Search Results for author:
Found 23 papers, 16 papers with code
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities.
MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agents
MLLM agents demonstrate potential for complex embodied tasks by retrieving multimodal task-relevant trajectory data.
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages
Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1, 300 indigenous languages and a population of 671 million people.
A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges
The swift evolution of Large-scale Models (LMs), either language-focused or multi-modal, has garnered extensive attention in both academy and industry.
Cradle: Empowering Foundation Agents Towards General Computer Control
To handle this issue, we propose the General Computer Control (GCC) setting to restrict foundation agents to interact with software through the most unified and standardized interface, i. e., using screenshots as input and keyboard and mouse actions as output.
Decomposed Meta-Learning for Few-Shot Sequence Labeling
With the detected mention spans, we further leverage the MAML-enhanced span-level prototypical network for few-shot type classification.
Ranked #1 on
Slot Filling
on SNIPS
(F1 (1-shot) avg metric)
GenDec: A robust generative Question-decomposition method for Multi-hop reasoning
Multi-hop QA (MHQA) involves step-by-step reasoning to answer complex questions and find multiple relevant supporting facts.
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
The Aya initiative also serves as a valuable case study in participatory research, involving collaborators from 119 countries.
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)
All Data on the Table: Novel Dataset and Benchmark for Cross-Modality Scientific Information Extraction
To close this gap in data availability and enable cross-modality IE, while alleviating labeling costs, we propose a semi-supervised pipeline for annotating entities in text, as well as entities and relations in tables, in an iterative procedure.
AutoAgents: A Framework for Automatic Agent Generation
Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks.
CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition
Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language.
TACR: A Table-alignment-based Cell-selection and Reasoning Model for Hybrid Question-Answering
A common challenge in HQA and other passage-table QA datasets is that it is generally unrealistic to iterate over all table rows, columns, and linked passages to retrieve evidence.
Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions
Temporal and numerical expression understanding is of great importance in many downstream Natural Language Processing (NLP) and Information Retrieval (IR) tasks.
Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey
Storytelling and narrative are fundamental to human experience, intertwined with our social and cultural engagement.
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text
Self-supervised representation learning has proved to be a valuable component for out-of-distribution (OoD) detection with only the texts of in-distribution (ID) examples.
TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases
Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios.
BoningKnife: Joint Entity Mention Detection and Typing for Nested NER via prior Boundary Knowledge
While named entity recognition (NER) is a key task in natural language processing, most approaches only target flat entities, ignoring nested structures which are common in many scenarios.
Ranked #1 on
Nested Mention Recognition
on ACE 2005
AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER
Neural methods have been shown to achieve high performance in Named Entity Recognition (NER), but rely on costly high-quality labeled data for training, which is not always available across languages.
UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data
Prior works in cross-lingual named entity recognition (NER) with no/little labeled data fall into two primary categories: model transfer based and data transfer based methods.
Ranked #1 on
Cross-Lingual NER
on NoDaLiDa Norwegian Bokmål
Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language
However, such methods either are not applicable if the labeled data in the source languages is unavailable, or do not leverage information contained in unlabeled data in the target language.
Ranked #1 on
Cross-Lingual NER
on CoNLL German
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER).
Ranked #1 on
Cross-Lingual NER
on MSRA
CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters.
Ranked #1 on
Chinese Named Entity Recognition
on Weibo NER
(Accuracy-NE metric)
Chinese Named Entity Recognition
named-entity-recognition
+4
