Search Results for author:
Found 9 papers, 5 papers with code
RET-LLM: Towards a General Read-Write Memory for Large Language Models
Large language models (LLMs) have significantly advanced the field of natural language processing (NLP) through their extensive parameters and comprehensive data utilization.
Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages
The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i. e., making them better for about 100 languages.
Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging
An established method for training a POS tagger in such a scenario is to create a labeled training set by transferring from high-resource languages.
$Λ$-DARTS: Mitigating Performance Collapse by Harmonizing Operation Selection among Cells
The main shortcoming of DARTS is performance collapse, where the discovered architecture suffers from a pattern of declining quality during search.
Graph Neural Networks for Multiparallel Word Alignment
First, we create a multiparallel word alignment graph, joining all bilingual word alignment pairs in one graph.
Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages
Prior work on extracting MNE datasets from parallel corpora required resources such as large monolingual corpora or word aligners that are unavailable or perform poorly for underresourced languages.
Graph Algorithms for Multiparallel Word Alignment
With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently.
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus
With more than 7000 languages worldwide, multilingual natural language processing (NLP) is essential both from an academic and commercial perspective.
Deep Neural Networks for Query Expansion using Word Embeddings
In this paper, we show that this is also true for more recently proposed embedding-based query expansion methods.
