examples/politeness-strategies/Politeness_Strategies_in_MT-mediated_Communication.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Politeness strategies in MT-mediated communication\n",
    "\n",
    "In this notebook, we demo how to extract politeness strategies using ConvoKit's `PolitenessStrategies` module both in English and in Chinese. We will make use of this functionality to assess the degree to which politeness strategies are preserved in machine-translated texts.\n",
    "\n",
    "The politeness strategies considered are adapted from operationalizations in the following papers:\n",
    "\n",
    "- English: [A computational approach to politeness with application to social factors](https://www.cs.cornell.edu/~cristian/Politeness.html), [The politeness Package: Detecting Politeness in Natural Language](https://journal.r-project.org/archive/2018/RJ-2018-079/RJ-2018-079.pdf)\n",
    "\n",
    "- Chinese: [Studying Politeness across Cultures using English Twitter and Mandarin Weibo](https://dl.acm.org/doi/abs/10.1145/3415190)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from collections import defaultdict, Counter\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import pearsonr\n",
    "import spacy\n",
    "\n",
    "from convokit import Corpus, Speaker, Utterance, download\n",
    "from convokit import TextParser, PolitenessStrategies\n",
    "\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Preparing diagnostic test sets \n",
    "\n",
    "We sample utterances from Wikipedia Talkpages discussions in both English and Chinese. In particular, we use the medium-sized `wiki-corpus` shipped by ConvoKit as the source for sampling English utterances (as shown below), and we sampled a subset of utterances from [WikiConv](https://www.cs.cornell.edu/~cristian/index_files/wikiconv-conversation-corpus.pdf) (Chinese) as shared in [figshare](https://figshare.com/articles/dataset/WikiConv_-_Chinese/7376012). \n",
    "\n",
    "For those who would like to skip the preparatory steps and go straight to our analysis exploring how to assess the permeability of politeness signals in machine-translated communication ([Part 2 of this notebook](#2.-Computing-permeability-for-politeness-strategies)), we have made the sampled corpora directly downloadable via ConvoKit as `wiki-sampled-en-corpus` and `wiki-sampled-zh-corpus`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1. English data: `wiki-corpus`\n",
    "\n",
    "The medium-sized Wikipedia dataset is provided by ConvoKit as `wiki-corpus` ([documentation](https://convokit.cornell.edu/documentation/wiki.html)). Note that ConvoKit also offers a more complete collection of Wikipedia Talkpage discussions: [the Cornell Wikiconv Dataset](https://convokit.cornell.edu/documentation/wikiconv.html). We choose to use `wiki-corpus` as it is already sufficiently large for our purpose. \n",
    "\n",
    "To load the corpus, see options in the cell below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OPTION 1: DOWNLOAD CORPUS \n",
    "# UNCOMMENT THESE LINES TO DOWNLOAD CORPUS\n",
    "# DATA_DIR = '<YOUR DIRECTORY>'\n",
    "# WIKI_ROOT_DIR = download('wiki-corpus', data_dir=DATA_DIR)\n",
    "\n",
    "# OPTION 2: READ PREVIOUSLY-DOWNLOADED CORPUS FROM DISK\n",
    "# UNCOMMENT THIS LINE AND REPLACE WITH THE DIRECTORY WHERE THE WIKI-CORPUS IS LOCATED\n",
    "# WIKI_ROOT_DIR = '<YOUR DIRECTORY>'\n",
    "\n",
    "corpus = Corpus(filename=WIKI_ROOT_DIR)\n",
    "\n",
    "# load parses\n",
    "corpus.load_info('utterance',['parsed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Speakers: 38462\n",
      "Number of Utterances: 391294\n",
      "Number of Conversations: 125292\n"
     ]
    }
   ],
   "source": [
    "# Overall stats of the dataset \n",
    "corpus.print_summary_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extracting strategies for sampling\n",
    "\n",
    "In the case when the corpus is not dependency parsed, it will need to go through an additional step of parsing, which can be achieved via `TextParser`. See [this demo](https://github.com/CornellNLP/ConvoKit/blob/master/examples/politeness-strategies/politeness_demo.ipynb) for an example. As the `wiki-corpus` ships with pre-computed dependency parses (which we already loaded as you may notice), we can go straight to politeness strategy extraction. \n",
