There is no mandatory structure, but you should make sure that whatever you do is logical. Start with an Introduction and end with a Conclusion, with a list of References at the end. There is no need for an abstract, a table of contents, or a list of figures. Three possible ways to structure your report would be: (1) experiment-by-experiment in which you present the methods, experimental setup, results, and discussion for each experiment in its own section; (2) overall methods section, followed by experiments, followed by results and discussion, each section covering all experients; or (3) overall methods section followed by sections per experiments for the experimental process, results, and discussion.
The introduction should place your work in context, giving the overall motivation for the work, and clearly outlining the research questions you are investigating. This should include a concise description of the task and the data (be precise, eg state the size of the training/dev/test sets), linking the research questions to the task, and perhaps motivating them with reference to your previous coursework and to other papers in the literature. You get extra credit for this section if you are proposing interesting/innovated research hypotheses or if your introduction provides great insight into the task or data, with references to the literature.
When you present your experimental methods you need to clearly motivate what you have done, perhaps with references to the literature. You need to be precise in specifying all the hyperparameters used (someone reading the paper needs to be able to replicate your experiments), and you should explain why you have chosen particular hyperparameter settings. Extra credit for a wide range of experiments and hypotheses tested - this does not mean you have to focus on very deep or computationally expensive architectures: it means that you get credit for carefully and thoroughly carrying out and reporting experiments to investigate your research questions. This section should also discuss how you implemented your work in TensorFlow, giving details for design choices and showing TF implementation choices, mostly just by referencing TF functions (rather than including code).
You should also make sure you concisely describe the baseline system(s) you developed in CW3, and how they are used in this work (they might just be used as a baseline accuracy to compare against, or you may be more directly further developing those systems).
Your experimental results should be presented clearly and concisely, followed by interpretation and discussion of results and conclusions you can draw from the experiments. You need to present your results in a way that makes it easy for a reader to understand what they main. You should facilitate comparisons either using graphs with multiple curves or (if appropriate, e.g. for accuracies) a results table. You need to avoid many plots spread over several pages, poorly labelled graphs, graphs which should be comparable but which use different axis scales. A good presentation will enable the reader to compare trends in the same graph - each graph should summarise the results relating to a particular hypothesis or (sub)question.
Minimally you need to show the learning curves (error vs epoch) for training and dev, and the test set accuracies. You should use your CW3 baseline system as a reference you compare against. Extra credit would be given for results which give other analyses beyond just the learning curves and accuracies/errors.
Your discussion should interpret the results, both in terms of summarising the outcomes of a particular test/hypothesis, and attempting to relate to the underlying models. A good report would have a detailed analysis, and would show good understanding of why particular results are observed, perhaps with reference to the literature.
You should draw conclusions from each experiment, related to the research question which motivated it. Your should state the conclusions clearly and concisely. It is also good if the conclusion from one experiment influence what you do in later experiments - your aim is to learn from your experiments. Extra credit if you relate your findings to what has been reported in the literature.
If you are using an experiment-by-experiment structure, it is OK to have conclusions for an experiment in "its" section, but you should also have an overall conclusions section at the end. If there is an overall conclusion across the research questions, then discuss it, and also you might reprise what you conclude in terms of your "best" system (based on your research questions and constraints).
A good conclusions section would also include a further work discussion, building on work done so far, and referencing the literature where appropriate.
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