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Morphological Processing of Low-Resource Languages: Where We Are and What's Next ...
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Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability ...
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Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data ...
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Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages ...
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How to Adapt Your Pretrained Multilingual Model to 1600 Languages ...
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Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas ...
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{PROST}: {P}hysical Reasoning about Objects through Space and Time ...
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Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data ...
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How to Adapt Your Pretrained Multilingual Model to 1600 Languages ...
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AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages ...
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CLiMP: A Benchmark for Chinese Language Model Evaluation ...
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Abstract:
Linguistically informed analyses of language models (LMs) contribute to the understanding and improvement of these models. Here, we introduce the corpus of Chinese linguistic minimal pairs (CLiMP), which can be used to investigate what knowledge Chinese LMs acquire. CLiMP consists of sets of 1,000 minimal pairs (MPs) for 16 syntactic contrasts in Mandarin, covering 9 major Mandarin linguistic phenomena. The MPs are semi-automatically generated, and human agreement with the labels in CLiMP is 95.8%. We evaluated 11 different LMs on CLiMP, covering n-grams, LSTMs, and Chinese BERT. We find that classifier-noun agreement and verb complement selection are the phenomena that models generally perform best at. However, models struggle the most with the ba construction, binding, and filler-gap dependencies. Overall, Chinese BERT achieves an 81.8% average accuracy, while the performances of LSTMs and 5-grams are only moderately above chance level. ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2101.11131 https://dx.doi.org/10.48550/arxiv.2101.11131
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Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas
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In: Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas. Edited by: Mager, Manuel; Oncevay, Arturo; Rios, Annette; Meza Ruiz, Ivan Vladimir; Palmer, Alexis; Neubig, Graham; Kann, Katharina (2021). Online: Association for Computational Linguistics. (2021)
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Learning to Learn Morphological Inflection for Resource-Poor Languages ...
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Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings
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Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings ...
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Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge ...
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