Parallel Text Alignment and Monolingual Parallel Corpus Creation from Philosophical Texts for Text Simplification (2021.naacl-srw)
Copied to clipboard
| Challenge: | Existing methods for text simplification require a lot of annotated data, however there are few suitable tools for this task. |
| Approach: | They propose an unsupervised method for aligning text based on Doc2Vec embeddings and an alignment algorithm capable of aligning texts at different levels. |
| Outcome: | The proposed method can be used to create a monolingual parallel corpus composed of the works of early modern philosophers and their corresponding simplified versions. |
Similar Papers
CATS: A Tool for Customized Alignment of Text Simplification Corpora (L18-1)
Copied to clipboard
| Challenge: | Existing corpora of original sentences and their manual simplifications are very scarce and small in size, hindering automated text simplification systems. |
| Approach: | They propose a language-independent tool for sentence alignment from parallel/comparable TS resources. |
| Outcome: | The proposed tool performs well on English and Spanish corpora and compares sentences based on their semantic overlap. |
DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification (2023.acl-long)
Copied to clipboard
| Challenge: | Current text simplification research mostly focuses on English and on sentencelevel simplification. |
| Approach: | They propose to use a dataset of parallel, professionally written and manually aligned simplifications in plain German "plain DE" and "Einfache Sprache" they build a web harvester and experiment with automatic alignment methods to facilitate integration of non-aligned and to be-published parallel documents. |
| Outcome: | The proposed dataset of parallel, professionally written and manually aligned simplifications in plain German is extended to 750 document pairs and 3.5k sentence pairs. |
Neural CRF Model for Sentence Alignment in Text Simplification (2020.acl-main)
Copied to clipboard
| Challenge: | Text simplification systems are based on the quality and quantity of complex-simple sentence pairs extracted by aligning sentences between parallel articles. |
| Approach: | They propose a neural CRF alignment model which leverages the sequential nature of sentences in parallel documents and utilizes a sentence pair model to capture semantic similarity. |
| Outcome: | The proposed model outperforms previous work on monolingual sentence alignment task by more than 5 points in F1. |
SimAlign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Word alignments are useful for statistical and neural machine translation (NMT) and cross-lingual annotation projection. |
| Approach: | They propose to leverage multilingual word embeddings for word alignment. |
| Outcome: | The proposed methods perform better for four languages and comparable for two languages than traditional statistical aligners even with abundant parallel data. |
Effectively Aligning and Filtering Parallel Corpora under Sparse Data Conditions (2020.acl-srw)
Copied to clipboard
| Challenge: | Parallel corpora are key to developing good machine translation systems, but abundant parallel data is hard to come by for languages with a low number of speakers. |
| Approach: | They propose an unsupervised alignment method that can handle rich morphology by removing incorrect translations and segments containing extraneous data. |
| Outcome: | The proposed method maximizes the number of correctly translated segments in a corpus and minimises noise by removing incorrect translations and segments containing extraneous data. |
Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
Copied to clipboard
| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
| Outcome: | The proposed methods can be applied to encoder models and encoder-decoder-only models . they show that language-neutral and language-specific information is key . |
Word Alignment by Fine-tuning Embeddings on Parallel Corpora (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing work on word alignment has focused on unsupervised learning on parallel text. |
| Approach: | They propose to combine pre-trained contextualized word embeddings with multilingually trained language models to achieve competitive results on word alignment tasks. |
| Outcome: | The proposed model outperforms state-of-the-art models on five language pairs and can train multilingual word aligners that can obtain robust performance on different language pairs. |
AlignFix: A Tool for Parallel Corpora Augmentation and Refinement (2026.eacl-demo)
Copied to clipboard
| Challenge: | High-quality datasets are crucial for training effective state of the art machine translation systems, but they can be noisy and degrade performance. |
| Approach: | They propose an open-source tool for augmenting data, identifying and correcting errors in parallel corpora. |
| Outcome: | The tool extracts consistent phrase pairs, enabling targeted replacements that can improve the dataset quality. |
An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to build parallel sentence simplification corpora are limited . SS is used to rephrase sentences into simpler forms for those with cognitive disabilities . |
| Approach: | They propose to build SS corpora from large-scale bilingual translation corpors using a parallel approach. |
| Outcome: | The proposed method outperforms the existing methods on WikiLarge and achieves state-of-the-art results. |
Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation (2020.lrec-1)
Copied to clipboard
| Challenge: | Summarizing text is not a straightforward task. |
| Approach: | They propose to use automated transcriptions to generate reports from automatic transcriptions as a dataset for neural summarization. |
| Outcome: | The proposed model improves on publicmeetings corpus on a dataset of aligned public meetings. |