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On the State of the Art of Evaluation in Neural Language Models
[article]
2017
arXiv
pre-print
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Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. ...
We establish a new state of the art on the Penn Treebank and Wikitext-2 corpora, as well as strong baselines on the Hutter Prize dataset. ...
In Table 2
ENWIK8 In contrast to the previous datasets, our numbers on this task (reported in BPC, following convetion) are slightly off the state of the art. ...
arXiv:1707.05589v2
fatcat:ozrvqn2wgjgt3fon25jhqnosne
An In-depth Walkthrough on Evolution of Neural Machine Translation
[article]
2020
arXiv
pre-print
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This paper aims to study the major trends in Neural Machine Translation, the state of the art models in the domain and a high level comparison between them. ...
Neural Machine Translation (NMT) methodologies have burgeoned from using simple feed-forward architectures to the state of the art; viz. BERT model. ...
The state of the art Language Modelling concepts were engendered and disseminated with Neural Networks. ...
arXiv:2004.04902v1
fatcat:giua7w4y4bh3pbucubmh43mlc4
Progress and Tradeoffs in Neural Language Models
[article]
2018
arXiv
pre-print
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We compare state-of-the-art NLMs with "classic" Kneser-Ney (KN) LMs in terms of energy usage, latency, perplexity, and prediction accuracy using two standard benchmarks. ...
Undoubtedly, neural language models (NLMs) have reduced perplexity by impressive amounts. ...
Quasirecurrent neural networks (QRNNs; achieve current state of the art in word-level language modeling (Merity et al., 2018a) . ...
arXiv:1811.00942v1
fatcat:h2e3nyv2y5eu7e4oniuepy65ty
Deep Affix Features Improve Neural Named Entity Recognizers
2018
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
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Additionally, we show improvement on SemEval 2013 task 9.1 DrugNER, achieving state of the art results on the MedLine dataset and the second best results overall (-1.3% from state of the art). ...
1.5-2.3 percent over the state of the art without relying on any dictionary features. ...
Morphological features were highly effective in named entity recognizers before neural networks became the new state-of-the-art. ...
doi:10.18653/v1/s18-2021
dblp:conf/starsem/YadavSB18
fatcat:4rekxxh5sve5ra3eai4got7fnm
Introduction to the special issue on deep learning approaches for machine translation
2017
Computer Speech and Language
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This introduction covers all topics contained in the papers included in this special issue, which basically are: integration of deep learning in statistical MT; development of the end-to-end neural MT ...
system; and introduction of deep learning in interactive MT and MT evaluation. ...
Acknowledgements The work of the 1st author is supported by the Spanish Ministerio de Economía y ...
doi:10.1016/j.csl.2017.03.001
fatcat:qrep7sdnurfvnmoogz4hssnnh4
TransQuest: Translation Quality Estimation with Cross-lingual Transformers
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2011.01536v1 fatcat:lkrol4onereitn4jpttcxukck4
Our evaluation shows that the proposed methods achieve state-of-the-art results outperforming current open-source quality estimation frameworks when trained on datasets from WMT. ...
However, the majority of these methods work only on the language pair they are trained on and need retraining for new language pairs. ...
state-of-the-art quality estimation methods in low-resource language pairs. 4. ...
arXiv:2011.01536v2
fatcat:woigmbwhqrbxlne2sbancgvrwa
Characterizing the hyper-parameter space of LSTM language models for mixed context applications
[article]
2017
arXiv
pre-print
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Applying state of the art deep learning models to novel real world datasets gives a practical evaluation of the generalizability of these models. ...
We present work to characterize the hyper parameter space of an LSTM for language modeling on a code-mixed corpus. ...
The effect of this would be that reproducing state of the art neural models on a unique dataset would require significant hyper parameter search thus limiting the reach of these models to parties with ...
arXiv:1712.03199v1
fatcat:oz3pmfvvk5eixaggbs2ynojcgy
Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction
[article]
2019
arXiv
pre-print
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We help close the gap with a framework that unifies the learning of RE and KBE models leading to significant improvements over the state-of-the-art in RE. ...
For general purpose KBs, this is often done through Relation Extraction (RE), the task of predicting KB relations expressed in text mentioning entities known to the KB. ...
