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Putting Sarcasm Detection into Context: The Effects of

%0 Conference Proceedings %T Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations %A Abercrombie, Gavin %A Hovy, Dirk %Y He, He %Y Lei, Tao %Y Roberts, Will %S Proceedings of the ACL 2016 Student Research …

Bleu: a Method for Automatic Evaluation of …

B leu: a Method for Automatic Evaluation of Machine Translation. B. leu: a Method for Automatic Evaluation of Machine Translation. Kishore Papineni, Salim Roukos, Todd Ward, Wei-Jing …

Re-evaluating the Role of Bleu in Machine Translation Research

Cite (ACL): Chris Callison-Burch, Miles Osborne, and Philipp Koehn. 2006. ... %0 Conference Proceedings %T Re-evaluating the Role of Bleu in Machine Translation Research %A Callison-Burch, Chris %A Osborne, Miles %A Koehn, Philipp %Y McCarthy, Diana %Y Wintner, Shuly %S 11th Conference of the European Chapter of the …

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The 1st Workshop on Machine Learning for Ancient Languages @ ACL 2024 Bangkok, Thailand Thursday, August 15 2024 (co-located with ACL 2024) https:// DESCRIPTION. ACL-Sponsored Event: 1. ... (RepL4NLP-2021), co-located with ACL 2021 in Bangkok, Thailand, invites papers of a theoretical or …

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On Compositional Generalization of Neural Machine Translation

Cite (ACL): Yafu Li, Yongjing Yin, Yulong Chen, and Yue Zhang. 2021. On Compositional Generalization of Neural Machine Translation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages …

Improving Grammatical Error Correction with …

Wangchunshu Zhou, Tao Ge, Chang Mu, Ke Xu, Furu Wei, Ming Zhou. Findings of the Association for Computational Linguistics: EMNLP 2020. 2020.

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Chunk-based Nearest Neighbor Machine Translation

%0 Conference Proceedings %T Chunk-based Nearest Neighbor Machine Translation %A Martins, Pedro Henrique %A Marinho, Zita %A Martins, André F. T. %Y Goldberg, Yoav %Y Kozareva, Zornitsa %Y Zhang, Yue %S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing %D 2022 %8 …

Learning Phrase Representations using

Cite (ACL): Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation.

The Utility of Continuous Passive Motion After Anterior …

Anterior cruciate ligament (ACL) injuries are among the most frequent injuries seen by sports medicine surgeons, with more than 120,000 occurring each year. 11 Advancements in the understanding of pre- and postoperative rehabilitation have changed the way many surgeons and physical therapists achieve the ultimate goal of returning …

Neural Machine Translation of Rare Words with Subword …

Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of …

Glancing Transformer for Non-Autoregressive Neural Machine Translation

With GLM, we develop Glancing Transformer (GLAT) for machine translation. With only single-pass parallel decoding, GLAT is able to generate high-quality translation with 8×-15× speedup. Note that GLAT does not modify the network architecture, which is a training method to learn word interdependency. Experiments on multiple WMT language ...

Survey of Low-Resource Machine Translation

Abstract. We present a survey covering the state of the art in low-resource machine translation (MT) research. There are currently around 7,000 languages spoken in the world and almost all language pairs lack significant resources for training machine translation models. There has been increasing interest in research addressing the …

To Cache or Not To Cache? Experiments with Adaptive …

Experiments with Adaptive Models in Statistical Machine Translation. Jorg Tiedemann¨ Department of Linguistics and Philology Uppsala University, Uppsala/Sweden jorg.tiedemann@lingfil.uu.se Abstract. We report results of our submissions to the WMT 2010 shared translation task in which we applied a system that includes adaptive …

Google's Multilingual Neural Machine Translation

Abstract We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language.

Machine Reading Comprehension as Data Augmentation: A

liu-etal-2021-machine Cite (ACL): Jian Liu, Yufeng Chen, and Jinan Xu. 2021. Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2716–2725, Online and Punta Cana, Dominican …

On the Impact of Various Types of Noise on Neural Machine Translation

Abstract. We examine how various types of noise in the parallel training data impact the quality of neural machine translation systems. We create five types of artificial noise and analyze how they degrade performance in neural and statistical machine translation. We find that neural models are generally more harmed by noise than …

Unsupervised Neural Machine Translation for Low

Abstract. Unsupervised machine translation, which utilizes unpaired monolingual corpora as training data, has achieved comparable performance against supervised machine translation. However, it still suffers from data-scarce domains. To address this issue, this paper presents a novel meta-learning algorithm for unsupervised …

Knowledge Distillation for Multilingual Unsupervised Neural Machine

Abstract. Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs. However, it can only translate between a single language pair and cannot produce translation results for multiple language pairs at the same time. That is, research on multilingual UNMT has been limited.

Training Neural Machine Translation to Apply

In this paper we approach the problem by training a neural MT system to learn how to use custom terminology when provided with the input. Comparative experiments show that our method is not only more effective than a state-of-the-art implementation of constrained decoding, but is also as fast as constraint-free decoding. Anthology ID: P19 …

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Neural Machine Translation with Extended Context

Abstract. We investigate the use of extended context in attention-based neural machine translation. We base our experiments on translated movie subtitles and discuss the effect of increasing the segments beyond single translation units. We study the use of extended source language context as well as bilingual context extensions.

Massively Multilingual Neural Machine …

Abstract. Multilingual Neural Machine Translation enables training a single model that supports translation from multiple source languages into multiple target languages. We perform extensive …

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Event Extraction as Machine Reading Comprehension

Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. Previous methods for EE typically model it as a classification task, which are usually prone to the data scarcity problem. In this paper, we propose a new learning paradigm of EE, by explicitly casting it as a machine reading ...

Revealing the Importance of Semantic …

Machine Reading at Scale (MRS) is a challenging task in which a system is given an input query and is asked to produce a precise output by "reading" information from a large knowledge base. The task …

Adaptive Nearest Neighbor Machine Translation

kNN-MT, recently proposed by Khandelwal et al. (2020a), successfully combines pre-trained neural machine translation (NMT) model with token-level k-nearest-neighbor (kNN) retrieval to improve the translation accuracy. However, the traditional kNN algorithm used in kNN-MT simply retrieves a same number of nearest neighbors for each …

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AdvAug: Robust Adversarial Augmentation for Neural

Abstract. In this paper, we propose a new adversarial augmentation method for Neural Machine Translation (NMT). The main idea is to minimize the vicinal risk over virtual sentences sampled from two vicinity distributions, in which the crucial one is a novel vicinity distribution for adversarial sentences that describes a smooth interpolated ...