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Master Thesis Instructions

Contact Points:

This repository contains the information used for the development a master thesis in the NLP domain at PoliTO.

Table of contents

Instructions and templates

The latex template to write the master thesis is avaiable in Overleaf

The first step is to create a GitHub Educational account and create an ad-hoc repository containing all relevant code and information for the master thesis.

The research work expected during the development of the master thesis will cover the following steps.

State-of-the-art exploration

Collect, read and analyze the most recent and relevant publications in the proposed application field. Related works could be summarized and presented by using the Markdown Template available here. Publication could be searched by using the following services:

Data collection and finding

The majority of the thesis requires a step of data collection or data search. During the exploration of the state of the art the student is asked to collect and organize the data used by each publication. Dataset must be presented in an organized way by expoiting the Markdown template available here If a new data collection is created/parsed please create a specific Markdown file (template available here) explaining both the data collection procedure and the statistics of the data collection.

A video tutorial explaining how to create a Python package is available here: YouTube .

Thesis proposals

AL meets NLP Available

Active Learning Natural Language Processing

immagine image source: https://www.semanticscholar.org/paper/Deep-active-learning-using-Monte-Carlo-Dropout-Moura/5418d0d92165270afa20cf84e93b0beeba41202f

Active Learning is a subfield of machine learning that aims at reducing the number of supervised samples needed to train a machine learning model. It include an human-in-the-loop procedure aimed at selecting the most relevant examples for model's learning.

The main objectives of this thesis are:

  • Explore the state of the art in Active Learning
  • Define and propose an Active Learning approach for the fine-tuning of deep neural language models.
  • Simulate the process on existing benchmark dataset

References 📚:

Citovsky, G., DeSalvo, G., Gentile, C., Karydas, L., Rajagopalan, A., Rostamizadeh, A., & Kumar, S. (2021). Batch Active Learning at Scale. arXiv preprint arXiv:2107.14263. article

Peshterliev, S., Kearney, J., Jagannatha, A., Kiss, I., & Matsoukas, S. (2019, June). Active Learning for New Domains in Natural Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers) (pp. 90-96). article

Schröder, C., & Niekler, A. (2020). A Survey of Active Learning for Text Classification using Deep Neural Networks. arXiv preprint arXiv:2008.07267. article

Interesting projects 💻:

Additional Material:

Active Learning: Algorithmically Selecting Training Data to Improve Alexa’s Natural-Language Understanding post

Active Learning for Natural Language Processing. post


Text Paraphrasing Available

Natural Language Processing

synonymNew image by: https://quillbot.com/

Text paraphrasing aims at reformulating natural language by conveying the same meaning with different words. For example:

  • I would like to know if tomorrow will be sunny in Turin.
  • Will it be sunny tomorrow in Turin?
  • Will be a sunny day in Turin tomorrow?

All those sentences bring the same meaning using slightly different formulations. The syntactic element are different, however the semantic behind the is the same.

The main objectives of this thesis are:

  1. Analyze the state-of-the-art in text paraphrasing models.
  2. Exploit latest advancement in deep learning techniques to create a text paraphrasing tool based on Transformers.
  3. Evaluate the proposed pipeline on benchmark datasets (Quora, ParaSCI, etc.).

References 📚:

Dong, Q., Wan, X., & Cao, Y. ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation. article

Hosking, T., & Lapata, M. (2021). Factorising Meaning and Form for Intent-Preserving Paraphrasing. arXiv preprint arXiv:2105.15053. article


Unsupervised Representation Learning Available

Natural Language Processing

immagine image by: https://arxiv.org/abs/2009.12061

Learning effective representations of the input data is one of the main challenges of deep learning algorithms. While it is framed as an independent task, effective representations are beneficial in a variety of contexts and domains. Contrastive Learning is one of the hot-topics in current research. It allows exploiting un-annotated data to let the model learns by contrastive examples. It all started in the Computer Vision domain with some groundbreaking publications (e.g., SimCLR), however, at the time of writing, almost 400 papers have been published both for CV and NLP.

