18 citations · 36 across the 15 of their papers we have counts for
15 papers · 1 filter
Language and Task Arithmetic with Parameter-Efficient Layers for Zero-Shot Summarization
Alexandra Chronopoulou, Jonas Pfeiffer, Joshua Maynez +3
Parameter-efficient fine-tuning (PEFT) using labeled task data can significantly improve the performance of large language models (LLMs) on the downstream task. However, there are…
On the Copying Problem of Unsupervised NMT: A Training Schedule with a Language Discriminator Loss
Yihong Liu, Alexandra Chronopoulou, Hinrich Schütze +1
Although unsupervised neural machine translation (UNMT) has achieved success in many language pairs, the copying problem, i.e., directly copying some parts of the input sentence as…
Improving Isochronous Machine Translation with Target Factors and Auxiliary Counters
Proyag Pal, Brian Thompson, Yogesh Virkar +3
To translate speech for automatic dubbing, machine translation needs to be isochronous, i.e. translated speech needs to be aligned with the source in terms of speech durations. We…
Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine Translation
Wen Lai, Alexandra Chronopoulou, Alexander Fraser
Despite advances in multilingual neural machine translation (MNMT), we argue that there are still two major challenges in this area: data imbalance and representation degeneration.…
AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models
Alexandra Chronopoulou, Matthew E. Peters, Alexander Fraser +1
Pretrained language models (PLMs) are trained on massive corpora, but often need to specialize to specific domains. A parameter-efficient adaptation method suggests training an ada…
Jointly Optimizing Translations and Speech Timing to Improve Isochrony in Automatic Dubbing
Alexandra Chronopoulou, Brian Thompson, Prashant Mathur +3
Automatic dubbing (AD) is the task of translating the original speech in a video into target language speech. The new target language speech should satisfy isochrony; that is, the…
: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter
Wen Lai, Alexandra Chronopoulou, Alexander Fraser
Multilingual neural machine translation models (MNMT) yield state-of-the-art performance when evaluated on data from a domain and language pair seen at training time. However, when…
Language-Family Adapters for Low-Resource Multilingual Neural Machine Translation
Alexandra Chronopoulou, Dario Stojanovski, Alexander Fraser
Large multilingual models trained with self-supervision achieve state-of-the-art results in a wide range of natural language processing tasks. Self-supervised pretrained models are…
Efficient Hierarchical Domain Adaptation for Pretrained Language Models
Alexandra Chronopoulou, Matthew E. Peters, Jesse Dodge
The remarkable success of large language models has been driven by dense models trained on massive unlabeled, unstructured corpora. These corpora typically contain text from divers…
Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation
Alexandra Chronopoulou, Dario Stojanovski, Alexander Fraser
Successful methods for unsupervised neural machine translation (UNMT) employ crosslingual pretraining via self-supervision, often in the form of a masked language modeling or a seq…
The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task
Alexandra Chronopoulou, Dario Stojanovski, Viktor Hangya +1
This paper describes the submission of LMU Munich to the WMT 2020 unsupervised shared task, in two language directions, German<->Upper Sorbian. Our core unsupervised neural machine…
Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT
Alexandra Chronopoulou, Dario Stojanovski, Alexander Fraser
Using a language model (LM) pretrained on two languages with large monolingual data in order to initialize an unsupervised neural machine translation (UNMT) system yields state-of-…
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models
Alexandra Chronopoulou, Christos Baziotis, Alexandros Potamianos
A growing number of state-of-the-art transfer learning methods employ language models pretrained on large generic corpora. In this paper we present a conceptually simple and effect…
NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification
Alexandra Chronopoulou, Aikaterini Margatina, Christos Baziotis +1
In this paper we present our approach to tackle the Implicit Emotion Shared Task (IEST) organized as part of WASSA 2018 at EMNLP 2018. Given a tweet, from which a certain word has…
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning
Christos Baziotis, Nikos Athanasiou, Alexandra Chronopoulou +5
In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets". We participated in all subtasks for English tweets. We prop…