33 citations · 64 across the 22 of their papers we have counts for
24 papers
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects
Daniel Deutsch, Eleftheria Briakou, Isaac Caswell +14
As large language models (LLM) become more and more capable in languages other than English, it is important to collect benchmark datasets in order to evaluate their multilingual p…
ECLeKTic: a Novel Challenge Set for Evaluation of Cross-Lingual Knowledge Transfer
Omer Goldman, Uri Shaham, Dan Malkin +11
To achieve equitable performance across languages, large language models (LLMs) must be able to abstract knowledge beyond the language in which it was learnt. However, the current…
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet +1
We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We id…
CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models
Zaid Sheikh, Antonios Anastasopoulos, Shruti Rijhwani +3
Effectively using Natural Language Processing (NLP) tools in under-resourced languages requires a thorough understanding of the language itself, familiarity with the latest models…
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
Sebastian Ruder, Jonathan H. Clark, Alexander Gutkin +24
Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) -- languages for which NLP re-search is part…
SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation
Elizabeth Clark, Shruti Rijhwani, Sebastian Gehrmann +7
Reliable automatic evaluation of summarization systems is challenging due to the multifaceted and subjective nature of the task. This is especially the case for languages other tha…
User-Centric Evaluation of OCR Systems for Kwak'wala
Shruti Rijhwani, Daisy Rosenblum, Michayla King +2
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 langua…
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder +42
African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability o…
Lexically Aware Semi-Supervised Learning for OCR Post-Correction
Shruti Rijhwani, Daisy Rosenblum, Antonios Anastasopoulos +1
Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents. Optical character recognition (OCR) can be used to produce…
Dependency Induction Through the Lens of Visual Perception
Ruisi Su, Shruti Rijhwani, Hao Zhu +4
Most previous work on grammar induction focuses on learning phrasal or dependency structure purely from text. However, because the signal provided by text alone is limited, recentl…
Evaluating the Morphosyntactic Well-formedness of Generated Texts
Adithya Pratapa, Antonios Anastasopoulos, Shruti Rijhwani +4
Text generation systems are ubiquitous in natural language processing applications. However, evaluation of these systems remains a challenge, especially in multilingual settings. I…
MasakhaNER: Named Entity Recognition for African Languages
David Ifeoluwa Adelani, Jade Abbott, Graham Neubig +58
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 en…
OCR Post Correction for Endangered Language Texts
Shruti Rijhwani, Antonios Anastasopoulos, Graham Neubig
There is little to no data available to build natural language processing models for most endangered languages. However, textual data in these languages often exists in formats tha…
Soft Gazetteers for Low-Resource Named Entity Recognition
Shruti Rijhwani, Shuyan Zhou, Graham Neubig +1
Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-cr…
Practical Comparable Data Collection for Low-Resource Languages via Images
Aman Madaan, Shruti Rijhwani, Antonios Anastasopoulos +2
We propose a method of curating high-quality comparable training data for low-resource languages with monolingual annotators. Our method involves using a carefully selected set of…
A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization
Graham Neubig, Shruti Rijhwani, Alexis Palmer +21
Despite recent advances in natural language processing and other language technology, the application of such technology to language documentation and conservation has been limited…
AlloVera: A Multilingual Allophone Database
David R. Mortensen, Xinjian Li, Patrick Littell +6
We introduce a new resource, AlloVera, which provides mappings from 218 allophones to phonemes for 14 languages. Phonemes are contrastive phonological units, and allophones are the…
Towards Zero-resource Cross-lingual Entity Linking
Shuyan Zhou, Shruti Rijhwani, Graham Neubig
Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia. XEL is challenging for most languages because o…
Choosing Transfer Languages for Cross-Lingual Learning
Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee +10
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…
The ARIEL-CMU Systems for LoReHLT18
Aditi Chaudhary, Siddharth Dalmia, Junjie Hu +27
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 a…
Zero-shot Neural Transfer for Cross-lingual Entity Linking
Shruti Rijhwani, Jiateng Xie, Graham Neubig +1
Cross-lingual entity linking maps an entity mention in a source language to its corresponding entry in a structured knowledge base that is in a different (target) language. While p…
Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models
Abhilasha Ravichander, Shruti Rijhwani, Rajat Kulshreshtha +3
Hierarchical models are utilized in a wide variety of problems which are characterized by task hierarchies, where predictions on smaller subtasks are useful for trying to predict a…
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations
Paul Michel, Abhilasha Ravichander, Shruti Rijhwani
We investigate the pertinence of methods from algebraic topology for text data analysis. These methods enable the development of mathematically-principled isometric-invariant mappi…