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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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| 64 |
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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| 68 |
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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| 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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| 73 |
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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| 74 |
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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| 75 |
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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| 76 |
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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| 77 |
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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Abstract:
In this paper, we evaluate the translation of negation both automatically and manually, in English–German (EN–DE) and English– Chinese (EN–ZH). We show that the ability of neural machine translation (NMT) models to translate negation has improved with deeper and more advanced networks, although the performance varies between language pairs and translation directions. The accuracy of manual evaluation in EN→DE, DE→EN, EN→ZH, and ZH→EN is 95.7%, 94.8%, 93.4%, and 91.7%, respectively. In addition, we show that under-translation is the most significant error type in NMT, which contrasts with the more diverse error profile previously observed for statistical machine translation. To better understand the root of the under-translation of negation, we study the model’s information flow and training data. While our information flow analysis does not reveal any deficiencies that could be used to detect or fix the under-translation of negation, we find that negation is often rephrased during training, which could make it more difficult for the model to learn a reliable link between source and target negation. We finally conduct intrinsic analysis and extrinsic probing tasks on negation, showing that NMT models can distinguish negation and non-negation tokens very well and encode a lot of information about negation in hidden states but nevertheless leave room for improvement.
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Keyword:
Computational linguistics. Natural language processing; P98-98.5
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URL: https://doaj.org/article/e59dc6942e564cabb1e6de3489c18e5c
https://doi.org/10.1162/tacl_a_00395
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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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| 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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