Papers by Christos Baziotis
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models (N19-1)
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| Challenge: | Existing transfer learning methods employ language models pretrained on large generic corpora, but results come at a high computational cost and require task-specific architectures. |
| Approach: | They propose a transfer learning approach that combine a task-specific optimization function with an auxiliary language model objective, which is adjusted during the training process. |
| Outcome: | The proposed method surpasses well established transfer learning methods with greater level of complexity on a variety of affective and text classification tasks surpassing well established methods with higher level of difficulty. |
When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale (2024.naacl-long)
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| Challenge: | Multilingual machine translation (MMT) is a key tool for improving translation in low-resource languages. |
| Approach: | They examine how denoising autoencoding and backtranslation impact multilingual machine translation under different data conditions and model scales. |
| Outcome: | The proposed method improves translation efficiency in low-resource languages by using denoising autoencoding (DAE) and backtranslation (BT) . |
Attention-based Conditioning Methods for External Knowledge Integration (P19-1)
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| Challenge: | Existing approaches for incorporating external knowledge into deep neural networks (RNNs) lexicon features are used to concatenate external information into the input or hidden network layers. |
| Approach: | They propose a method for conditioning external knowledge into RNNs by concatenating a representation of the external information to the input or hidden network layers. |
| Outcome: | The proposed approach improves performance on six benchmark datasets. |
Language Model Prior for Low-Resource Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Neural machine translation is based on large parallel corpora and requires expensive training and training. |
| Approach: | They propose to incorporate a LM as prior in a neural translation model (TM) they add a regularization term which pushes the output distributions to be probable under the LM prior . |
| Outcome: | The proposed approach does not compromise decoding speed, because the LM is used only at training time, unlike previous work that requires it during inference. |
Multilingual Machine Translation with Hyper-Adapters (2022.emnlp-main)
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| Challenge: | Multilingual machine translation suffers from negative interference across languages. |
| Approach: | They propose a rescaling fix that reduces the number of parameters and enables training larger hyper-networks. |
| Outcome: | The proposed approach outperforms regular adapters and achieves the same performance with 12 times less parameters. |
SEQˆ3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression (N19-1)
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| Challenge: | Neural sequence-to-sequence models are currently the dominant approach in natural language processing tasks, but require massive parallel corpora. |
| Approach: | They propose a sequence-to-sequence-tosequnce autoencoder with words as latent variables . they apply the model to unsupervised abstractive sentence compression . |
| Outcome: | The proposed model achieves promising results in unsupervised sentence compression on benchmark datasets. |
Exploring Unsupervised Pretraining Objectives for Machine Translation (2021.findings-acl)
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| Challenge: | Unsupervised cross-lingual pretraining has significantly reduced the need for large parallel data. |
| Approach: | They compare unsupervised cross-lingual pretraining with masking and reconstructing inputs in the decoder to produce real sentences. |
| Outcome: | The proposed methods produce inputs resembling real (full) sentences, by reordering and replacing words based on their context. |
Automatic Evaluation and Analysis of Idioms in Neural Machine Translation (2023.eacl-main)
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| Challenge: | Neural machine translation (NMT) struggles with the translation of rare multi-word expressions (MWEs). |
| Approach: | They propose a metric for automatically measuring the frequency of literal translation errors without human involvement. |
| Outcome: | The proposed metric measures the frequency of literal translation errors without human involvement with the models trained in different conditions and across a wide range of metrics and test sets. |