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Found 12 papers, 3 papers with code
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Crosslingual Lexical Semantic Similarity
We introduce Multi-SimLex, a large-scale lexical resource and evaluation benchmark covering data sets for 12 typologically diverse languages, including major languages (e. g., Mandarin Chinese, Spanish, Russian) as well as less-resourced ones (e. g., Welsh, Kiswahili).
Semantic Data Set Construction from Human Clustering and Spatial Arrangement
We demonstrate how the resultant data set can be used for fine-grained analyses and evaluation of representation learning models on the intrinsic tasks of semantic clustering and semantic similarity.
Natural Language Processing for Multilingual Task-Oriented Dialogue
In this tutorial, we will thus discuss and demonstrate the importance of (building) multilingual ToD systems, and then provide a systematic overview of current research gaps, challenges and initiatives related to multilingual ToD systems, with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP.
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation
Through this process we annotate a new large-scale dataset for training and evaluation of multilingual and cross-lingual ToD systems.
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems
We find that the most critical factor preventing the creation of truly multilingual ToD systems is the lack of datasets in most languages for both training and evaluation.
Verb Knowledge Injection for Multilingual Event Processing
We investigate whether injecting explicit information on verbs' semantic-syntactic behaviour improves the performance of LM-pretrained Transformers in event extraction tasks -- downstream tasks for which accurate verb processing is paramount.
Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis
We present the first evaluation of the applicability of a spatial arrangement method (SpAM) to a typologically diverse language sample, and its potential to produce semantic evaluation resources to support multilingual NLP, with a focus on verb semantics.
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers
Following the major success of neural language models (LMs) such as BERT or GPT-2 on a variety of language understanding tasks, recent work focused on injecting (structured) knowledge from external resources into these models.
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
In order to simulate human language capacity, natural language processing systems must be able to reason about the dynamics of everyday situations, including their possible causes and effects.
Ranked #3 on
Cross-Lingual Transfer
on XCOPA
(using extra training data)
Spatial Multi-Arrangement for Clustering and Multi-way Similarity Dataset Construction
We present a novel methodology for fast bottom-up creation of large-scale semantic similarity resources to support development and evaluation of NLP systems.
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity
We introduce Multi-SimLex, a large-scale lexical resource and evaluation benchmark covering datasets for 12 typologically diverse languages, including major languages (e. g., Mandarin Chinese, Spanish, Russian) as well as less-resourced ones (e. g., Welsh, Kiswahili).