    "\n",
    "Here, we will focus on a set of local strategies, and hence specify that we will want to extract with strategy collection _politeness_local_. For other available options, refer to the [documentation](https://convokit.cornell.edu/documentation/politenessStrategies.html) for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/391294 utterances processed\n",
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      "380000/391294 utterances processed\n",
      "390000/391294 utterances processed\n"
     ]
    }
   ],
   "source": [
    "ps_local = PolitenessStrategies(strategy_collection=\"politeness_local\", verbose=10000)\n",
    "\n",
    "# By default, strategy extraction results are saved under \"politeness_strategies\". \n",
    "corpus = ps_local.transform(corpus, markers=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Computing strategy prevalance \n",
    "\n",
    "We can first take a glimpse over utterance-level strategy prevalence, i.e., proportion of utterances in the dataset that use the politeness strategy. This can be easily done using `summarize()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Actually        0.092892\n",
       "Adverb.Just     0.135192\n",
       "Affirmation     0.068373\n",
       "Apology         0.039078\n",
       "By.The.Way      0.015546\n",
       "Conj.Start      0.134996\n",
       "Filler          0.004140\n",
       "For.Me          0.011099\n",
       "For.You         0.011784\n",
       "Gratitude       0.202492\n",
       "Greeting        0.073242\n",
       "Hedges          0.198127\n",
       "Indicative      0.015620\n",
       "Please          0.046149\n",
       "Please.Start    0.041657\n",
       "Reassurance     0.016573\n",
       "Subjunctive     0.025725\n",
       "Swearing        0.004051\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prevalence = ps_local.summarize(corpus)\n",
    "df_prevalence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sampling \n",
    "\n",
    "To assess permeability of these strategies, we sample 1000 instances for each strategy. The results will be saved to a smaller `wiki-sampled-en` corpus, which may be directly downloaded via ConvoKit if one wants to skip the intermediate steps (which will take a while to run) see [Part 2 of this notebook](#2.-Computing-permeability-for-politeness-strategies). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# utterance-level strategy uses\n",
    "df_feat = pd.DataFrame.from_dict({utt.id: utt.meta['politeness_strategies'] \\\n",
    "                                  for utt in corpus.iter_utterances()}, orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sampling from least common to most\n",
    "sorted_strategies = df_prevalence.sort_values().index\n",
    "sampled_ids, samples = set(), []\n",
    "\n",
    "for k in sorted_strategies:\n",
    "    df_sample = df_feat[(~df_feat.index.isin(sampled_ids)) & (df_feat[k]==1)].sample(1000, random_state=42)\n",
    "    df_sample['strategy'] = k\n",
    "    samples.append(df_sample[['strategy']])\n",
    "    sampled_ids.update(df_sample.index)\n",
    "    \n",
    "df_en_sample = pd.concat(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving as a convokit corpus\n",
    "for i, info in df_en_sample.itertuples():\n",
    "    utt = corpus.get_utterance(i)\n",
    "    utt.add_meta('selected', True)\n",
    "    utt.add_meta('strategy', info)\n",
    "\n",
    "# filter only selected utterances \n",
    "# (not that this does not maintain conversation structure)\n",
    "wiki_sampled_en = corpus.filter_utterances_by(lambda utt:'selected' in utt.meta and utt.meta['selected'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Translating \n",
    "\n",
    "To determine the degree to which politeness markers are perserved in translation, we will make comparisons between original and translated texts. To set it up, we two rounds of translations, forming a English -> Chinese -> English loop, i.e., we first translate the English texts into Chinese, and then translate the Chinese translations back into English. \n",
    "\n",