Acknowledgments This work was supported in part by grants from the Natural Sciences and Engineering Research Council of Canada and a gift from Diffbot Inc. ...
arXiv:1903.10126v3
fatcat:hwseki6cxrgatlrlmh7px5jsna
Improving Named Entity Recognition for Morphologically Rich Languages Using Word Embeddings
2014
2014 13th International Conference on Machine Learning and Applications
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Unlike the previous state-ofthe-art systems developed for these languages, our system does not make use of any language dependent features. ...
In this paper, we addressed the Named Entity Recognition (NER) problem for morphologically rich languages by employing a semi-supervised learning approach based on neural networks. ...
State-of-the-art systems developed for such languages usually depend on manually designed language specific features that utilize the rich morphological structures of the words. ...
doi:10.1109/icmla.2014.24
dblp:conf/icmla/DemirO14
fatcat:falw4ef5cngm7iey4wizocgjly
An Extensive Empirical Evaluation of Character-Based Morphological Tagging for 14 Languages
2017
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
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We evaluate on 14 languages and observe consistent gains over a state-of-the-art morphological tagger across all languages except for English and French, where we match the state-of-the-art. ...
This paper investigates neural characterbased morphological tagging for languages with complex morphology and large tag sets. ...
Acknowledgment This work has been partly funded by the European Unions Horizon 2020 research and innovation programme under grant agreement No. 645452 (QT21). ...
doi:10.18653/v1/e17-1048
dblp:conf/eacl/GenabithHN17
fatcat:mmvxvhzm7vbolmjy5zskjw5m3i
Neural Task Representations as Weak Supervision for Model Agnostic Cross-Lingual Transfer
[article]
2018
arXiv
pre-print
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On a battery of tests, we show that our models outperform a number of strong baselines and rival state-of-the-art results, which rely on more complex approaches and significantly more resources and data ...
Yet, the task of transferring a model from one language to another can be expensive in terms of annotation costs, engineering time and effort. ...
state-of-the-art on one language, while relying on a much simpler method and requiring significantly fewer resources. ...
arXiv:1811.01115v1
fatcat:d4axa3dbfnfuhizpbix2ngyx24
IndicSpeech: Text-to-Speech Corpus for Indian Languages
2020
International Conference on Language Resources and Evaluation
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In this work, we also train a state-of-the-art TTS system for each of these languages and report their performances. The collected corpus, code, and trained models are made publicly available. ...
We believe that one of the major reasons for this is the lack of large, publicly available text-to-speech corpora in these languages that are suitable for training neural text-to-speech systems. ...
We see that the corpus consists of a diverse vocabulary and is at a scale well-suited for state-of-the-art neural TTS models. ...
dblp:conf/lrec/SrivastavaMRJ20
fatcat:ttzc6v7pxnedhb3yoaj4kmlcdi
Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
[article]
2016
arXiv
pre-print
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Our resulting model achieves a performance gain of 19.6% over previous state-of-the-art models. ...
To fully explore the potential of neural models, we propose a methodology for collecting a large corpus of regular expression, natural language pairs. ...
Despite the small size of KB13, our model achieves state-of-the-art results on this very resource-constrained dataset (814 examples). ...
arXiv:1608.03000v1
fatcat:kg3i4y56nrboxcznzl635ywuky
Dynamic Evaluation of Neural Sequence Models
[article]
2017
arXiv
pre-print
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Dynamic evaluation improves the state-of-the-art word-level perplexities on the Penn Treebank and WikiText-2 datasets to 51.1 and 44.3 respectively, and the state-of-the-art character-level cross-entropies ...
We present methodology for using dynamic evaluation to improve neural sequence models. ...
Neural caching has recently been used to improve the state-of-the-art at word-level language modelling (Merity et al., 2017a) . ...
arXiv:1709.07432v2
fatcat:jvzrw46qkfaltaynxk7mlibriu
Tree-to-tree Neural Networks for Program Translation
[article]
2018
arXiv
pre-print
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We evaluate the program translation capability of our tree-to-tree model against several state-of-the-art approaches. ...
Further, our approach can improve the previous state-of-the-art program translation approaches by a margin of 20 points on the translation of real-world projects. ...
Acknowledgement We thank the anonymous reviewers for their valuable comments. This material is in part ...
arXiv:1802.03691v3
fatcat:k2ew6jncj5bepancofv337yg2y
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