HTREC 2022


A baseline based on language modelling and rules


This baseline used a simple rule, to replace " ς " (final s) with a proper correction. Visualised the training data, trained a statistical language model to pick the correction and changed only texts that comprise the error.  

LMing-Rules baseline

  • Using language modeling.
  • Using rules, extracted from the training data.

Sign in

  • To get the data.
In [ ]:
!pip install aicrowd-cli
%load_ext aicrowd.magic
In [2]:
%aicrowd login
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In [3]:
!rm -rf data
!mkdir data
%aicrowd ds dl -c htrec-2022 -o data
In [ ]:
!pip install pywer
import pywer
import pandas as pd
import numpy as np
import os
In [12]:
train = pd.read_csv("data/train.csv")
test = pd.read_csv("data/test.csv")
print(f"{train.shape[0]} train and {test.shape[0]} instances"); train.sample()
1875 train and 338 instances
246 οιονει και την υλην την κοσμουσαν και αυτην διόν εικαὶ τὴν χην την κοσμουσαν και αυτη 11 9 Bodleian-Library-MS-Barocci-102_00165_fol-79... 8


Using the human-transcribed data to train a statistical character language model.

In [15]:
!git clone https://github.com/ipavlopoulos/lm.git
from lm.markov.models import LM
lm = LM(gram="CHAR").train(train.HUMAN_TRANSCRIPTION.sum()); #cslm.generate_text()
'ἐγγινομενου σρς ἐχθρούς. Εἰ πάντας φιλοπαίγμονα των αὐτῷ, ὡς τῶν λόγῳἔστ᾽, ἐπεὶ νεκρόν· οὐκ ἴσχυσαν ἅπαντός ὀνόματος και αλλωπισμου πόδακαί ὑπό τόν Θεόνὄντα δυναστενόν δέ μοι το αιδοτριβενταυθα μὲν περιεργοῖς, οὓς ἔκτειν᾽ ἀπεικαὶ τύχας σριαςυποθεν ην παρ᾽ οὐδὲν τῶν φράσονται. Τουτέστι, προσεται, καὶ ὑμεῖς πῶς λέβητα ὑποκαιόμενον τό δέξασθαι δύ᾽ ἢ τῆς ἐργάσεται εφοβουαφαιρεσεως πότεπρουθέμην βασιλειαν εμε μιμουμένῃδεδωκοτας ἕλκει· εἶτ᾽ ἀνὰ λειμῶναΧορεύσω και σάρκα κατὰ τοῦ παλαιαὶ Πριαμιδῶν και ουκ ανποτε πέλαβερᾶς και εναντιας του γλωττηςΒακχε,νυκτερηθήσοντα ποιησεν Ἰλιάδ᾽ ἄν τι;δεινόν μ᾽ ἀπειλε Σαοὺλ, καὶ τοῦ ΝαυῆἸησοῦς ἔνεστανττω και προς και παντας ἐλάβοι πλησας ὧν σε ἔκβαλεῖς κόρησεμνον, καθως εἰς τόν σ᾽ ἀνθεμώδεις, ἤνεγκαναὐτὸς ὑμὶν ἐξ Αἰγύπτου και το ποιος και εκαστον αὐτοῖσι τραποίμην προσθοῦ. Εὐφραίνειν ει γαρ ετι τα ιματια υμωντου φανες ησπαζοντες ολην αυτάὁ Ξανθίαν, βοήθησομένην, καὶ σὺ, Κε.Οὐδὲν μακάριος Δαβὶδ, πρόσθε ον φίλταται· ἐγὼπάλαινα μαθηται· ἐγὼμόνος ἄκανθανάτῳ οὐκ '

Frequently mistaken tokens

In [13]:
LEX = train.HUMAN_TRANSCRIPTION.sum().split()
In [18]:
from collections import Counter
broken_words = [w for w in train.SYSTEM_TRANSCRIPTION.sum().split() if w not in set(LEX)]
x,y = zip(*Counter(broken_words).most_common(10))
pd.DataFrame({"mistaken":x, "occurrences":y}).plot.barh(x="mistaken");

Picking one, one that can be fixed without much ambiguity.

In [19]:
train[train.SYSTEM_TRANSCRIPTION.str.contains(" ς ")].sample(2)
595 μηρ δ᾽ ἐν οἴκοις· ἣν σὺ μὴ δείσῃς ποθ᾽ ὡς ξυ ρομι ς ὡς υτῦ δήσεινταρχας γ 16 44 Bodleian-Library-MS-Barocci-66_00318_fol-15... 7
1156 ως· ὡς αὐτοῦ ὄντος τοῦ Χῦ και ῥήμα ως ω ς αὐτοῦ ον πος τοῦ χυ καιρήε, 15 77 Bodleian-Library-MS-Barocci-59_00082_fol-45... 7

The method

Exploring the mistakes, two are the easier fixes: merge with the previous word (i.e., this is a final character, which makes sense) or delete. To pick out of the two, we ask the LM.

In [22]:
def lmr(text, word=" ς ", replacements=["ς ", " "], lm=lm):
    scores = []
    for the_candidate in replacements:
        scores.append(lm.cross_entropy(text.replace(word, the_candidate)))
    text_out = text.replace(word, replacements[scores.index(min(scores))])
    return text_out
lmr("ως ω ς αὐτοῦ ον πος τοῦ χυ καιρήε,")
'ως ως αὐτοῦ ον πος τοῦ χυ καιρήε,'

Predict and submit

In [23]:
R1 = test.SYSTEM_TRANSCRIPTION.apply(lmr)
In [27]:
submission = pd.DataFrame(zip(test.IMAGE_PATH, R1), columns=["ImageID", "Transcriptions"])
ImageID Transcriptions
2 66 Bodleian-Library-MS-Barocci-127_00136_fol-66v συ σ κατεχισωμε ἐπιτελωτι
In [25]:
submission.to_csv("submission.csv", index=False)
In [26]:
%aicrowd submission create -c htrec-2022 -f submission.csv

                                    │ Successfully submitted! │                                     
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{'submission_id': 187737, 'created_at': '2022-06-06T11:41:37.986Z'}
In [ ]:


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