Resolving Gendered Pronouns
With the goal to learning PyTorch and getting more hands-on experience with transfer learning via pre-trained language models, I took part in the Gendered Pronoun Resolution Competition on Kaggle. The learning alone was quite worth it. And I placed 30th solo out of 800+ teams with limited time invested.
I entered the Gendered Pronoun Resolution Competition on Kaggle with two goals in mind:
- Learn PyTorch: PyTorch has been giving Tensorflow a run for its run since its 1.0 launch. With many popular packages like Hugging Face, fast.ai, allennlp built on top of it, PyTorch is becoming the standard in Machine Learning research.
- Get experience with Language Model Finetuning: transfer learning with pre-trained langauge models like BERT has been very popular in the NLP community and I haven’t had much experience using them in a project.
I used the edge probing model architecture from this ICLR 2019 paper. I experimented with OpenAI GPT, ELMo, BERT base and large as the pre-trained encoders.
- Input Tokens: the respective tokenizer of the pre-trained language model were used
- Span Representation: extracting span representation withallennlp.modules.span_extractors. I experimented with both EndpointSpanExtractor and SelfAttentiveSpanExtractor but there doesn’t seem to be much of a difference.
- MLP: very simple 1-layer MLP from the size of the concatenated span representation to the number of classes(3). A dropout of 0.1 is applied, as suggest from the BERT paper.
Results & Analysis
In order to properly benchmark the performance of the fine-tuned pre-trained language models, I built two baseline models without transfer learning.
- Baseline with random initialization: Replace the pre-trained encoder in the edge probing model with a word embedding and a Bidirectional LSTM. The word embedding is initialized randomly and trainable
- Baseline with Glove: Same model as the Baseline with random initialization, except using Glove to initialize the word embeddings
As expected, the performance improves as we add transfer learning or increase the size and complexity of the pre-trained encoder. I was very surprised by the strong performance of the BERT models. Even the BERT base models significantly outperform ELMo and GPT.
My final submission was a ensemble of BERT large models fine-tuned on the GAP dataset. It scores around 0.33 on the public test set and 0.26 on the private test set.
Learning From Top Solutions
Things that I should have considered:
- Truncation: most of the top solutions involved truncating the text to a certain length. I didn’t do it since I was worried that a mention will get truncated. This normally wouldn’t matter but it did for this competition due to the size of BERT large. Without truncation, I was only able to run a batch size of 2 when fine-tuning BERT large on my 1080-Ti, which was very unstable and 20% of the models failed to learn.
- Augmentation: one of the challenges of the competition was the small size of the training data. 7th place solution involved data augmentation by replacing the A and B names with a sets of placeholder names. This gave an improvement of 0.02.
- Intermediate layers of BERT: A few solutions mentioned mentioned using intermediate layers. The 3rd place solution used -5th and -6th layer of BERT and the 11th place solution concatenates last 8 layers of BERT. The reasoning behind this is that the last layer of BERT is too close to the masked LM objective and intermediate layers might offer better generalizations. I actually have a parameter in my model configuration that specified which layer to fine-tune with but I didn’t find a big difference when I experimented with -1 and -2.
- BertAdam Optimizer: I didn’t experiment with the BertAdam optimizer that comes with the pre-trained BERT package in PyTorch and used vanilla Adam instead. The biggest challenge I faced for this competition was the unstable training due to small batch size and some solution mentioned that the warm-up portion of the BertAdam helped stabilizing the training.