The main objectives of this thesis are:

  1. Analyze the state-of-the-art in contrastive learning and data augmentation techniques (mainly in NLP).
  2. Find interesting spots where Contrastive Learning can be effectively exploited (e.g., structured data representation)
  3. Define a novel contrastive learning algorithm and demonstrate its effectiveness in real-world scenarios. The model should be trained and evaluated on benchmark dataset (the Master Thesis could involve also data collection).

References 📚:

Radford, Alec, et al. "Learning transferable visual models from natural language supervision." arXiv preprint arXiv:2103.00020 (2021). article

Chen, Ting, et al. "A simple framework for contrastive learning of visual representations." International conference on machine learning. PMLR, 2020. article

Hosking, T., & Lapata, M. (2021). Factorising Meaning and Form for Intent-Preserving Paraphrasing. arXiv preprint arXiv:2105.15053. article

Zhang, Yan, et al. "An unsupervised sentence embedding method by mutual information maximization." arXiv preprint arXiv:2009.12061 (2020). article

Lilian Weng - Contrastive Representation Learning

Interesting projects 💻:

solo-learn library

TensorFlow Similarity


Social media analytics Available

Natural Language Processing

immagine image source: https://medium.com/@aadityamonu67/introduction-to-natural-language-processing-nlp-f834c4aa7357

Analyzing social data is the process of examining how people relate to each other in social settings by using data from social networking sites. The goal may simply be understanding human behavior or propagating a story that will be of interest to the target audience. Methods might involve analyzing how data flows within a network, discovering influential nodes (people, entities, etc. ), or discovering trending topics.

The main objectives of this thesis are:

  1. Analyze the state-of-the-art in NLP applied to multilingual contexts.
  2. Design and implement a machine learning pipeline to extract relevant information from social media posts.
  3. Test and evaluate the proposed methodology on real-world benchmark data collections provided by Facebook and Instagram.

References 📚:

Ferreira, C. H., Murai, F., Silva, A. P., Almeida, J. M., Trevisan, M., Vassio, L., ... & Drago, I. (2021). On the dynamics of political discussions on Instagram: A network perspective. Online Social Networks and Media, 25, 100155. https://arxiv.org/pdf/2109.09152.pdf

Trevisan, M., Vassio, L., & Giordano, D. (2021). Debate on online social networks at the time of COVID-19: An Italian case study. Online Social Networks and Media, 23, 100136. https://arxiv.org/pdf/2106.01013.pdf


NLP-aided voice cloning Not Available

Natural Language Processing Audio Processing

immagine

image by: https://deepmind.com/blog/article/wavenet-generative-model-raw-audio

What if you can speak with someone else voice? Voice imitation is usually challenging from an human perspective. It requires both specific training and great voice flexibility. This thesis aims at exploring the intersection of audio and natural language processing domain for the task of voice cloning.

The main objectives of this thesis are:

  1. Analyze the state-of-the-art voice cloning.
  2. Leverage state-of-the-art NLP models.
  3. Define a novel approach and demonstrate its effectiveness both using benchmark datasets and real-world data (Master Thesis could involve also data collection).

References 📚:

Voice Cloning: a Multi-Speaker Text-to-Speech Synthesis Approach based on Transfer Learning article

Neural Voice Cloning with a Few Samples article

Blog post

Interesting projects 💻:

Real time voice cloning


Efficent AI-powered platform for scientific literature Available

Natural Language Processing SW Design

immagine

image by: https://grammarly.com

The task of reviewing the literature for a scientific project is one of the most crucial steps for its success. This thesis is intented to apply several NLP architecture and tools for generating a platform aimed at helping researchers during the academic literature review.

The main objectives of this thesis are:

  1. Define the scope and the tools for platform develpment.
  2. Leverage state-of-the-art NLP models for classification, summarization, automated scoring, user-item similarity, etc.
  3. Implement, train and make efficient relevant DL architectures that can be used in the platform.
  4. Platform deployment.

References 📚:

ArXiv Sanity platform

Semantic Scholar platform

A Web-based Knowledge Hub for Exploration of MultipleResearch Article Collections paper

Interesting projects 💻:

arxiv scraper

Graduated Students

About

This repo contains master thesis proposals available at DBMDG Group (Politecnico di Torino) for Deep Learning applied to NLP and Audio Processing.

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