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Found 13 papers, 9 papers with code
Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu
In-context machine translation (MT) with large language models (LLMs) is a promising approach for low-resource MT, as it can readily take advantage of linguistic resources such as grammar books and dictionaries.
SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model
To this end, we propose SSMLoRA (State Space Model Low-Rank Adaptation), an extension of LoRA that incorporates a State Space Model (SSM) to interconnect low-rank matrices.
EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models
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, focusing on improving language coverage for low-resource languages.
A Recipe of Parallel Corpora Exploitation for Multilingual Large Language Models
Recent studies have highlighted the potential of exploiting parallel corpora to enhance multilingual large language models, improving performance in both bilingual tasks, e. g., machine translation, and general-purpose tasks, e. g., text classification.
XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples
Thus, we introduce XAMPLER: Cross-Lingual Example Retrieval, a method tailored to tackle the challenge of cross-lingual in-context learning using only annotated English data.
MaLA-500: Massive Language Adaptation of Large Language Models
Large language models (LLMs) have advanced the state of the art in natural language processing.
Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark
We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages.
Ranked #1 on
Named Entity Recognition (NER)
on UNER v1 (Danish)
OFA: A Framework of Initializing Unseen Subword Embeddings for Efficient Large-scale Multilingual Continued Pretraining
Instead of pretraining multilingual language models from scratch, a more efficient method is to adapt existing pretrained language models (PLMs) to new languages via vocabulary extension and continued pretraining.
mPLM-Sim: Better Cross-Lingual Similarity and Transfer in Multilingual Pretrained Language Models
Recent multilingual pretrained language models (mPLMs) have been shown to encode strong language-specific signals, which are not explicitly provided during pretraining.
Open-Ended Question Answering
Zero-Shot Cross-Lingual Transfer
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.
Modeling Content-Emotion Duality via Disentanglement for Empathetic Conversation
To solve the task, it is essential to model the content-emotion duality of a dialogue, which is composed of the content view (i. e., what personal experiences are described) and the emotion view (i. e., the feelings of the speaker on these experiences).
Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality
Empathetic response generation aims to comprehend the user emotion and then respond to it appropriately.
A Shared-Private Representation Model with Coarse-to-Fine Extraction for Target Sentiment Analysis
However, the relation between the target extraction task and the target classification task has not been well exploited.
