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.
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This dataset, named FarsTail, includes 10, 367 samples which are provided in both the Persian language as well as the indexed format to be useful for non-Persian researchers.
Ranked #1 on Natural Language Inference on FarsTail
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.
Ranked #1 on Abstractive Text Summarization on CNN / Daily Mail
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.
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.
CROSS-LINGUAL BITEXT MINING CROSS-LINGUAL DOCUMENT CLASSIFICATION CROSS-LINGUAL NATURAL LANGUAGE INFERENCE DOCUMENT CLASSIFICATION JOINT MULTILINGUAL SENTENCE REPRESENTATIONS PARALLEL CORPUS MINING ZERO-SHOT CROSS-LINGUAL TRANSFER
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Ranked #1 on Question Answering on CoQA
COMMON SENSE REASONING CONVERSATIONAL RESPONSE SELECTION CROSS-LINGUAL NATURAL LANGUAGE INFERENCE NAMED ENTITY RECOGNITION NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING SENTENCE CLASSIFICATION SENTIMENT ANALYSIS
State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models.
Ranked #3 on Natural Language Inference on XNLI French
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.
Ranked #1 on Semantic Textual Similarity on SentEval