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DASC7606 Machine Reading Comprehension
创建日期：2024-05-22 15:02:07
标签： DASC7606
Machine Reading Comprehension

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DASC-7606 Assignment 2 NLP: Machine Reading Comprehension
For questions and discussion,
We encourage you to use GitHub Issues of this repository.
Or if you prefer online doc: Discussion doc.
 Please don't upload your
answers or this codebase to any public platforms (e.g., GitHub) before permitted. All
rights reserved.
1 Introduction
1.1 What is Machine Reading Comprehension?
Open-domain Question Answering (ODQA) is a type of natural language processing task,
which asks a model to produce answers to factoid questions posed in natural language.
Open-domain means that the question is not limited to a specific domain but could be
anything about real-world knowledge and common sense. Based on whether external
context and background are provided to help answer the question, there are two types
of question answering, namely Open-book QA and Closed-book QA. In Open-book QA, the
system is equipped with an abundant knowledge base to refer to, while a Closed-book QA
system has to memorize all the world knowledge in its parameter. Usually, a standard
Open-book QA system works in a two-step pipeline: First, a retriever scans the
external knowledge base to filter related context, and then a reader is responsible
for extracting or generating the answer according to the retrieved context. In this
assignment, we focus on a simpler case in Open-book QA where the relevant document is
provided. It is also called Machine Reading Comprehension (MRC) in some literature.
1.2 What will you learn from this assignment?
This assignment will walk you through specific open-domain question-answering tasks.
You can refer to the following example for an intuitive illustration.
Context: The Review of Politics was founded in 1939 by Gurian, modeled after German
Catholic journals. It quickly emerged as part of an international Catholic
intellectual revival, offering an alternative vision to positivist philosophy. For 44
years, the Review was edited by Gurian, Matthew Fitzsimons, Frederick Crosson, and
Thomas Stritch. Intellectual leaders included Gurian, Jacques Maritain, Frank
O'Malley, Leo Richard Ward, F. A. Hermens, and John U. Nef. It became a major forum
for political ideas and modern political concerns, especially from a Catholic and
scholastic tradition.
Question: Over how many years did Gurian edit the Review of Politics at Notre Dame?
Answer: 44
Besides, you will fine-tune a pre-trained language model into Question Answering task
from scratch, and you will also be asked to utilize the standard metrics for
evaluation.
The goals of this assignment are as follows:
Understand the problem formulation and the challenge of open-domain question
answering.
Understand how pre-trained language models can be used for extractive open-
domain question answering.
Implement a Question Answering (Reading Comprehension) model by fine-tuning a
pre-trained language model from scratch and utilizing existing metrics for
evaluation.
2 Setup
You can work on the assignment in one of two ways: locally on your own machine, or on
a virtual machine on HKU GPU Farm.
2.1 Working remotely on HKU GPU Farm (Recommended)
Note: after following these instructions, make sure you go to work on the assignment
below (i.e., you can skip the Working locally section).
As part of this course, you can use HKU GPU Farm for your assignments. We recommend
you follow the quickstart provided by the official website to get familiar with HKU
GPU Farm.
After checking the quickstart document, make sure you have gained the following
skills:
Knowing how to access the GPU Farm and use GPUs in interactive mode. We
recommend using GPU support for this assignment, since your training will go
much, much faster.
Getting familiar with running Jupyter Lab without starting a web browser.
Knowing how to use tmux for unstable network connections.
2.2 Working locally
If you have the GPU resources on your own PC/laptop. Here’s how you install the
necessary dependencies:
Installing GPU drivers (Recommend if working locally): If you choose to work locally,
you are at no disadvantage for the first parts of the assignment. Still, having a GPU
will be a significant advantage. If you have your own NVIDIA GPU, however, and wish to
use that, that’s fine – you’ll need to install the drivers for your GPU, install CUDA,
install cuDNN, and then install PyTorch. You could theoretically do the entire
assignment with no GPUs, though this will make training the model much slower.
Installing Python 3.8+: To use python3, make sure to install version 3.8+ on your
local machine.