    "We use [EasyNMT](https://github.com/UKPLab/EasyNMT) to perform translations between English and Chinese, using models from [Opus-MT](https://github.com/Helsinki-NLP/Opus-MT) from [Helsinki-NLP](https://blogs.helsinki.fi/language-technology/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from easynmt import EasyNMT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# texts to be translated\n",
    "df_utts = wiki_sampled_en.get_utterances_dataframe(exclude_meta=True)\n",
    "\n",
    "# translation model\n",
    "model = EasyNMT('opus-mt', cache_folder=\"/belafonte_sauna/liye_translations/easynmt/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18000/18000.0 [2:28:46<00:00,  2.02it/s]\n"
     ]
    }
   ],
   "source": [
    "df_utts['en-zh'] = model.translate(list(df_utts['text']), \\\n",
    "                                   target_lang='zh', \\\n",
    "                                   source_lang='en', \\\n",
    "                                   show_progress_bar=True,\n",
    "                                   batch_size=8, \\\n",
    "                                   perform_sentence_splitting=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18000/18000.0 [1:09:48<00:00,  4.30it/s]\n"
     ]
    }
   ],
   "source": [
    "df_utts['en-back'] = model.translate(list(df_utts['en-zh']), \\\n",
    "                                   target_lang='en', \\\n",
    "                                   source_lang='zh', \\\n",
    "                                   show_progress_bar=True,\n",
    "                                   batch_size=8, \\\n",
    "                                   perform_sentence_splitting=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add these translated texts as meta data to our sampled corpus, and parse them to prepare for later strategy extraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from convokit.text_processing.textParser import TextParser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in df_utts[['text', 'en-zh', 'en-back']].itertuples():\n",
    "    idx, trans, backtrans = row[0], row[2], row[3]\n",
    "    utt = wiki_sampled_en.get_utterance(idx)\n",
    "    utt.add_meta('en-zh', trans)\n",
    "    utt.add_meta('en-back', backtrans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parser to parse back-translated English texts \n",
    "en_parser = TextParser(output_field='en_parsed', input_field='en-back', \\\n",
    "                       verbosity=5000)\n",
    "\n",
    "# parer to parse translated texts in Chinese\n",
    "spacy_zh = spacy.load('zh_core_web_sm', disable=['ner'])\n",
    "zh_parser = TextParser(output_field='zh_parsed', input_field='en-zh', \\\n",
    "                       spacy_nlp=spacy_zh, verbosity=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000/18000 utterances processed\n",
      "10000/18000 utterances processed\n",
      "15000/18000 utterances processed\n",
      "18000/18000 utterances processed\n",
      "5000/18000 utterances processed\n",
      "10000/18000 utterances processed\n",
      "15000/18000 utterances processed\n",
      "18000/18000 utterances processed\n"
     ]
    }
   ],
   "source": [
    "wiki_sampled_en = en_parser.transform(wiki_sampled_en)\n",
    "wiki_sampled_en = zh_parser.transform(wiki_sampled_en)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can then save the corpus using wiki_sampled_en.dump(YOUR_OUT_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Chinese data: [WikiConv](https://www.cs.cornell.edu/~cristian/index_files/wikiconv-conversation-corpus.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the Chinese data, we start utterances from [WikiConv](https://figshare.com/articles/dataset/WikiConv_-_Chinese/7376012) and similarly sampled 1000 instances for a subset of strategies from the collection \"_politeness-cscw-zh_\". The corpus is saved as `wiki-sampled-zh-corpus`, with all textual data (i.e., both the original utterance texts and the the corresponding translations) tokenized and parsed.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset already exists at /belafonte_sauna/liye_translations/convokit_mt/test/wiki-sampled-en-corpus\n"
     ]
    }
   ],
   "source": [
