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Understanding BERT
Natural Language Processing

Understanding BERT

Author(s): Shweta Baranwal

Source: Photo by Min An on Pexels

BERT (Bidirectional Encoder Representations from Transformers) is a research paper published by Google AI language. Unlike previous versions of NLP architectures, BERT is conceptually simple and empirically powerful. It obtains a new state of the art results on 11 NLP tasks.

BERT has a benefit over another standard LM because it applies deep bidirectional context training of the sequence meaning it considers both left and right context while training whereas other LM model such as OpenAI GPT is unidirectional, every token can only attend to previous tokens in attention layers. Such restrictions are suboptimal for sentence-level tasks (paraphrasing) or token level tasks (named entity recognition, question-answering), where it is crucial to incorporate context from both directions.

What is BERT?

In earlier versions of LM, such as Glove, we have fixed embeddings of the words. For example, for the word “right,” the embedding is the same irrespective of its context in the sentence. Does it mean “correct” or “right direction”? Then came ELMo (bi-directional LSTM), it tried to solve this problem by using the left and right context for generating embedding, but it simply concatenated the left-to-right and right-to-left information, meaning that the representation couldn’t take advantage of both left and right contexts simultaneously. Then BERT, with its attention layers, outperformed all the previous models.

Differences in pre-training model architectures. BERT uses a bidirectional Transformer. OpenAI GPT uses a left-to-right Transformer. ELMo uses the concatenation of independently trained left-to-right and right-to-left LSTMs to generate features for downstream tasks. Among the three, only BERT representations are jointly conditioned on both left and right contexts in all layers.

The basic architecture of the Transformer, a popular attention model, has two major components: Encoder and Decoder. The encoder part reads the input sequence and processes it, and the Decoder part takes the processed input from Encoder and re-process it to perform the prediction task. To understand more about the transformer, refer: here. Since here we are interested in generating the Language Model (LM), only the Encoder part is necessary. BERT uses this transformer encoder architecture to generate bi-directional self-attention for the input sequence. It reads the entire sentence in one go, and attention layers learn the context of a word from all of its left and right surrounding words.

Pre-training BERT:

The pre-training of the BERT is done on an unlabeled dataset and therefore is un-supervised in nature. There are two pre-training steps in BERT:

  1. Masked Language Model (MLM)

a) Model masks 15% of the tokens at random with [MASK] token and then predict those masked tokens at the output layer. Loss is based only on the prediction of masked tokens, not on all tokens’ prediction.

b) During fine-tuning of the model [MASK] token does not appear, creating a mismatch. In order to mitigate this, if the i-th token is chosen for masking during pre-training, it is replaced with:

80% times [MASK] token: My dog is hairy → My dog is [MASK]

10% times Random word from the corpus: My dog is hairy → My dog is apple

10% times Unchanged: My dog is hairy → My dog is hairy

http://jalammar.github.io/illustrated-bert/

2. Next Sentence Prediction

a) In this pre-training approach, given the two sentences A and B, the model trains on binarized output whether the sentences are related or not.

b) While choosing the sentence A and B for pre-training examples, 50% of the time B is the actual next sentence that follows A (label: IsNext), and 50% of the time it is a random sentence from the corpus (label: NotNext).

http://jalammar.github.io/illustrated-bert/

The training loss is the sum of the mean masked LM likelihood and the mean next sentence prediction likelihood.

In prior works of NLP, only sentence embeddings are transferred to downstream tasks, whereas BERT transfers all parameters of pre-training to initialize models for different downstream tasks. The pre-trained BERT models are made available by Google and can be used directly for the fine-tuning downstream tasks.

https://huggingface.co/transformers/pretrained_models.html

BERT Architecture:

BERT’s model architecture is a multilayer bi-directional Transformer encoder based on Google’s Attention is all you need paper. It comes in two model forms:

BERT BASE: less transformer blocks and hidden layers size, have the same model size as OpenAI GPT. [12 Transformer blocks, 12 Attention heads, 768 hidden layer size]

BERT LARGE: huge network with twice the attention layers as BERT BASE, achieves a state of the art results on NLP tasks. [24 Transformer blocks, 16 Attention heads, 1024 hidden layer size]

During fine-tuning of the model, parameters of these layers (Transformer blocks, Attention heads, hidden layers) along with additional layers of the downstream task are fine-tuned end-to-end.

BERT Input Representations:

  1. The first token of every sequence is always a special classification token [CLS]. The final hidden state corresponding to this token is used for the classification task.
  2. The two sentences are separated using the [SEP] token.
  3. In the case of sentence pair, a segment embedding is added, which indicates whether the token belongs to sentence A or sentence B.
  4. For a given token, its input representation is constructed by adding the corresponding token, segment, and position embedding.
BERT input representation. The input embeddings are the sum of the token embeddings, the segmentation embedding, and the position embeddings.

Fine-tuning BERT:

Fine-tuning BERT is simple and straightforward. The model is modified as per the task in-hand. For each task, we simply plug in the task-specific inputs and outputs into BERT and fine-tune all the parameters end-to-end.

At the input, sentence A and sentence B from pre-training are analogous to

  1. sentence pairs in paraphrasing
  2. hypothesis-premise pairs in entailment
  3. question-passage pairs in question answering
  4. a degenerate text-∅ pair in text classification or sequence tagging.

At the output, the token representations are fed into an output layer for token level tasks, such as sequence tagging or question answering, and the [CLS] representation is fed into an output layer for classification, such as sentiment analysis.

http://jalammar.github.io/illustrated-bert/

HuggingFace has provided a framework for fine-tuning task-specific models as well.

https://huggingface.co/transformers/model_doc/bert.html#bertforpretraining

Model framework for MaskedLM, NextSentence Prediction, Sequence Classification, Multiple Choice, etc. are readily available along with pre-training parameters for BERT. These are simple and fun to implement.

BERT Tokenizer:

https://huggingface.co/transformers/model_doc/bert.html#berttokenizer

In the BERT input representations, we have seen there are three types of embeddings we need (token, segment, position). The Transformers package by HuggingFace constructs the tokens for each of the embedding requirements (encode_plus). Here both pre-trained tokenizer, as well as tokenizer from a given vocab file, can be used.

BERT tokenizer from pre-trained ‘bert-base-uncased’

BERT tokenizer uses WordPiece Model for tokenization. It breaks the words into sub-words to increase the coverage of vocabulary.

Word: Jet makers feud over seat width with big orders at stake

Wordpieces: _J et _makers _fe ud _over _seat _width _with _big _orders _at _stake

In the above example, the word “Jet” is broken into two wordpieces “_J” and “et”, and the word “feud” is broken into two wordpieces “_fe” and “ud”. The other words remain as single wordpieces. “_” is a special character added to mark the beginning of a word.

ShwetaBaranwal/BERT

References:


Understanding BERT was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Published via Towards AI

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