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Dependency Patterns of Complex Sentences and Semantic Disambiguation for Abstract Meaning Representation Parsing ...
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An Information-Theoretic Characterization of Morphological Fusion ...
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One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets ...
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Learning Data Augmentation Schedules for Natural Language Processing ...
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Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model ...
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A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders ...
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InFillmore: Frame-Guided Language Generation with Bidirectional Context ...
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Learning Embeddings for Rare Words Leveraging Internet Search Engine and Spatial Location Relationships ...
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Evaluating Universal Dependency Parser Recovery of Predicate Argument Structure via CompChain Analysis ...
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ParsFEVER : a Dataset for Farsi Fact Extraction and Verification ...
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Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge ...
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Teach the Rules, Provide the Facts: Targeted Relational-knowledge Enhancement for Textual Inference ...
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Exploring Pre-Trained Transformers and Bilingual Transfer Learning for Arabic Coreference Resolution ...
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Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Review ...
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MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer ...
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IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.79/ Abstract: With the advent of contextualized embeddings, attention towards neural ranking approaches for Information Retrieval increased considerably. However, two aspects have remained largely neglected: i) queries usually consist of few keywords only, which increases ambiguity and makes their contextualization harder, and ii) performing neural ranking on non-English documents is still cumbersome due to shortage of labeled datasets. In this paper we present SIR (Sense-enhanced Information Retrieval) to mitigate both problems by leveraging word sense information. At the core of our approach lies a novel multilingual query expansion mechanism based on Word Sense Disambiguation that provides sense definitions as additional semantic information for the query. Importantly, we use senses as a bridge across languages, thus allowing our model to perform considerably better than its supervised and unsupervised alternatives across French, German, ...
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Keyword:
Data Management System; Machine translation; Natural Language Processing; Neural Network
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URL: https://underline.io/lecture/37802-ir-like-a-sir-sense-enhanced-information-retrieval-for-multiple-languages https://dx.doi.org/10.48448/z11a-gr02
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