    "wiki_sampled_zh = Corpus(download('wiki-sampled-zh-corpus'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'utterances-index': {'is-admin': [\"<class 'bool'>\"], 'parsed': [\"<class 'list'>\"], 'politeness_strategies': [\"<class 'dict'>\"], 'politeness_markers': [\"<class 'dict'>\"], 'selected': [\"<class 'bool'>\"], 'strategy': [\"<class 'str'>\"], 'en-zh': [\"<class 'str'>\"], 'en-back': [\"<class 'str'>\"], 'en_parsed': [\"<class 'list'>\"], 'zh_parsed': [\"<class 'list'>\"]}, 'speakers-index': {'is-admin': [\"<class 'bool'>\"], 'edit-count': [\"<class 'str'>\"]}, 'conversations-index': {}, 'overall-index': {}, 'version': 1, 'vectors': []}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the meta data avaible, should have the following:\n",
    "# 'parsed' contains the dependency parses for the utterance text \n",
    "# 'zh-en' and 'zh-back' contains the translations and back translations for utterance texts respectively \n",
    "# 'en_parsed' and 'zh_parsed' contain the respective parses, which we will use for strategy extractions\n",
    "wiki_sampled_zh.meta_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Computing permeability for politeness strategies "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the two sampled datasets tokenized and parsed, we are now ready to the degree to which strategies are perserved vs. lost in different translation directions. \n",
    "\n",
    "We make two types of comparisons:\n",
    "\n",
    "* First, we consider a direct comparison between the original vs. translated texts. In particular, we check strategies used in utterances in English texts and Chinese texts with respective politeness strategy operationalizations to make comparisons. \n",
    "\n",
    "* Second, we consider comparing the original vs. the backtranslated texts using the same strategy operationalization and compare strategies detected.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset already exists at /belafonte_sauna/liye_translations/convokit_mt/test/wiki-sampled-en-corpus\n",
      "Dataset already exists at /belafonte_sauna/liye_translations/convokit_mt/test/wiki-sampled-zh-corpus\n"
     ]
    }
   ],
   "source": [
    "# Download the data if Part 1 of the notebook is skipped \n",
    "\n",
    "# replace with where you'd like the corpora to be saved \n",
    "DATA_DIR = '/belafonte_sauna/liye_translations/convokit_mt/test/'\n",
    "\n",
    "wiki_sampled_en = Corpus(download('wiki-sampled-en-corpus', data_dir=DATA_DIR))\n",
    "wiki_sampled_zh = Corpus(download('wiki-sampled-zh-corpus', data_dir=DATA_DIR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Speakers: 7290\n",
      "Number of Utterances: 18000\n",
      "Number of Conversations: 16583\n"
     ]
    }
   ],
   "source": [
    "wiki_sampled_en.print_summary_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Speakers: 2933\n",
      "Number of Utterances: 12000\n",
      "Number of Conversations: 1\n"
     ]
    }
   ],
   "source": [
    "wiki_sampled_zh.print_summary_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting strategies \n",
    "\n",
    "As a first step, we extract strategies for all translations and back-translations. We will need two politeness strategy transformers: \n",
    "\n",
    "* for texts in English, we will again use the strategy collection _politeness_local_ \n",
    "* for texts in Chinese, we will be using the strategy collection _politeness-cscw-zh_.\n",
    "\n",
    "More details of different politeness strategy collections can be found at the [documentation page]( https://convokit.cornell.edu/documentation/politenessStrategies.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps_zh = PolitenessStrategies(parse_attribute_name='zh_parsed', \\\n",
    "                             strategy_attribute_name=\"zh_strategies\", \\\n",
    "                             strategy_collection=\"politeness_cscw_zh\", \n",
    "                             verbose=5000)\n",
    "\n",
    "ps_en = PolitenessStrategies(parse_attribute_name='en_parsed', \\\n",
    "                             strategy_attribute_name=\"en_strategies\", \\\n",
    "                             strategy_collection=\"politeness_local\",\n",
    "                             verbose=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000/18000 utterances processed\n",
