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41
Neural Coreference Resolution for Arabic ...
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42
Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words ...
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43
Choosing a Feature Set for Generating Referring Expressions in Context ...
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44
Aspect-Similarity-Aware Historical Influence Modeling for Rating Prediction ...
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45
Information Extraction from Federal Open Market Committee Statements ...
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46
Social Event: Music for COLING'2020 ...
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47
Sentiment Forecasting in Dialog ...
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48
Exploiting Microblog Conversation Structures to Detect Rumors ...
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49
Individual corpora predict fast memory retrieval during reading ...
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50
Improving Document-Level Sentiment Analysis with User and Product Context ...
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51
Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs ...
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52
Measuring Alignment to Authoritarian State Media as Framing Bias ...
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53
Hitachi at SemEval-2020 Task 7: Stacking at Scalewith Heterogeneous Language Models for Humor Recognition ...
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54
Exploring the Zero Shot Limit of FewRel ...
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55
ASR for Non-standardised Languages with Dialectal Variation: the case of Swiss German ...
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56
DisenE: Disentangling Knowledge Graph Embeddings ...
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57
IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE ...
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58
Query Distillation: BERT-based Distillation for Ensemble Ranking ...
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59
Complaint Identification in Social Media with Transformer Networks ...
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60
AraBench: Benchmarking Dialectal Arabic-English Machine Translation ...
Abstract: Low-resource machine translation suffers from the scarcity of training data and the unavailability of standard evaluation sets. While a number of research efforts target the former, the unavailability of evaluation benchmarks remain a major hindrance in tracking the progress in low-resource machine translation. In this paper, we introduce AraBench, an evaluation suite for dialectal Arabic to English machine translation. Compared to Modern Standard Arabic, Arabic dialects are challenging due to their spoken nature, non-standard orthography, and a large variation in dialectness. To this end, we pool together already available Dialectal Arabic-English resources and additionally build novel test sets. AraBench offers 4 coarse, 15 fine-grained and 25 city-level dialect categories, belonging to diverse genres, such as media, chat, religion and travel with varying level of dialectness. We report strong baselines using several training settings: fine-tuning, back-translation and data augmentation. The evaluation ...
Keyword: Computer and Information Science; Natural Language Processing; Neural Network
URL: https://dx.doi.org/10.48448/wbq8-xt62
https://underline.io/lecture/6389-arabench-benchmarking-dialectal-arabic-english-machine-translation
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