Papers by Matthias Paulik
Empirical Evaluation of Active Learning Techniques for Neural MT (D19-61)
Copied to clipboard
| Challenge: | Several active learning (AL) algorithms for machine translation (MT) have been well-studied for phrase-based MT. |
| Approach: | They propose to use a phrase-based algorithm to compare different AL methods in a simulated AL framework to demonstrate how unsupervised pre-training and paraphrastic embedding can be used to improve existing AL methods. |
| Outcome: | The proposed method outperforms existing methods in the context of phrase-based MT and is based on a simulated phrase-driven dataset. |
Toward Machine Interpreting: Lessons from Human Interpreting Studies (2025.emnlp-main)
Copied to clipboard
| Challenge: | Current speech translation systems are static and do not adapt to real-world situations in ways human interpreters do. |
| Approach: | They propose to model human interpreting using a new language model to improve usability . they argue that there is great potential to adopt many human interpreted principles . |
| Outcome: | The proposed models can be used to improve human interpreting and improve translation performance. |
Variational Neural Machine Translation with Normalizing Flows (2020.acl-main)
Copied to clipboard
| Challenge: | Existing frameworks for learning informative latent variables are limited by limitations . existing models rely on strong assumptions on distribution of latent code . |
| Approach: | They propose to apply a variational neural machine translation framework to a Transformer . they propose to introduce a more flexible approximate posterior based on normalizing flows . |
| Outcome: | The proposed framework outperforms baseline models under in-domain and out-of-domain conditions. |
Jointly Learning to Align and Translate with Transformer Models (D19-1)
Copied to clipboard
| Challenge: | Existing word alignment models are not accurate for word alignments. |
| Approach: | They propose a method to train a Transformer model to produce accurate translations and alignments. |
| Outcome: | The proposed model outperforms GIZA++ trained models on translation and alignment tasks while maintaining translation accuracy. |
Consistent Transcription and Translation of Speech (2020.tacl-1)
Copied to clipboard
| Challenge: | Existing models that translate without transcribing focus on translation quality, while transcription receives less emphasis. |
| Approach: | They propose a method to evaluate consistency and compare different approaches . they propose 'coupled inference' models that feature a coupled inference procedure can achieve strong consistency. |
| Outcome: | The proposed model is poorly suited to the joint transcription/translation task, but is strong enough to train for consistency. |
Speech Translation and the End-to-End Promise: Taking Stock of Where We Are (2020.acl-main)
Copied to clipboard
| Challenge: | Until recently, the only feasible approach to translating acoustic speech signals into text was the cascaded approach. |
| Approach: | They propose a classification of the main challenges of traditional approaches to speech translation . they argue that end-to-end models fall short due to compromises made to address data scarcity . |
| Outcome: | This paper provides a brief survey of the main challenges of traditional approaches in speech translation . it reveals that many end-to-end models fail due to compromises made to address data scarcity. |
End-to-End Speech Translation for Code Switched Speech (2022.findings-acl)
Copied to clipboard
Orion Weller, Matthias Sperber, Telmo Pires, Hendra Setiawan, Christian Gollan, Dominic Telaar, Matthias Paulik
| Challenge: | Code switching (CS) is the phenomenon of interchangeably using words and phrases from different languages. |
| Approach: | They propose a new ST corpus that extends the joint transcription and translation setup. |
| Outcome: | The proposed model performs well even when no training data is used. |