Scalable Cross-Lingual Transfer of Neural Sentence Embeddings
This work addresses scalable cross-lingual transfer for NLP applications, but it is incremental as it builds on existing embedding models and alignment methods.
The paper tackled cross-lingual alignment of neural sentence embeddings by evaluating three frameworks, finding that representation transfer outperforms joint models, especially with limited parallel data.
We develop and investigate several cross-lingual alignment approaches for neural sentence embedding models, such as the supervised inference classifier, InferSent, and sequential encoder-decoder models. We evaluate three alignment frameworks applied to these models: joint modeling, representation transfer learning, and sentence mapping, using parallel text to guide the alignment. Our results support representation transfer as a scalable approach for modular cross-lingual alignment of neural sentence embeddings, where we observe better performance compared to joint models in intrinsic and extrinsic evaluations, particularly with smaller sets of parallel data.