      "10000/18000 utterances processed\n",
      "15000/18000 utterances processed\n",
      "5000/18000 utterances processed\n",
      "10000/18000 utterances processed\n",
      "15000/18000 utterances processed\n"
     ]
    }
   ],
   "source": [
    "# extracting for English samples\n",
    "wiki_sampled_en = ps_zh.transform(wiki_sampled_en)\n",
    "wiki_sampled_en = ps_en.transform(wiki_sampled_en)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000/12000 utterances processed\n",
      "10000/12000 utterances processed\n",
      "5000/12000 utterances processed\n",
      "10000/12000 utterances processed\n"
     ]
    }
   ],
   "source": [
    "# extracting for Chinese samples\n",
    "wiki_sampled_zh = ps_zh.transform(wiki_sampled_zh)\n",
    "wiki_sampled_zh = ps_en.transform(wiki_sampled_zh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making comparisons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We consider permeability of a politeness strategy _s_ as the percentage utterances in a given collection containing such markers for which the translated version also contains (potentially different) markers from the same set.\n",
    " \n",
    "As mentioned earlier, we estimate permeability both with translations and backtranslations. Note that each approach has its own limitations, and thus both of them are at best _proxies_ for strategy permeability and should be not read as the groundtruth values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping between strategy names in different collections \n",
    "# Note that the collections are not exactly equivalent, \n",
    "# i.e., there are strategies we can't find a close match between the two collections\n",
    "\n",
    "en2zh = {'Actually': 'factuality',\n",
    "         'Adverb.Just': None,\n",
    "         'Affirmation': 'praise',\n",
    "         'Apology': 'apologetic',\n",
    "         'By.The.Way': 'indirect_btw',\n",
    "         'Conj.Start': 'start_so',\n",
    "         'Filler': None,\n",
    "         'For.Me': None,\n",
    "         'For.You': None,\n",
    "         'Gratitude': 'gratitude',\n",
    "         'Greeting':'greeting',\n",
    "         'Hedges':'hedge',\n",
    "         'Indicative':'can_you',\n",
    "         'Please': 'please',\n",
    "         'Please.Start': 'start_please',\n",
    "         'Reassurance': None,\n",
    "         'Subjunctive': 'could_you',\n",
    "         'Swearing': 'taboo'\n",
    "       }\n",
    "\n",
    "zh2en = {v:k for k,v in en2zh.items() if v}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add utterance-level assessing result to utterance metadata for the English corpus\n",
    "for utt in wiki_sampled_en.iter_utterances():\n",
    "    \n",
    "    # strategy names in English and Chinese \n",
    "    en_name = utt.retrieve_meta('strategy')\n",
    "    zh_name = en2zh[en_name]\n",
    "    \n",
    "    # translations\n",
    "    if zh_name:\n",
    "        trans_status = utt.retrieve_meta('zh_strategies')[zh_name]\n",
    "        utt.add_meta('translation_result', trans_status)\n",
    "    else:\n",
    "        # when a comparison isn't applicable, we use the value -1 \n",
    "        utt.add_meta('translation_result', -1)\n",
    "    \n",
    "    # back translations \n",
    "    backtrans_status = utt.retrieve_meta('en_strategies')[en_name]\n",
    "    utt.add_meta('backtranslation_result', backtrans_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add utterance-level assessing result to utterance metadata for the Chinese corpus\n",
    "for utt in wiki_sampled_zh.iter_utterances():\n",
    "    \n",
    "    # strategy names in English and Chinese \n",
    "    zh_name = utt.retrieve_meta('strategy')\n",
    "    en_name = zh2en[zh_name]\n",
    "    \n",
    "    # translations\n",
    "    if en_name:\n",
    "        trans_status = utt.retrieve_meta('en_strategies')[en_name]\n",
    "        utt.add_meta('translation_result', trans_status)\n",
    "    \n",
    "    # back translations \n",
    "    backtrans_status = utt.retrieve_meta('zh_strategies')[zh_name]\n",
