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SummEval: Re-evaluating Summarization Evaluation
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 391-409 (2021) (2021)
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62 |
Neural OCR Post-Hoc Correction of Historical Corpora
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 479-493 (2021) (2021)
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63 |
Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1460-1474 (2021) (2021)
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How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 962-977 (2021) (2021)
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65 |
Modeling Content and Context with Deep Relational Learning
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 100-119 (2021) (2021)
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66 |
A Statistical Analysis of Summarization Evaluation Metrics Using Resampling Methods
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1132-1146 (2021) (2021)
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67 |
Optimizing over subsequences generates context-sensitive languages
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 528-537 (2021) (2021)
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Morphology Matters: A Multilingual Language Modeling Analysis
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 261-276 (2021) (2021)
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Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1249-1267 (2021) (2021)
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70 |
Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 586-604 (2021) (2021)
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Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1303-1319 (2021) (2021)
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72 |
Deciphering Undersegmented Ancient Scripts Using Phonetic Prior
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 69-81 (2021) (2021)
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Sparse, Dense, and Attentional Representations for Text Retrieval
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 329-345 (2021) (2021)
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Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 570-585 (2021) (2021)
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Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1320-1335 (2021) (2021)
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Formal Basis of a Language Universal
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In: Computational Linguistics, Vol 47, Iss 1, Pp 9-42 (2021) (2021)
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Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 978-994 (2021) (2021)
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78 |
Revisiting Negation in Neural Machine Translation
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 740-755 (2021) (2021)
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79 |
Quantifying Cognitive Factors in Lexical Decline
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1529-1545 (2021) (2021)
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Abstract:
AbstractWe adopt an evolutionary view on language change in which cognitive factors (in addition to social ones) affect the fitness of words and their success in the linguistic ecosystem. Specifically, we propose a variety of psycholinguistic factors—semantic, distributional, and phonological—that we hypothesize are predictive of lexical decline, in which words greatly decrease in frequency over time. Using historical data across three languages (English, French, and German), we find that most of our proposed factors show a significant difference in the expected direction between each curated set of declining words and their matched stable words. Moreover, logistic regression analyses show that semantic and distributional factors are significant in predicting declining words. Further diachronic analysis reveals that declining words tend to decrease in the diversity of their lexical contexts over time, gradually narrowing their ‘ecological niches’.
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Keyword:
Computational linguistics. Natural language processing; P98-98.5
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URL: https://doi.org/10.1162/tacl_a_00441 https://doaj.org/article/d75d0294bae249ac97a00f133d6e647d
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80 |
Joint Universal Syntactic and Semantic Parsing
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 756-773 (2021) (2021)
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