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+# Advanced lab 2: Building a labelled parser
+
+## Objective
+
+In this assignment, you will take the existing from-scratch implementation of the GPT architecture from lab 2 and modify it to implement the BERT architecture with minimal necessary changes. You will validate your implementation by loading pre-trained BERT weights from [Hugging Face](https://huggingface.co) and verifying that it produces the same input-output behaviour as the official BERT model.
+
+## Instructions
+
+1. **Understand the architecture**
+
+   - Read Section 3 of [Glavaš and Vulić (2021)](http://dx.doi.org/10.18653/v1/2021.eacl-main.270) to see how they compute relation scores.
+   - You also need to understand how to compute the loss for the relation prediction task.
+
+2. **Modify your parser to support labelled parsing**
+
+   - Extend or adapt your implementation of the bi-affine layer to support the computation of relation scores.
+   - Extend or adapt your implementation of the loss function.
+   - Make only the minimal necessary modifications to your existing parser.
+
+3. **Validate your implementation**
+
+   - Attempt to replicate the results reported for BERT on the EWT, Table 1 in [Glavaš and Vulić (2021)](http://dx.doi.org/10.18653/v1/2021.eacl-main.270).
+   - You only need to replicate the results for the standard setup, not for the adapter setup.
+
+4. **Add your work to your portfolio**
+
+   - Include a short report summarising the changes you made and the results of your replication attempt.
+   - Add your notebook and the report to your lab portfolio and present it at the oral exam.
+
+## Hints & considerations
+
+- Computing the arc scores can be seen as a special case of computing the relation scores.
+- The overall loss of the parser is the sum of the arc loss and the relation loss.
+
+## Deliverables
+
+- `parser.ipynb` – a notebook containing your parser implementation
+- `report.md` – short report summarising your work
+
+Good luck! 🚀