    "    utt.add_meta('backtranslation_result', backtrans_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then export these utterance-level assessing results to pandas DataFrames (via `get_attribute_table`) for easy aggregation and plotting. The utterance metadata we need are:\n",
    "\n",
    "    * strategy: the strategy to be checked for the utterance \n",
    "    * translation_result: whether the checked strategy remains in the translated text\n",
    "    * backtranslation_result: whether the checked strategy remains in the back-translated text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A. English -> Chinese"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results for the English corpus \n",
    "res_df_en = wiki_sampled_en.get_attribute_table(obj_type='utterance', \\\n",
    "                                                attrs=['strategy', \\\n",
    "                                                       'translation_result', \\\n",
    "                                                       'backtranslation_result'])\n",
    "\n",
    "res_df_en.columns = ['strategy', 'en->zh', 'en->zh->en']\n",
    "\n",
    "# strategy-level permeability, -1 means the strategy is not applicable \n",
    "permeability_df_en = res_df_en.groupby('strategy').sum() / 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As a reference, we include permeability computed through an informal small-scale human annotations \n",
    "# (50 instances, one annotator)\n",
    "reference = {'Actually': 0.7, 'Adverb.Just': 0.62, 'Affirmation': 0.8, 'Apology': 0.94, 'By.The.Way': 0.42,\n",
    "             'Conj.Start': 0.66, 'Filler': 0.58, 'For.Me': 0.62, 'For.You': 0.52, 'Gratitude': 0.86,\n",
    "             'Greeting': 0.52, 'Hedges': 0.68, 'Indicative': 0.64, 'Please': 0.72, 'Please.Start': 0.82,\n",
    "             'Reassurance': 0.88, 'Subjunctive': 0.0, 'Swearing': 0.3}\n",
    "\n",
    "permeability_df_en['reference'] = [reference[name] for name in permeability_df_en.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As further context, we can inlcude information about strategy prevalence on our plot\n",
    "prevalence_en = dict(df_prevalence*100)\n",
    "permeability_df_en.index = [f\"{name} ({prevalence_en[name]:.1f}%)\" for name in permeability_df_en.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 12))\n",
    "sns.set(font_scale=1.2)\n",
    "\n",
    "# cells that are not applicable are masked in white\n",
    "with sns.axes_style(\"white\"):\n",
    "    sns.heatmap(permeability_df_en, annot=True, cmap=\"Greens\", fmt=\".1%\", mask=permeability_df_en==-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### B. Chinese -> English"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results for the English corpus \n",
    "res_df_zh = wiki_sampled_zh.get_attribute_table(obj_type='utterance', \\\n",
    "                                                attrs=['strategy', \\\n",
    "                                                       'translation_result', \\\n",
    "                                                       'backtranslation_result'])\n",
    "\n",
    "# convert names to make it easier to compare between directions \n",
    "res_df_zh['strategy'] = res_df_zh['strategy'].apply(lambda name:zh2en[name])\n",
    "\n",
    "res_df_zh.columns = ['strategy', 'zh->en', 'zh->en->zh'] \n",
    "permeability_df_zh = res_df_zh.groupby('strategy').sum() / 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# as the original dataset for the Chinese corpus is quite large\n",
    "# we present strategy prevalence results directly\n",
    "\n",
    "prevalence_zh = {'apologetic': 0.6, 'can_you': 0.3, 'could_you': 0.0, \n",
    "                 'factuality': 0.4,'gratitude': 3.1, 'greeting': 0.0, \n",
    "                 'hedge': 42.8, 'indirect_btw': 0.1,\n",
    "                 'praise': 0.4, 'please': 25.4, \n",
    "                 'start_please': 17.7, 'start_so': 0.7, 'taboo': 0.4}\n",
    "\n",
    "permeability_df_zh.index = [f\"{name} ({prevalence_zh[en2zh[name]]:.1f}%)\" for name in permeability_df_zh.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 9))\n",
    "sns.set(font_scale=1.2)\n",
    "with sns.axes_style(\"white\"):\n",
    "    sns.heatmap(permeability_df_zh, annot=True, cmap=\"Blues\", fmt=\".1%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}