Multilingual Identification of English Code-Switching

AnE-LID (Any-English Code-Switching Language Identification) is a token-level model for detecting English code-switching in multilingual texts. It classifies words into four classes: English, notEnglish, Mixed, and Other. The model shows strong performance on both languages seen and unseen in the training data.

Usage

You can use AnE-LID with Huggingface’s pipeline or AutoModelForTokenClassification.

Let's try the following example (taken from this paper)

input = "ich glaub ich muss echt rewatchen like i feel so empty was soll ich denn jetzt machen?"

Pipeline

from transformers import pipeline
classifier = pipeline("token-classification", model="igorsterner/AnE-LID", aggregation_strategy="simple")
result = classifier(input)

which returns

[{'entity_group': 'notEnglish',
  'score': 0.9999998,
  'word': 'ich glaub ich muss echt',
  'start': 0,
  'end': 23},
 {'entity_group': 'Mixed',
  'score': 0.9999941,
  'word': 'rewatchen',
  'start': 24,
  'end': 33},
 {'entity_group': 'English',
  'score': 0.99999154,
  'word': 'like i feel so empty',
  'start': 34,
  'end': 54},
 {'entity_group': 'notEnglish',
  'score': 0.9292571,
  'word': 'was soll ich denn jetzt machen?',
  'start': 55,
  'end': 86}]

Advanced

If your input is already word-tokenized, and you want the corresponding word language labels, you can try the following strategy

import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer

lid_model_name = "igorsterner/AnE-LID"
lid_tokenizer = AutoTokenizer.from_pretrained(lid_model_name)
lid_model = AutoModelForTokenClassification.from_pretrained(lid_model_name)

word_tokens = ['ich', 'glaub', 'ich', 'muss', 'echt', 'rewatchen', 'like', 'i', 'feel', 'so', 'empty', 'was', 'soll', 'ich', 'denn', 'jetzt', 'machen', '?']

subword_inputs = lid_tokenizer(
    word_tokens, truncation=True, is_split_into_words=True, return_tensors="pt"
)

subword2word = subword_inputs.word_ids(batch_index=0)
logits = lid_model(**subword_inputs).logits
predictions = torch.argmax(logits, dim=2)

predicted_subword_labels = [lid_model.config.id2label[t.item()] for t in predictions[0]]
predicted_word_labels = [[] for _ in range(len(word_tokens))]

for idx, predicted_subword in enumerate(predicted_subword_labels):
    if subword2word[idx] is not None:
        predicted_word_labels[subword2word[idx]].append(predicted_subword)

def most_frequent(lst):
    return max(set(lst), key=lst.count) if lst else "Other"

predicted_word_labels = [most_frequent(sublist) for sublist in predicted_word_labels]

for token, label in zip(word_tokens, predicted_word_labels):
    print(f"{token}: {label}")

which returns

ich: notEnglish
glaub: notEnglish
ich: notEnglish
muss: notEnglish
echt: notEnglish
rewatchen: Mixed
like: English
i: English
feel: English
so: English
empty: English
was: notEnglish
soll: notEnglish
ich: notEnglish
denn: notEnglish
jetzt: notEnglish
machen: notEnglish
?: Other

Named entities

If you also want to tag named entities, you can also run AnE-NER. Checkout my evaluation scripts for examples on using both at the same time, as we did in the paper: https://github.com/igorsterner/AnE/tree/main/eval.

Citation

Please consider citing my work if it helped you

@inproceedings{sterner-2024-multilingual,
    title = "Multilingual Identification of {E}nglish Code-Switching",
    author = "Sterner, Igor",
    editor = {Scherrer, Yves  and
      Jauhiainen, Tommi  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Zampieri, Marcos  and
      Nakov, Preslav  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.vardial-1.14",
    doi = "10.18653/v1/2024.vardial-1.14",
    pages = "163--173",
    abstract = "Code-switching research depends on fine-grained language identification. In this work, we study existing corpora used to train token-level language identification systems. We aggregate these corpora with a consistent labelling scheme and train a system to identify English code-switching in multilingual text. We show that the system identifies code-switching in unseen language pairs with absolute measure 2.3-4.6{\%} better than language-pair-specific SoTA. We also analyse the correlation between typological similarity of the languages and difficulty in recognizing code-switching.",
}