895 citations · 2.1k across the 169 of their papers we have counts for
18 papers · 1 filter
A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities
Haotian Ye, Axel Wisiorek, Antonis Maronikolakis +2
Hate speech online remains an understudied issue for marginalized communities, particularly in the Global South, which includes developing societies with increasing internet penetr…
EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models
Shaoxiong Ji, Zihao Li, Jaakko Paavola +7
In this work, we introduce EMMA-500, a large-scale multilingual language model continue-trained on texts across 546 languages designed for enhanced multilingual performance, focusi…
ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Language
Yongkang Liu, Feng Shi, Daling Wang +2
Although large language models(LLMs) show amazing capabilities, among various exciting applications discovered for LLMs fall short in other low-resource languages. Besides, most ex…
Exploring the Role of Transliteration in In-Context Learning for Low-resource Languages Written in Non-Latin Scripts
Chunlan Ma, Yihong Liu, Haotian Ye +1
Decoder-only large language models (LLMs) excel in high-resource languages across various tasks through few-shot or even zero-shot in-context learning (ICL). However, their perform…
A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation
Yongkang Liu, Ercong Nie, Shi Feng +5
Current state-of-the-art dialogue systems heavily rely on extensive training datasets. However, challenges arise in domains where domain-specific training datasets are insufficient…
Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training Alignment
Orgest Xhelili, Yihong Liu, Hinrich Schütze
Multilingual pre-trained models (mPLMs) have shown impressive performance on cross-lingual transfer tasks. However, the transfer performance is often hindered when a low-resource t…
BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning
Ercong Nie, Bo Shao, Zifeng Ding +3
This paper introduces BMIKE-53, a comprehensive benchmark for cross-lingual in-context knowledge editing (IKE) across 53 languages, unifying three knowledge editing (KE) datasets:…
Joint Lemmatization and Morphological Tagging with LEMMING
Thomas Muller, Ryan Cotterell, Alexander Fraser +1
We present LEMMING, a modular log-linear model that jointly models lemmatization and tagging and supports the integration of arbitrary global features. It is trainable on corpora a…
TransMI: A Framework to Create Strong Baselines from Multilingual Pretrained Language Models for Transliterated Data
Yihong Liu, Chunlan Ma, Haotian Ye +1
Transliterating related languages that use different scripts into a common script is effective for improving crosslingual transfer in downstream tasks. However, this methodology of…
XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples
Peiqin Lin, André F. T. Martins, Hinrich Schütze
Recent studies indicate that leveraging off-the-shelf or fine-tuned retrievers, capable of retrieving relevant in-context examples tailored to the input query, enhances few-shot in…
Labeled Morphological Segmentation with Semi-Markov Models
Ryan Cotterell, Thomas Müller, Alexander Fraser +1
We present labeled morphological segmentation, an alternative view of morphological processing that unifies several tasks. From an annotation standpoint, we additionally introduce…
Rehearsal-Free Modular and Compositional Continual Learning for Language Models
Mingyang Wang, Heike Adel, Lukas Lange +2
Continual learning aims at incrementally acquiring new knowledge while not forgetting existing knowledge. To overcome catastrophic forgetting, methods are either rehearsal-based, i…
GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network
Shuzhou Yuan, Ercong Nie, Michael Färber +2
Large Language Models (LLMs) exhibit strong In-Context Learning (ICL) capabilities when prompts with demonstrations are used. However, fine-tuning still remains crucial to further…
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks
Bolei Ma, Ercong Nie, Shuzhou Yuan +4
Prompt-based methods have been successfully applied to multilingual pretrained language models for zero-shot cross-lingual understanding. However, most previous studies primarily f…
HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy
Yongkang Liu, Yiqun Zhang, Qian Li +5
Full-parameter fine-tuning has become the go-to choice for adapting language models (LMs) to downstream tasks due to its excellent performance. As LMs grow in size, fine-tuning the…
MaLA-500: Massive Language Adaptation of Large Language Models
Peiqin Lin, Shaoxiong Ji, Jörg Tiedemann +2
Large language models (LLMs) have advanced the state of the art in natural language processing. However, their predominant design for English or a limited set of languages creates…
TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language Models
Yihong Liu, Chunlan Ma, Haotian Ye +1
The world's more than 7000 languages are written in at least 293 scripts. Due to various reasons, many closely related languages use different scripts, which poses a difficulty for…
MoSECroT: Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer
Haotian Ye, Yihong Liu, Chunlan Ma +1
Transformer-based pre-trained language models (PLMs) have achieved remarkable performance in various natural language processing (NLP) tasks. However, pre-training such models can…