chapters/dl-representations_contextual.ipynb

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    "%%capture\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# %cd ..\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "import statnlpbook.util as util\n",
    "util.execute_notebook('language_models.ipynb')"
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   "source": [
    "from IPython.display import Image\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "# Contextualised Word Representations\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "## What makes a good word representation? ##\n",
    "\n",
    "1. Representations are **distinct**\n",
    "2. **Similar** words have **similar** representations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## Reminder: word2vec\n",
    "\n",
    "<center><img width=1000 src=\"../img/cbow_sg2.png\"></center>\n",
    "\n",
    "<div style=\"text-align: right;\">\n",
    "    (word2vec: <a href=\"https://arxiv.org/abs/1301.3781\">Mikolov et al., 2013</a>)\n",
    "</div>"
   ]
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   "source": [
    "### Disadvantage of Static Word Embeddings\n",
    "\n",
    "* No context (or maybe small fixed context window) - the representation depends only on the word itself\n",
    "\n",
    "How can we address this shortcoming?"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "## What does this mean? ##\n",
    "\n",
    "\n",
    "* \"Yesterday I saw a bass ...\""
   ]
  },
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   "source": [
    "# Contextualised Representations #\n",
    "\n",
    "* Static embeddings (e.g., [word2vec](dl-representations_simple.ipynb)) have one representation per word *type*, regardless of context\n",
    "\n",
    "* Contextualised representations use the context surrounding the word *token*\n"
   ]
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  {
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   "source": [
    "## Contextualised Representations Example ##\n",
    "\n",
    "\n",
    "* a) \"Yesterday I saw a bass swimming in the lake\""
   ]
  },
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   "source": [
    "* b) \"Yesterday I saw a bass in the music shop\""
   ]
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   "source": [
    "## Contextualised Representations Example ##\n",
    "\n",
    "\n",
    "* a) <span style=\"color:red\">\"Yesterday I saw a bass swimming in the lake\"</span>.\n",
    "* b) <span style=\"color:green\">\"Yesterday I saw a bass in the music shop\"</span>."
   ]
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   "source": [
    "## What makes a good representation? ##\n",
    "\n",
    "1. Representations are **distinct**\n",
    "2. **Similar** words have **similar** representations"
   ]
  },
  {
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   "source": [
    "Additional criterion:\n",
    "\n",
    "3. Representations take **context** into account"
   ]
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   "source": [
    "## How to train contextualised representations ##\n",
    "\n",
    "Basicallly like word2vec: predict a word from its context (or vice versa).\n",
    "\n",
    "Cannot just use lookup table (i.e., embedding matrix) any more.\n",
    "\n",
    "Train a network with the sequence as input! Does this remind you of anything?"
   ]
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       "<img src=\"../img/elmo_1.png?0.4888163857481026\" width=\"800\"/>"
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   "source": [
    "The hidden state of an RNN LM is a contextualised word representation!\n",
    "\n",
    "For the LM to use the hidden state to predict the next word, it should be a generally good sequence representation.\n",
    "\n",
    "In this example: a two-layer LSTM LM taken from a model called *ELMo*.\n",
    "\n",
    "<div style=\"text-align: right;\">\n",
    "    (from <a href=\"http://jalammar.github.io/illustrated-bert/\">The Illustrated BERT</a>)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## Bidirectional RNN LM ##\n",
    "\n",
    "An RNN (or LSTM) LM only considers preceding context.\n",
    "\n",
    "ELMo (Embeddings from Language Models) is based on a biLM: *bidirectional language model* ([Peters et al., 2018](https://www.aclweb.org/anthology/N18-1202/))."
