit zoonotically and cause severe respiratory disease syndromes. |
| What was discovered in Wuhuan in December 2019? | In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. |
| What is Coronovirus Disease 2019? | Coronavirus Disease 2019 (COVID-19) is an emerging disease with a rapid increase in cases and deaths since its first identification in Wuhan, China, in December 2019. |
| What is COVID-19? | COVID-19 is a viral respiratory illness caused by a new coronavirus called SARS-CoV-2. |
| What is caused by SARS-COV2? | Coronavirus disease (COVID-19) is caused by SARS-COV2 and represents the causative agent of a potentially fatal disease that is of great global public health concern. |
| How is COVID-19 spread? | First, although COVID-19 is spread by the airborne route, air disinfection of cities and communities is not known to be effective for disease control and needs to be stopped. |
| Where was COVID-19 discovered? | In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. |
| How does coronavirus spread? | The new coronavirus was reported to spread via droplets, contact and natural aerosols from human-to-human. |
This repository consists of 5 notebooks
Firstly, I needed to pre-process the CORD-19 (first version) dataset, that is consisted of multiple papers, focused on COVID-19 pandemic and disease. This dataset gives a lot of information for every paper and so I had to choose what I should and shouldn’t use. I decided to use the corpus of each paper, with the pre-process of:
- Small sentences, with size < 3 words are dropped.
- Removed references to other papers.
- Removed links.
- Removed list symbols and other unicodes. Also, I tried using Summarizer from bert-extractive-summarizer because papers corpus was extremely big in some papers and training of that model needed hours to finish.
Data-storage I am using a big dictionary of: sentence −→ (paper_id,paper_title) If a sentence belongs to more than one papers, it is not a problem, as question is answered from at least one paper. This dictionary I am storing it and reading it every time I need (.pickle file)
In these two tasks I had to build multiple QA models and remark their performance in various ways of creating the embeddings. I have implemented the following embedding approaches:
- SentenceBert
- Notebook:
SBERT_CORD19_QA_Roberta.ipynb - Pre-trained model: paraphrase-distilroberta-base-v1
- Results: Not very good and this wasn’t expected. Only a few from the given questions answered. In mine questions, half of them had been answered with questions. I had multiple questions with extremely irrelevant answers. I noticed that summarized papers needed much less time to transform to embeddings with not much difference in answers. Finally, the number of papers 6000 or 9000 in summarized papers had a very small differnce in results and still small time of execution (in CUDA).
- Future work: I would remove the sentences from the corpus that are questions and probably I would change the pretrained NN that I used.
- Notebook:
- CrossEncoders
- Notebook:
SBERT_CORD19_QA_CrossEncoders.ipynb - Pre-trained model:
- NN: msmarco-distilbert-base-v2 and
- cross-encoder: ms-marco-TinyBERT-L-6
- Results: Non summarized papers had the best results so far as lots of the questions were answered. Summarized papers model, didn’t have good performance and this due to mine summarization and not due the model selection (in my understanding).
- Future work: Process again the summarized papers by increasing the ratio and max_length
- Notebook:
- InferSent
- Notebook:
SBERT_CORD19_QA_InferSent.ipynb - Implemented but stucked to encoding. I tried to make it work but I get some errors that I don’t understand. No remarks for this model
- Notebook:
- Doc2Vec
- Notebook:
SBERT_CORD19_QA_Doc2Vec.ipynb - Results: Terrible results for both summarized and not papers. I can’t understand what I am doing wrong
- Future work: Implementation again from the very start.
- Notebook:
Finally, from my experiments the best model based on criteria:
- Time needed for creating the embeddings
- Answer success
I conclude that Sentence Transformer in combination with Cross Encoders is the best model as it has the best results in answers. The time needed for creating the embeddings is approximately the same among the models. (all these remarks for the 6000 papers)
© Konstantinos Nikoletos | 2020 - 2021