Papers with language classification
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages (2020.acl-main)
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| Challenge: | a recent trend in neural NLP has been the introduction of feature-based and fine-tuning methods . we train monolingual contextualized word embeddings for five mid-resource languages . |
| Approach: | They use common Crawl corpus to train monolingual contextualized word embeddings . they compare performance of OSCAR-based and Wikipedia-based embeddables on part-of-speech tasks . |
| Outcome: | The results show that OSCAR-based and Wikipedia-based embeddings perform better than Wikipedia-style embedders on part-of-speech tagging and parsing tasks. |
A little goes a long way: Improving toxic language classification despite data scarcity (2020.findings-emnlp)
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| Challenge: | Existing methods for toxic language classification have not been thoroughly explored. |
| Approach: | They propose to use data augmentation to generate new synthetic data from labeled seed datasets to improve toxic language classification. |
| Outcome: | The proposed techniques perform well on very scarce toxic language datasets while performing worse on shallower models. |
Search Query Language Identification Using Weak Labeling (2020.lrec-1)
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| Challenge: | a recent study has shown that language identification is a well-known task for natural language documents. |
| Approach: | They propose a search query language identification task that trains large-scale query-language pairs for training without loss of generalization. |
| Outcome: | The proposed model outperforms open domain model baselines by a large margin. |
FigMemes: A Dataset for Figurative Language Identification in Politically-Opinionated Memes (2022.emnlp-main)
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| Challenge: | FigMemes is a dataset for figurative language classification in politically-opinionated memes. |
| Approach: | They propose to use figurative language classification to identify politically-opinionated memes by analyzing their datasets and comparing them to other machine learning models. |
| Outcome: | The proposed dataset includes annotations of six commonly used types of figurative language in politically-opinionated memes and a wide range of topics and visual styles. |
From Bytes to Subwords: Challenges of Input Representations in NLP (2026.findings-acl)
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| Challenge: | Traditionally, characters or words have been used, but recently, subwords have become the standard. |
| Approach: | They examine the current use of tokenizers and examine the weaknesses of character normalization . they propose proof of concept alternatives focused on fairness and efficiency . |
| Outcome: | The proposed model is based on a systematic review of current tokenizers and character encodings. |
Figurative Language Processing: A Linguistically Informed Feature Analysis of the Behavior of Language Models and Humans (2023.findings-acl)
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| Challenge: | Recent years have witnessed a growing interest in investigating what Transformer-based language models (TLMs) actually learn from training data. |
| Approach: | They propose to use a black-box TLM and two intrinsically transparent white-box models to investigate the performance of figurative language models on sarcasm, similes, idioms, and metaphors. |
| Outcome: | The proposed models perform better than other models on figurative language classification tasks. |