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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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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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69 |
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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Abstract:
AbstractIn this work, we examine the ability of NER models to use contextual information when predicting the type of an ambiguous entity. We introduce NRB, a new testbed carefully designed to diagnose Name Regularity Bias of NER models. Our results indicate that all state-of-the-art models we tested show such a bias; BERT fine-tuned models significantly outperforming feature-based (LSTM-CRF) ones on NRB, despite having comparable (sometimes lower) performance on standard benchmarks.To mitigate this bias, we propose a novel model-agnostic training method that adds learnable adversarial noise to some entity mentions, thus enforcing models to focus more strongly on the contextual signal, leading to significant gains on NRB. Combining it with two other training strategies, data augmentation and parameter freezing, leads to further gains.
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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_00386 https://doaj.org/article/e7d935421447491093092feda1634276
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71 |
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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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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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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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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