Virtual environment: If you decide to work locally, we recommend using a virtual
environment via anaconda for the project. If you choose not to use a virtual
environment, it is up to you to make sure that all dependencies for the code are
installed globally on your machine. To set up a conda virtual environment, run the
following:
conda create -n qa_env python=3.8
conda activate qa_env
Install the PyTorch environment following the official instructions. Here we use
PyTorch 1.10.1 and CUDA 11.3. You may also switch to another version by specifying the
version number:
pip install torch==1.10.1+cu113 -f
https://download.pytorch.org/whl/torch_stable.html
Install pytorch-transformers, numpy and tqdm:
pip install pytorch-transformers==1.2.0, numpy, tqdm
3 Working on the assignment
3.1 Basis of pre-trained language models
Basic knowledge about the language model pre-training is necessary for completing the
task. Please refer to the related chapter (15.8) in Dive Into Deep Learning and the
blog written by Jay Alammar for a general introduction of pre-training techniques, how
the BERT models work, and etc.
If you are interested, please read the related papers (e.g., BERT and SpanBERT ) for
more details.
3.2 Task description
The datum of open-domain question answering is a triplet $(q, a, D)$, where $q,a$ are
question and answer respectively, and $D$ is the external document and context. Open
domain question answering aims to correctly answer $q$ by extracting $a$ from $D$.
Note here we assume that the question $q$ is always answerable and $a$ is always a
substring within $D$. Open-domain question answering has drawn increasing attention in
recent years because of its wide application in natural language processing and
vertical commerce setting.
In this assignment, you are going to fine-tune a pre-trained language model for the QA
task with the provided skeleton code. You will be provided with a standard dataset.
3.3 Get Code
The code structure is as follows:
└── src
├── dataset
│ ├── test.json # test data (which will be released later)
│ ├── train.json # train data
| └── valid.json # validation data
├── main.py # main file to train and evaluate the model
├── utils_squad.py # utillity data structures and funcions
├── utils_squad_evaluate.py # official SQuAD v2.0 evaluation script
└── README.md
Train
python main.py --train_file dataset/train.json --predict_file dataset/valid.json --
model_type bert --model_name_or_path bert-base-uncased --output_dir output/ --
version_2_with_negative --do_train --do_eval --do_lower_case --overwrite_output --
save_steps 0
Ps. Note that we are using SQuAD version 2.0, --version_2_with_negative argument is
necessary. Please find descriptions and functions of arguments in the script.
Evaluation
python main.py --train_file dataset/train.json --predict_file dataset/test.json --
model_type bert --model_name_or_path output/ --output_dir output/eval/ --
version_2_with_negative --do_eval --do_lower_case
Ps. The argument model_name_or_path here should be the ourput saving directory in
training to load saved checkpoint, and output_dir is the directory to save evaluation
results.
3.4 Assignment tasks
Task 1: Implement the self-attention function
Task 2: Implement the residual connection
Task 3: Implement the feed-forward layer
We have provided an almost-complete code for the BERT model. Please complete the code
in the places tagged with Write Your Code Here in modeling_bert.py.
Task 4: Implement the data preprocessing function
This function is responsible for converting the raw dataset into the required format
for training the model. You will need to understand the preprocessing steps needed for
the Machine Reading Comprehension task and use them to complete the code in the places
tagged with Write Your Code Here in utils_squad.py.
Task 5: Implement the training pipeline
Task 6: Implement the validation pipeline
We have provided an almost-complete code for fine-tuning and evaluating the BERT on
the QA task. Please complete the code in the places tagged with Write Your Code Here
in main.py to adapt BERT for our own interest.
Task 7: Predict the outputs of the test set
This task requires you to predict the outputs of the test set which will be released 7
days before the deadline. To accomplish this task, you will need to make some
modifications to the validation code. You are encouraged to use more advanced models
and adopt some practical tricks in hyper-parameter tuning to improve model
performance.
Task 8: Write a report (including the introduction, method, and experiment sections)
Your report should include three main sections: introduction, method, and experiment.
You are required to improve the baseline model with your own configuration. There are
lots of ways to improve the baseline model. Here are some suggestions for you.
Hyper-parameter tuning. There are lots of important hyper-parameters.
