Adaptive Bilingual Alignment System with Multilingual Sentence Embedding

Core Concepts

AIlign achieves state-of-the-art results in bi-textual alignment using multilingual embeddings.

Abstract

The AIlign system introduces an adaptive bitextual alignment approach utilizing multilingual sentence embeddings to identify alignable areas efficiently. By extracting anchor points and employing dynamic programming methods, AIlign demonstrates equivalent results to the state of the art systems like Vecalign or Bertalign, even for texts with non-monotonic properties. The system's two-stage architecture optimizes alignment paths by reducing algorithmic complexity and improving performance for challenging texts. Experimental results across various datasets showcase AIlign's effectiveness and quasi-linear complexity compared to existing systems.

Stats

AIlign achieves F-measure of 98.5% on a Chinese-French literary corpus.
Execution time: Text+Berg - 119s (AIlign) vs. 590s (Bertalign).
Execution time: MD.ar-en - 2166s (AIlign) vs. 8114s (Bertalign).
Execution time: BAF - 1437s (AIlign) vs. 10882s (Bertalign).

Quotes

"A new state of the art has been reached on this task, thanks to multilingual transformers."
"AIlign's results are very close to Bertalign's ones, representing the state-of-the-art system."
"The execution time of Ailign is significantly shorter than that of Bertalign."

Key Insights Distilled From

Adaptative Bilingual Aligning Using Multilingual Sentence Embedding

by Olivier Krai... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11921.pdf

Deeper Inquiries

How can the concept of adaptive alignment be applied beyond bi-textual scenarios

Adaptive alignment, as demonstrated in the context of bi-textual scenarios, can be applied beyond this specific domain to various other fields where aligning data or entities is crucial. For instance, in the field of cross-lingual information retrieval (CLIR), adaptive alignment techniques could be utilized to match queries in one language with relevant documents in another language. By leveraging multilingual sentence embeddings and dynamic programming algorithms similar to those used in bi-textual alignment, CLIR systems can adaptively align queries and documents across languages more effectively.

What potential limitations or biases could arise from relying solely on sentence embeddings for alignment

Relying solely on sentence embeddings for alignment may introduce certain limitations and biases. One potential limitation is the inability to capture nuanced semantic relationships that go beyond the surface-level similarities encoded in embeddings. This could lead to inaccuracies in aligning texts with complex or abstract content where contextual understanding is essential. Additionally, biases present in the training data used for generating sentence embeddings may propagate into the alignment process, potentially affecting the quality and fairness of alignments produced by such systems.

How might advancements in bi-textual alignment impact other fields like machine translation or cross-lingual information retrieval

Advancements in bi-textual alignment have significant implications for fields like machine translation and cross-lingual information retrieval (CILR). In machine translation, improved alignment techniques can enhance the accuracy of parallel corpora alignments used as input for neural machine translation models. This leads to better translations by reducing errors caused by misalignments between source and target sentences. Similarly, advancements in bi-textual alignment can benefit CILR systems by enabling more precise matching between multilingual documents or datasets, facilitating efficient cross-lingual search and knowledge discovery processes across different languages.