Commit cbb76413 authored by Jana Germies's avatar Jana Germies
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# Personalizing Conversational AI Using the Big Five Personality Traits
## Practical implementation of the Master's Thesis submitted in partial fulfillment to the requirements for an M.A. in Computational Linguistics at Ruhr-University Bochum
## Practical implementation of the Master's Thesis submitted in partial fulfillment to the requirements for an Master's
Degree in Computational Linguistics at Ruhr-University Bochum
### Abstract
As human-machine interactions become ever more frequent, the trend in Artificial Intelligence goes
towards personalized conversational agents. These agents mimic human behavior in respect to
personality and are either bestowed with their own persona or have received the ability to read and
respond to human emotion. Studies attest the success of such agents with increased measurements in
language competence and user satisfaction.
This thesis takes an approach to personalization that is based in psycholinguistic and personality
theory. The aim is to further improve the user experience in human-chatbot interaction by developing
a conversational agent that is flexible in its linguistic behavior and adapts to the user's
personality. It is assumed, that the user will feel more comfortable talking to the conversational
agent, when it embodies a personality that is similar to the user's, as to when it is dissimilar.
To that end, a new dataset comprising personality annotated dialogues was collected. Furthermore,
state-of-the-art Transformer neural networks and methods such as transfer learning are used for the
implementation of the agent.
In this context, the thesis covers fundamental linguistic dialogue theory, as well as pioneering and
contemporary techniques in dialogue response generation and machine learning. The focus will lie on
Transformer-based conversational agents and end-to-end approaches. Unfortunately, the thesis is not
able to provide a clear conclusion on the benefits of incorporating personality traits in
personalization, yet offers some discussion on issues with the approach in general and possible
improvements in the future.
### About
The practical part of the thesis is divided in two parts. The first part is concerned with data understanding
......@@ -47,8 +69,8 @@ label for language modeling and next-sentence prediction.
#### Step 2.3: Fine-tune the model on a multi-task objective
The dialogue model is fine-tuned on a multi-task objective using the preprocessed dataset. **train.py**
will preprocess the data and begin fine-tuning. The base model is loaded from the **HuggingFace Transformers
community model hub**.
will preprocess the data and begin fine-tuning. Model checkpoints are saved at modeling/runs/. The pretrained base model
is loaded from the **HuggingFace Transformers community model hub**.
### Notes:
As of now, the implementation produces some issues and is not running bug-free. The main issue is an
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