About optimizer: learning rate, batch size, warm-up and training epochs,
etc.
About embedding: Add input type embedding to indicate different parts of
inputs.
Different neural network architectures for predicting the span. You may choose
a more advanced neural network architecture and design your own customized
neural network for predicting the answer span.
Loss functions designs. You may add label smoothing or other tricks or come up
with a new loss function or training objectives.
Others. There are also many other possible techniques to explore. For example,
you can also design your own training strategies. Besides, you can also explore
some PLM fine-tuning skills in this project.
In the method section, describe the approaches you used to improve the baseline model
performance. In the experiment section, analyze the dataset statistics, qualitative
evaluations of the model's performance, and case analysis.
3.5 Files to submit (4 items in total)
1. Prepare a final report in PDF format (no more than 4 pages)
1.1 Introduction. Briefly introduce the task & background & related works.
1.2 Methods. Describe what you did to improve the baseline model performance.
For example, this may include but is not limited to: (i) Hyper-parameter
tuning, e.g. learning rate, batch size, and training epochs. (ii) Different
neural network architectures. (iii) Loss functions.
1.3 Experiments & Analysis (IMPORTANT) Analysis is the most important part of
the report. Possible analysis may include but is not limited to (i) Dataset
analysis (dataset statistics) (ii) Qualitative evaluations of your model.
Select several specific cases in the dataset and see if your model correctly
finds the answer. Failure case analysis is also suggested. (iii) Ablation
studies. Analyze why better performance can be achieved when you made some
modifications, e.g. hyper-parameters, model architectures, and loss functions.
The performance on the validation set should be given to validate your claim.
2. Codes
2.1 All the codes.
2.2 README.txt that describes which python file has been added/modified.
3. Models
3.1 Model checkpoint (i.e., pytorch_model.bin)
4. Generated results on the test set. Please submit the predicted answer for each
question in a single file (i.e., predictions_.json)
If your student id is 12345, then the file should be organized as follows:
12345.zip
|-report.pdf
|-BERTQA
| |-README.md
| |-your code
|-log
| |-pytorch_model.bin
|-results
| |-predictions_.json
3.6 When to submit?
The deadline is Apr 7 (Fri).
Late submission policy:
10% for late assignments submitted within 1 day late.
20% for late assignments submitted within 2 days late.
50% for late assignments submitted within 7 days late.
100% for late assignments submitted after 7 days late.
3.7 Need More Support?
For any questions about the assignment which potentially are common to all students,
your shall first look for related resources as follows,
We encourage you to use GitHub Issues of this repository.
Or if you prefer online doc: Discussion doc.
For any other private questions, please contact Li Chen ([email protected]) and
Xueliang Zhao ([email protected]) via email.
4 Marking Scheme:
Marks will be given based on the performance that you achieve on the test and the
submitted report file. TAs will perform an evaluation of the predicted answers.
The marking scheme has two parts, (1) the performance ranking based on F1 and EM (70%
marks) and (2) the final report (30% marks):
1. For the performance part (70%), the mark will be given based on the performance
(0.5 * EM + 0.5 * F1) of your model:
(1) 0.5 * EM + 0.5 * F1 larger than 76 will get the full mark of this part.
(2) 0.5 * EM + 0.5 * F1 between 72-76 will get 90% mark of this part.
(3) 0.5 * EM + 0.5 * F1 between 68-72 will get 80% mark of this part.
(4) 0.5 * EM + 0.5 * F1 between 64-68 will get 70% mark of this part.
(5) 0.5 * EM + 0.5 * F1 between 60-64 will get 60% mark of this part.
(6) 0.5 * EM + 0.5 * F1 larger than 10 will get 50% mark of this part.
(7) Others will get 0% mark.
2. For the final report part (30%): The marks will be given mainly based on the
richness of the experiments & analysis.
(1) Rich experiments + detailed analysis: 90%-100% mark of this part.
(2) Reasonable number of experiments + analysis: 70%-80% mark of this part.
(3) Basic analysis: 50%-60% mark of this part.
(4) Not sufficient analysis: lower than 50%.
案例分类
标签云
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COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 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