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1
Cross-media Scientific Research Achievements Query based on Ranking Learning ...
Wang, Benzhi; Liang, Meiyu; Li, Ang. - : arXiv, 2022
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2
Exploring Sub-skeleton Trajectories for Interpretable Recognition of Sign Language ...
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3
Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformers ...
Vitiugin, Fedor; Castillo, Carlos. - : arXiv, 2022
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4
Simplifying Multilingual News Clustering Through Projection From a Shared Space ...
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5
Towards Best Practices for Training Multilingual Dense Retrieval Models ...
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6
Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains ...
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7
C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval ...
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8
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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9
QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers ...
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10
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset ...
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11
From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction ...
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12
Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations ...
Meng, Yu; Zhang, Yunyi; Huang, Jiaxin. - : arXiv, 2022
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13
Offensive Language Detection in Under-resourced Algerian Dialectal Arabic Language ...
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14
Shedding New Light on the Language of the Dark Web ...
Abstract: The hidden nature and the limited accessibility of the Dark Web, combined with the lack of public datasets in this domain, make it difficult to study its inherent characteristics such as linguistic properties. Previous works on text classification of Dark Web domain have suggested that the use of deep neural models may be ineffective, potentially due to the linguistic differences between the Dark and Surface Webs. However, not much work has been done to uncover the linguistic characteristics of the Dark Web. This paper introduces CoDA, a publicly available Dark Web dataset consisting of 10000 web documents tailored towards text-based Dark Web analysis. By leveraging CoDA, we conduct a thorough linguistic analysis of the Dark Web and examine the textual differences between the Dark Web and the Surface Web. We also assess the performance of various methods of Dark Web page classification. Finally, we compare CoDA with an existing public Dark Web dataset and evaluate their suitability for various use cases. ... : To appear at NAACL 2022 (main conference) ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Information Retrieval cs.IR; Machine Learning cs.LG
URL: https://dx.doi.org/10.48550/arxiv.2204.06885
https://arxiv.org/abs/2204.06885
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15
Query Expansion and Entity Weighting for Query Reformulation Retrieval in Voice Assistant Systems ...
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16
LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback ...
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17
Finding Inverse Document Frequency Information in BERT ...
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18
Improving Word Translation via Two-Stage Contrastive Learning ...
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19
nigam@COLIEE-22: Legal Case Retrieval and Entailment using Cascading of Lexical and Semantic-based models ...
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20
Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval Models ...
Chen, Tao; Zhang, Mingyang; Lu, Jing. - : arXiv, 2022
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