965 research outputs found
Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
We propose a novel data augmentation for labeled sentences called contextual
augmentation. We assume an invariance that sentences are natural even if the
words in the sentences are replaced with other words with paradigmatic
relations. We stochastically replace words with other words that are predicted
by a bi-directional language model at the word positions. Words predicted
according to a context are numerous but appropriate for the augmentation of the
original words. Furthermore, we retrofit a language model with a
label-conditional architecture, which allows the model to augment sentences
without breaking the label-compatibility. Through the experiments for six
various different text classification tasks, we demonstrate that the proposed
method improves classifiers based on the convolutional or recurrent neural
networks.Comment: NAACL 201
Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Prompts
Domain adaptation is an important challenge for neural machine translation.
However, the traditional fine-tuning solution requires multiple extra training
and yields a high cost. In this paper, we propose a non-tuning paradigm,
resolving domain adaptation with a prompt-based method. Specifically, we
construct a bilingual phrase-level database and retrieve relevant pairs from it
as a prompt for the input sentences. By utilizing Retrieved Phrase-level
Prompts (RePP), we effectively boost the translation quality. Experiments show
that our method improves domain-specific machine translation for 6.2 BLEU
scores and improves translation constraints for 11.5% accuracy without
additional training
Data Augmentation for Machine Translation via Dependency Subtree Swapping
We present a generic framework for data augmentation via dependency subtree
swapping that is applicable to machine translation. We extract corresponding
subtrees from the dependency parse trees of the source and target sentences and
swap these across bisentences to create augmented samples. We perform thorough
filtering based on graphbased similarities of the dependency trees and
additional heuristics to ensure that extracted subtrees correspond to the same
meaning. We conduct resource-constrained experiments on 4 language pairs in
both directions using the IWSLT text translation datasets and the Hunglish2
corpus. The results demonstrate consistent improvements in BLEU score over our
baseline models in 3 out of 4 language pairs. Our code is available on GitHub
Data augmentation for machine translation via dependency subtree swapping
We present a generic framework for data augmentation via dependency subtree swapping that is applicable to machine translation. We extract corresponding subtrees from the dependency parse trees of the source and target sentences and swap these across bisentences to create augmented samples. We perform thorough filtering based on graphbased similarities of the dependency trees and additional heuristics to ensure that extracted subtrees correspond to the same meaning. We conduct resource-constrained experiments on 4 language pairs in both directions using the IWSLT text translation datasets and the Hunglish2 corpus. The results demonstrate consistent improvements in BLEU score over our baseline models in 3 out of 4 language pairs. Our code is available on GitHub
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing
We introduce a novel method for multilingual transfer that utilizes deep
contextual embeddings, pretrained in an unsupervised fashion. While contextual
embeddings have been shown to yield richer representations of meaning compared
to their static counterparts, aligning them poses a challenge due to their
dynamic nature. To this end, we construct context-independent variants of the
original monolingual spaces and utilize their mapping to derive an alignment
for the context-dependent spaces. This mapping readily supports processing of a
target language, improving transfer by context-aware embeddings. Our
experimental results demonstrate the effectiveness of this approach for
zero-shot and few-shot learning of dependency parsing. Specifically, our method
consistently outperforms the previous state-of-the-art on 6 tested languages,
yielding an improvement of 6.8 LAS points on average.Comment: NAACL 201
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