Cross-Lingual Natural Language Inference
17 papers with code • 1 benchmarks • 2 datasets
Using data and models available for one language for which ample such resources are available (e.g., English) to solve a natural language inference task in another, commonly more low-resource, language.
Libraries
Use these libraries to find Cross-Lingual Natural Language Inference models and implementations
XNLI
XGLUE
Most implemented papers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
google-research/bert
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NAACL 2019
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
facebookresearch/InferSent
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EMNLP 2017
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
facebookresearch/LASER
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TACL 2019
We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts.
XNLI: Evaluating Cross-lingual Sentence Representations
facebookresearch/XLM
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EMNLP 2018
State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models.
ByT5: Towards a token-free future with pre-trained byte-to-byte models
Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units.
Better Fine-Tuning by Reducing Representational Collapse
Although widely adopted, existing approaches for fine-tuning pre-trained language models have been shown to be unstable across hyper-parameter settings, motivating recent work on trust region methods.
Rethinking embedding coupling in pre-trained language models
PaddlePaddle/PaddleNLP
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ICLR 2021
We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models.
Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings
yeraidm/meemi
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While monolingual word embeddings encode information about words in the context of a particular language, cross-lingual embeddings define a multilingual space where word embeddings from two or more languages are integrated together.
Language Embeddings for Typology and Cross-lingual Transfer Learning
DianDYu/language_embeddings
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ACL 2021
Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data.
PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining
machelreid/paradise
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NAACL 2022
Despite the success of multilingual sequence-to-sequence pretraining, most existing approaches rely on monolingual corpora, and do not make use of the strong cross-lingual signal contained in parallel data.