   ]
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   "source": [
    "<center><img src=\"../img/quiz_time.png\"></center>\n",
    "\n",
    "# [ucph.page.link/bilm](https://ucph.page.link/bilm)\n",
    "([Responses](https://docs.google.com/forms/d/1BimPo-S12XWt1qOJLXBTIGjRpt-bVW8H7hmT3j0iRRQ/edit#responses))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## Problem: Long-Term Dependencies ##\n",
    "\n",
    "LSTMs have *longer-term* memory, but they still forget.\n",
    "\n",
    "Solution: *transformers*! ([Vaswani et al. (2017)](https://arxiv.org/abs/1706.03762))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "* In 2024, all state-of-the-art LMs are transformers.\n",
    "    * Yes, also GPT-4\n",
    "    * But some [RNN-inspired models](https://github.com/state-spaces/mamba) are in fact on the rise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## OpenAI GPT (Generative Pre-trained Transformer)\n",
    "\n",
    "Series of *decoder-only* neural language models using the *transformer* architecture.\n",
    "\n",
    "As contextualised representations, can be accessed using the Embeddings API: https://platform.openai.com/docs/api-reference/embeddings\n",
    "\n",
    "As a language model, can be accessed using the chat completions API: https://platform.openai.com/docs/api-reference/chat\n",
    "\n",
    "See more in the Transformers lecture.\n",
    "\n",
    "<center>\n",
    "    <img src=\"http://jalammar.github.io/images/xlnet/transformer-decoder-intro.png\" width=70%/>\n",
    "</center>\n",
    "\n",
    "\n",
    "<div style=\"text-align: right;\">\n",
    "    (from <a href=\"http://jalammar.github.io/illustrated-gpt2/\">The Illustrated GPT-2</a>)\n",
    "</div>"
   ]
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       "<img src=\"../img/transformers.png?0.20799997509039903\" width=\"400\"/>"
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    "Image(url='../img/transformers.png'+'?'+str(random.random()), width=400)"
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  {
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   "metadata": {
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   },
   "source": [
    "## BERT\n",
    "\n",
    "**B**idirectional **E**ncoder **R**epresentations from **T**ransformers ([Devlin et al., 2019](https://www.aclweb.org/anthology/N19-1423.pdf)), an *encoder-only* transformer.\n",
    "\n",
    "<center>\n",
    "    <img src=\"https://miro.medium.com/max/300/0*2XpE-VjhhLGkFDYg.jpg\" width=40%/>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "subslide"
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   },
   "source": [
    "### BERT training objective (1): **masked** language modelling (MLM)\n",
    "\n",
    "Predict *masked* words given context on both sides:\n",
    "\n",
    "<center>\n",
    "    <img src=\"http://jalammar.github.io/images/BERT-language-modeling-masked-lm.png\" width=70%/>\n",
    "</center>\n",
    "\n",
    "<div style=\"text-align: right;\">\n",
    "    (from <a href=\"http://jalammar.github.io/illustrated-bert/\">The Illustrated BERT</a>)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### BERT Training objective (2): next sentence prediction (NSP)\n",
    "\n",
    "Classify whether one sentence follows another using *conditional encoding* of both sentences:\n",
    "\n",
    "<center>\n",
    "    <img src=\"http://jalammar.github.io/images/bert-next-sentence-prediction.png\" width=70%/>\n",
    "</center>\n",
    "\n",
    "<div style=\"text-align: right;\">\n",
    "    (from <a href=\"http://jalammar.github.io/illustrated-bert/\">The Illustrated BERT</a>)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
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   "source": [
    "### How is that different from ELMo and GPT?\n",
    "\n",
    "<center>\n",
    "    <img src=\"mt_figures/bert_gpt_elmo.png\" width=100%/>\n",
    "</center>\n",
    "\n",
