53,902 research outputs found
Revisiting Negation in Neural Machine Translation
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.Comment: To appear at TACL and to be presented at ACL 2021. Authors' final
versio
Does Multimodality Help Human and Machine for Translation and Image Captioning?
This paper presents the systems developed by LIUM and CVC for the WMT16
Multimodal Machine Translation challenge. We explored various comparative
methods, namely phrase-based systems and attentional recurrent neural networks
models trained using monomodal or multimodal data. We also performed a human
evaluation in order to estimate the usefulness of multimodal data for human
machine translation and image description generation. Our systems obtained the
best results for both tasks according to the automatic evaluation metrics BLEU
and METEOR.Comment: 7 pages, 2 figures, v4: Small clarification in section 4 title and
conten
What do Neural Machine Translation Models Learn about Morphology?
Neural machine translation (MT) models obtain state-of-the-art performance
while maintaining a simple, end-to-end architecture. However, little is known
about what these models learn about source and target languages during the
training process. In this work, we analyze the representations learned by
neural MT models at various levels of granularity and empirically evaluate the
quality of the representations for learning morphology through extrinsic
part-of-speech and morphological tagging tasks. We conduct a thorough
investigation along several parameters: word-based vs. character-based
representations, depth of the encoding layer, the identity of the target
language, and encoder vs. decoder representations. Our data-driven,
quantitative evaluation sheds light on important aspects in the neural MT
system and its ability to capture word structure.Comment: Updated decoder experiment
A Convolutional Encoder Model for Neural Machine Translation
The prevalent approach to neural machine translation relies on bi-directional
LSTMs to encode the source sentence. In this paper we present a faster and
simpler architecture based on a succession of convolutional layers. This allows
to encode the entire source sentence simultaneously compared to recurrent
networks for which computation is constrained by temporal dependencies. On
WMT'16 English-Romanian translation we achieve competitive accuracy to the
state-of-the-art and we outperform several recently published results on the
WMT'15 English-German task. Our models obtain almost the same accuracy as a
very deep LSTM setup on WMT'14 English-French translation. Our convolutional
encoder speeds up CPU decoding by more than two times at the same or higher
accuracy as a strong bi-directional LSTM baseline.Comment: 13 page
Example-based machine translation of the Basque language
Basque is both a minority and a highly inflected language with free order of sentence constituents. Machine Translation of Basque is thus both a real need and a test bed for MT techniques. In this paper, we present a modular Data-Driven MT system which includes different chunkers as well as chunk aligners which can deal with the free order of sentence constituents of Basque. We conducted Basque to English translation experiments, evaluated on a large corpus
(270, 000 sentence pairs). The experimental results show that our system significantly outperforms state-of-the-art
approaches according to several common automatic evaluation metrics
A Large-Scale Comparison of Historical Text Normalization Systems
There is no consensus on the state-of-the-art approach to historical text
normalization. Many techniques have been proposed, including rule-based
methods, distance metrics, character-based statistical machine translation, and
neural encoder--decoder models, but studies have used different datasets,
different evaluation methods, and have come to different conclusions. This
paper presents the largest study of historical text normalization done so far.
We critically survey the existing literature and report experiments on eight
languages, comparing systems spanning all categories of proposed normalization
techniques, analysing the effect of training data quantity, and using different
evaluation methods. The datasets and scripts are made publicly available.Comment: Accepted at NAACL 201
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