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Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Classifying Dyads for Militarized Conflict Analysis
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Efficient Sampling of Dependency Structure
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Searching for More Efficient Dynamic Programs
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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A Bayesian Framework for Information-Theoretic Probing
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation
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AWESSOME : An unsupervised sentiment intensity scoring framework using neural word embeddings
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Robust fragment-based framework for cross-lingual sentence retrieval
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 ; 935 ; 944 (2021)
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Abstract:
© 2021 The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://aclanthology.org/2021.findings-emnlp.80 ; Cross-lingual Sentence Retrieval (CLSR) aims at retrieving parallel sentence pairs that are translations of each other from a multilingual set of comparable documents. The retrieved parallel sentence pairs can be used in other downstream NLP tasks such as machine translation and cross-lingual word sense disambiguation. We propose a CLSR framework called Robust Fragment-level Representation (RFR) CLSR framework to address Out-of- Domain (OOD) CLSR problems. In particular, we improve the sentence retrieval robustness by representing each sentence as a collection of fragments. In this way, we change the retrieval granularity from the sentence to the fragment level. We performed CLSR experiments based on three OOD datasets, four language pairs, and three base well-known sentence encoders: m-USE, LASER, and LaBSE. Experimental results show that RFR significantly improves the base encoders’ performance for more than 85% of the cases.
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Keyword:
Cross-lingual; Sentence Retrieval
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URL: http://hdl.handle.net/2436/624330
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Evaluating multilingual text encoders for unsupervised cross-lingual retrieval
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Come hither or go away? Recognising pre-electoral coalition signals in the news
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LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification ...
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Autoregressive Reasoning over Chains of Facts with Transformers ...
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Rethinking summarization and storytelling for modern social multimedia
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In: Rudinac, Stevan, Chua, Tat-Seng, Diaz-Ferreyra, Nicolas, Friedland, Gerald, Gornostaja, Tatjana, Huet, Benoit, Kaptein, Rianne, Lindén, Krister, Moens, Marie-Francine, Peltonen, Jaakko, Redi, Miriam, Schedl, Markus, Shamma, David A, Smeaton, Alan F. orcid:0000-0003-1028-8389 and Xie, Lexing (2018) Rethinking summarization and storytelling for modern social multimedia. In: The 24th International Conference on Multimedia Modeling (MMM2018), 5-7 Feb, 2018, Bangkok, Thailand. ISBN 978-3-319-73599-3 (2018)
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Word-Level Loss Extensions for Neural Temporal Relation Classification ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain. ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain.
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