    "<div style=\"text-align: right;\">\n",
    "    (from <a href=\"https://www.aclweb.org/anthology/N19-1423.pdf\">Devlin et al., 2019</a>)\n",
    "</div>\n",
    "\n",
    "See more in the Attention lecture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## T5 (Text-to-Text Transfer Transformer)\n",
    "\n",
    "An *encoder-decoder* model.\n",
    "\n",
    "See more in the Transfer Learning lecture.\n",
    "\n",
    "<center>\n",
    "    <img src=\"https://1.bp.blogspot.com/-89OY3FjN0N0/XlQl4PEYGsI/AAAAAAAAFW4/knj8HFuo48cUFlwCHuU5feQ7yxfsewcAwCLcBGAsYHQ/s640/image2.png\" width=50%>\n",
    "</center>\n",
    "\n",
    "<div style=\"text-align: right;\">\n",
    "    (from <a href=\"https://arxiv.org/abs/1910.10683\">Raffel et al., 2019</a>)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### BERT tokenisation: not words, but WordPieces\n",
    "\n",
    "WordPiece and BPE (byte-pair encoding) tokenise text to **subwords** ([Sennrich et al., 2016](https://aclanthology.org/P16-1162/), [Wu et al., 2016](https://arxiv.org/abs/1609.08144v2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "fragment"
    }
   },
   "source": [
    "* BERT has a [30,000 WordPiece vocabulary](https://huggingface.co/bert-base-cased/blob/main/vocab.txt), including ~10,000 unique characters.\n",
    "* No unknown words!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "<center>\n",
    "    <img src=\"https://vamvas.ch/assets/bert-for-ner/tokenizer.png\" width=60%/>\n",
    "</center>\n",
    "\n",
    "<div style=\"text-align: right;\">\n",
    "    (from <a href=\"https://vamvas.ch/bert-for-ner\">BERT for NER</a>)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Visualizing BERT word embeddings\n",
    "\n",
    "Pretty similar to [word2vec](dl-representations_simple.ipynb):\n",
    "\n",
    "<center>\n",
    "    <img src=\"https://home.ttic.edu/~kgimpel/viz-bert/viz-bert-voc.png\" width=70%/>\n",
    "</center>\n",
    "\n",
    "<div style=\"text-align: right;\">\n",
    "    (from <a href=\"https://home.ttic.edu/~kgimpel/viz-bert/viz-bert.html\">Visualizing BERT</a>)\n",
    "</div>"
   ]
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   "source": [
    "### Visualizing BERT word embeddings\n",
    "\n",
    "<center>\n",
    "    <img src=\"https://home.ttic.edu/~kgimpel/viz-bert/viz-bert-voc-house.png\" width=70%/>\n",
    "</center>\n",
    "\n",
    "<div style=\"text-align: right;\">\n",
    "    (from <a href=\"https://home.ttic.edu/~kgimpel/viz-bert/viz-bert.html\">Visualizing BERT</a>)\n",
    "</div>"
   ]
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  {
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   "source": [
    "### Visualizing BERT word embeddings\n",
    "\n",
    "<center>\n",
    "    <img src=\"https://home.ttic.edu/~kgimpel/viz-bert/viz-bert-voc-suffixes.png\" width=70%/>\n",
    "</center>\n",
    "\n",
    "<div style=\"text-align: right;\">\n",
    "    (from <a href=\"https://home.ttic.edu/~kgimpel/viz-bert/viz-bert.html\">Visualizing BERT</a>)\n",
    "</div>"
   ]
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   "source": [
    "# Summary #\n",
    "\n",
    "* Static word embeddings do not differ depending on context\n",
    "* Contextualised representations are dynamic\n",
    "* Popular pre-trained contextual representations:\n",
    "    * ELMo: bidirectional language model with LSTMs\n",
    "    * GPT: transformer language models (decoder-only)\n",
    "    * BERT: transformer masked language model (encoder-only)\n",
    "    * T5: text-to-text transformer (encoder-decoder)"
   ]
  },
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   "source": [
    "# Additional Reading #\n",
    "\n",
    "+ [Jurafsky & Martin Chapter 7](https://web.stanford.edu/~jurafsky/slp3/7.pdf)\n",
    "+ [Jurafsky & Martin Chapter 8](https://web.stanford.edu/~jurafsky/slp3/8.pdf)"
   ]
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