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1
EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification ...
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2
Delving Deeper into Cross-lingual Visual Question Answering ...
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3
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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4
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ...
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5
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation ...
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6
Improving Word Translation via Two-Stage Contrastive Learning ...
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7
On cross-lingual retrieval with multilingual text encoders
Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : Springer Science + Business Media, 2022
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8
XHate-999: analyzing and detecting abusive language across domains and languages
Glavaš, Goran [Verfasser]; Karan, Mladen [Verfasser]; Vulic, Ivan [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2021
DNB Subject Category Language
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9
Specializing unsupervised pretraining models for word-level semantic similarity
Lauscher, Anne [Verfasser]; Vulic, Ivan [Verfasser]; Ponti, Edoardo Maria [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2021
DNB Subject Category Language
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10
Towards instance-level parser selection for cross-lingual transfer of dependency parsers
Litschko, Robert [Verfasser]; Vulic, Ivan [Verfasser]; Agić, Želiko [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2021
DNB Subject Category Language
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11
SimLex-999 Slovenian translation SimLex-999-sl 1.0
Pollak, Senja; Vulić, Ivan; Pelicon, Andraž. - : University of Ljubljana, 2021
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12
Towards Zero-shot Language Modeling ...
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13
Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
Abstract: We present a systematic study on multilingual and cross-lingual intent detection from spoken data. The study leverages a new resource put forth in this work, termed MInDS-14, a first training and evaluation resource for the intent detection task with spoken data. It covers 14 intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties. Our key results indicate that combining machine translation models with state-of-the-art multilingual sentence encoders (e.g., LaBSE) can yield strong intent detectors in the majority of target languages covered in MInDS-14, and offer comparative analyses across different axes: e.g., zero-shot versus few-shot learning, translation direction, and impact of speech recognition. We see this work as an important step towards more inclusive development and evaluation of multilingual intent detectors from spoken data, in a much wider spectrum of languages compared to prior work. ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2104.08524
https://dx.doi.org/10.48550/arxiv.2104.08524
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14
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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15
Modelling Latent Translations for Cross-Lingual Transfer ...
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16
Prix-LM: Pretraining for Multilingual Knowledge Base Construction ...
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17
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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18
xGQA: Cross-Lingual Visual Question Answering ...
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19
On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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20
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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