Search Results for author:
Found 10 papers, 4 papers with code
Don’t Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings
The two approaches we find most effective are: 1) using identical words as seed lexicons (which unsupervised approaches incorrectly assume are not available for orthographically distinct language pairs) and 2) combining such lexicons with pairs extracted by matching romanized versions of words with an edit distance threshold.
Multilingual Word Embeddings for Low-Resource Languages using Anchors and a Chain of Related Languages
In this paper, we propose to build multilingual word embeddings (MWEs) via a novel language chain-based approach, that incorporates intermediate related languages to bridge the gap between the distant source and target.
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.
SilverAlign: MT-Based Silver Data Algorithm For Evaluating Word Alignment
Word alignments are essential for a variety of NLP tasks.
Don't Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings
The two approaches we find most effective are: 1) using identical words as seed lexicons (which unsupervised approaches incorrectly assume are not available for orthographically distinct language pairs) and 2) combining such lexicons with pairs extracted by matching romanized versions of words with an edit distance threshold.
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.
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing
Simultaneously, the transformer model, especially its combination with transfer learning, has been proven to be a powerful technique for natural language processing tasks.
Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction
In this paper, we enrich BWE-based BDI with transliteration information by using Bilingual Orthography Embeddings (BOEs).
LMU Bilingual Dictionary Induction System with Word Surface Similarity Scores for BUCC 2020
We participate in both the open and closed tracks of the shared task and we show improved results of our method compared to simple vector similarity based approaches.
