The task of answering natural language questions over RDF data has received wide interest in recent years, in particular in the context of the series of QALD benchmarks. The task consists of mapping a natural language question to an executable form, e.g. SPARQL, so that answers from a given KB can be extracted. So far, most systems proposed are i) monolingual and ii) rely on a set of hard-coded rules to interpret questions and map them into a SPARQL query. We present the first multilingual QALD pipeline that induces a model from training data for mapping a natural language question into logical form as probabilistic inference. In particular, our approach learns to map universal syntactic dependency representations to a language-independent logical form based on DUDES (Dependency-based Underspecified Discourse Representation Structures) that are then mapped to a SPARQL query as a deterministic second step. Our model builds on factor graphs that rely on features extracted from the dependency graph and corresponding semantic representations. We rely on approximate inference techniques, Markov Chain Monte Carlo methods in particular, as well as Sample Rank to update parameters using a ranking objective. Our focus lies on developing methods that overcome the lexical gap and present a novel combination of machine translation and word embedding approaches for this purpose. As a proof of concept for our approach, we evaluate our approach on the QALD-6 datasets for English, German & Spanish.
XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser
Beihang University（北京航空航天大学）；Beijing Language and Culture University（北京语言大学）；Beijing Information Science and Technology University（北京信息科技大学）；Shenzhen Intelligent Strong Technology Co.,Ltd.（深圳智强科技有限公司）
A System for Name and Address Parsing with Large Language Models
Center for Entity Resolution and Information Quality (ERIQ) - University of Arkansas - Little Rock, Arkansas, Little Rock, USA（实体解析与信息质量中心（ERIQ）- 亚拉荷马州立大学-小岩城，阿肯色州，小岩城，美国）
Do LLMs Provide Consistent Answers to Health-Related Questions across Languages?
Universitat Politècnica de València（瓦伦西亚理工大学）；Bonn-Aachen International Center for IT（波恩-亚琛国际信息技术中心）；University of Sheffield（谢菲尔德大学）；University of Trento（特伦托大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；ValgrAI Valencian Graduate School and Research Network of Artificial Intelligence（瓦伦西亚人工智能研究生院及研究网络）
A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese
NVIDIA（英伟达）；VinAI Research（越南人工智能研究院）
语义解析是自然语言处理的一项重要任务。然而，越南语在这一研究领域是一种低资源语言。在本文中，我们提出了第一个公共的大规模文本到SQL语义解析数据集越南。我们扩展并评估了两个强大的语义解析基线EditSQL（Zhang等人，2019）和IRNet（Guo et al.，2019年，在我们的数据集上。我们比较了两个基线的关键配置，发现：自动越南语分词提高了两个基线的分析结果;归一化逐点互信息（NPMI）得分（Bouma，2009）是有用的模式链接;潜在的句法特征提取的神经依赖分析越南语也改善了结果;用于越南语的单语语言模型PhoBERT（Nguyen和Nguyen，2020）有助于产生比最近最好的多语言语言模型XLM-R（Conneau等人，2020年）。
AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languages
Princeton University（普林斯顿大学）；Laboratory for Artificial Intelligence, Princeton University（普林斯顿大学人工智能实验室）；Gaston Berger University（加斯顿·伯杰大学）；Mila, McGill University（麦吉尔大学米拉研究所）；Canada CIFAR AI Chair（加拿大CIFAR人工智能教席）；Paris Nanterre University（巴黎南泰尔大学）；Paris-Saclay University（巴黎-萨克雷大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；LORIA（洛林计算机科学实验室）；Université de Lorraine（洛林大学）；University of Trento（特伦托大学）；University of Minnesota–Twin Cities（明尼苏达大学双城分校）；Imperial College London（伦敦帝国学院）；Binghamton University（宾汉姆顿大学）；Makerere University（马凯雷雷大学）；Penn State University（宾夕法尼亚州立大学）；Mbarara University of Science and Technology（姆巴拉拉科技大学）；Chalmers University of Technology（查尔姆斯理工大学）；University of Ibadan（伊巴丹大学）；Nnamdi Azikiwe University（纳姆迪·阿齐基韦大学）；South African Centre for Digital Language Resources（南非数字语言资源中心）
Upstage；KAIST（韩国科学技术院）；NAVER AI Lab（NAVER AI 实验室）；Seoul National University（首尔大学）；Yonsei University（延世大学）；Scatter Lab；KyungHee University（庆熙大学）；Kakao Enterprise；Sogang University（西江大学）；Hanbat National University（韩巴国立大学）；Riiid AI Research（Riiid AI 研究院）；New York University（纽约大学）
