Search Results for author:
Found 9 papers, 4 papers with code
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations
In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations.
Cross-Lingual Information Retrieval
Cross-Lingual Word Embeddings
+3
DART: Open-Domain Structured Data Record to Text Generation
Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures.
FeTaQA: Free-form Table Question Answering
Existing table question answering datasets contain abundant factual questions that primarily evaluate the query and schema comprehension capability of a system, but they fail to include questions that require complex reasoning and integration of information due to the constraint of the associated short-form answers.
Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review
Electronic health records (EHRs), digital collections of patient healthcare events and observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and research.
IsoVec: Controlling the Relative Isomorphism of Word Embedding Spaces
The ability to extract high-quality translation dictionaries from monolingual word embedding spaces depends critically on the geometric similarity of the spaces -- their degree of "isomorphism."
Multilingual Pixel Representations for Translation and Effective Cross-lingual Transfer
We introduce and demonstrate how to effectively train multilingual machine translation models with pixel representations.
Exploring Representational Disparities Between Multilingual and Bilingual Translation Models
Multilingual machine translation has proven immensely useful for both parameter efficiency and overall performance across many language pairs via complete multilingual parameter sharing.
Merging Text Transformer Models from Different Initializations
Recent work on one-shot permutation-based model merging has shown impressive low- or zero-barrier mode connectivity between models from completely different initializations.
Strategies for Adapting Multilingual Pre-training for Domain-Specific Machine Translation
Therefore, in this work, we propose two major fine-tuning strategies: our language-first approach first learns the translation language pair via general bitext, followed by the domain via in-domain bitext, and our domain-first approach first learns the domain via multilingual in-domain bitext, followed by the language pair via language pair-specific in-domain bitext.
