Search Results for author:
Found 25 papers, 12 papers with code
SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation
In this work, we introduce SEAHORSE, a dataset for multilingual, multifaceted summarization evaluation.
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
We evaluate commonly used models on the benchmark.
User-Centric Evaluation of OCR Systems for Kwak'wala
There has been recent interest in improving optical character recognition (OCR) for endangered languages, particularly because a large number of documents and books in these languages are not in machine-readable formats.
Optical Character Recognition
Optical Character Recognition (OCR)
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
African languages are spoken by over a billion people, but are underrepresented in NLP research and development.
Lexically Aware Semi-Supervised Learning for OCR Post-Correction
In addition, to enforce consistency in the recognized vocabulary, we introduce a lexically-aware decoding method that augments the neural post-correction model with a count-based language model constructed from the recognized texts, implemented using weighted finite-state automata (WFSA) for efficient and effective decoding.
Dependency Induction Through the Lens of Visual Perception
Our experiments find that concreteness is a strong indicator for learning dependency grammars, improving the direct attachment score (DAS) by over 50\% as compared to state-of-the-art models trained on pure text.
Evaluating the Morphosyntactic Well-formedness of Generated Texts
Text generation systems are ubiquitous in natural language processing applications.
MasakhaNER: Named Entity Recognition for African Languages
We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders.
OCR Post Correction for Endangered Language Texts
There is little to no data available to build natural language processing models for most endangered languages.
Temporally-Informed Analysis of Named Entity Recognition
Natural language processing models often have to make predictions on text data that evolves over time as a result of changes in language use or the information described in the text.
Soft Gazetteers for Low-Resource Named Entity Recognition
However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages.
A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization
Despite recent advances in natural language processing and other language technology, the application of such technology to language documentation and conservation has been limited.
Practical Comparable Data Collection for Low-Resource Languages via Images
We propose a method of curating high-quality comparable training data for low-resource languages with monolingual annotators.
AlloVera: A Multilingual Allophone Database
While phonemic representations are language specific, phonetic representations (stated in terms of (allo)phones) are much closer to a universal (language-independent) transcription.
Towards Zero-resource Cross-lingual Entity Linking
Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia.
Choosing Transfer Languages for Cross-Lingual Learning
Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages.
The ARIEL-CMU Systems for LoReHLT18
This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).
Zero-shot Neural Transfer for Cross-lingual Entity Linking
To address this problem, we investigate zero-shot cross-lingual entity linking, in which we assume no bilingual lexical resources are available in the source low-resource language.
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations
We investigate the pertinence of methods from algebraic topology for text data analysis.
Code-Switching as a Social Act: The Case of Arabic Wikipedia Talk Pages
Code-switching has been found to have social motivations in addition to syntactic constraints.
Estimating Code-Switching on Twitter with a Novel Generalized Word-Level Language Detection Technique
Word-level language detection is necessary for analyzing code-switched text, where multiple languages could be mixed within a sentence.
Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models
In this work, we focus on improving learning for such hierarchical models and demonstrate our method on the task of speaker trait prediction.
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations
We investigate the pertinence of methods from algebraic topology for text data analysis.
