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Cross-media Scientific Research Achievements Query based on Ranking Learning ...
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Exploring Sub-skeleton Trajectories for Interpretable Recognition of Sign Language ...
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Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformers ...
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Simplifying Multilingual News Clustering Through Projection From a Shared Space ...
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Towards Best Practices for Training Multilingual Dense Retrieval Models ...
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Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains ...
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C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval ...
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Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers ...
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MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset ...
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From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction ...
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Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations ...
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Offensive Language Detection in Under-resourced Algerian Dialectal Arabic Language ...
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Query Expansion and Entity Weighting for Query Reformulation Retrieval in Voice Assistant Systems ...
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LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback ...
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Improving Word Translation via Two-Stage Contrastive Learning ...
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nigam@COLIEE-22: Legal Case Retrieval and Entailment using Cascading of Lexical and Semantic-based models ...
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Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval Models ...
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Abstract:
The pre-trained language model (eg, BERT) based deep retrieval models achieved superior performance over lexical retrieval models (eg, BM25) in many passage retrieval tasks. However, limited work has been done to generalize a deep retrieval model to other tasks and domains. In this work, we carefully select five datasets, including two in-domain datasets and three out-of-domain datasets with different levels of domain shift, and study the generalization of a deep model in a zero-shot setting. Our findings show that the performance of a deep retrieval model is significantly deteriorated when the target domain is very different from the source domain that the model was trained on. On the contrary, lexical models are more robust across domains. We thus propose a simple yet effective framework to integrate lexical and deep retrieval models. Our experiments demonstrate that these two models are complementary, even when the deep model is weaker in the out-of-domain setting. The hybrid model obtains an average of ... : Accepted at ECIR 2022 (full paper) ...
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Keyword:
FOS Computer and information sciences; Information Retrieval cs.IR
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URL: https://arxiv.org/abs/2201.10582 https://dx.doi.org/10.48550/arxiv.2201.10582
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