Search Results for author:
Found 24 papers, 1 papers with code
Automatic Video Dubbing at AppTek
The revoicing usually comes with a changed script, mostly in a different language, and the revoicing should reproduce the original emotions, coherent with the body language, and lip synchronized.
Neural Speech Translation at AppTek
This work describes AppTek’s speech translation pipeline that includes strong state-of-the-art automatic speech recognition (ASR) and neural machine translation (NMT) components.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Without Further Ado: Direct and Simultaneous Speech Translation by AppTek in 2021
This paper describes the offline and simultaneous speech translation systems developed at AppTek for IWSLT 2021.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
SubER - A Metric for Automatic Evaluation of Subtitle Quality
This paper addresses the problem of evaluating the quality of automatically generated subtitles, which includes not only the quality of the machine-transcribed or translated speech, but also the quality of line segmentation and subtitle timing.
AppTek’s Submission to the IWSLT 2022 Isometric Spoken Language Translation Task
To participate in the Isometric Spoken Language Translation Task of the IWSLT 2022 evaluation, constrained condition, AppTek developed neural Transformer-based systems for English-to-German with various mechanisms of length control, ranging from source-side and target-side pseudo-tokens to encoding of remaining length in characters that replaces positional encoding.
AppTek's Submission to the IWSLT 2022 Isometric Spoken Language Translation Task
To participate in the Isometric Spoken Language Translation Task of the IWSLT 2022 evaluation, constrained condition, AppTek developed neural Transformer-based systems for English-to-German with various mechanisms of length control, ranging from source-side and target-side pseudo-tokens to encoding of remaining length in characters that replaces positional encoding.
SubER: A Metric for Automatic Evaluation of Subtitle Quality
This paper addresses the problem of evaluating the quality of automatically generated subtitles, which includes not only the quality of the machine-transcribed or translated speech, but also the quality of line segmentation and subtitle timing.
Start-Before-End and End-to-End: Neural Speech Translation by AppTek and RWTH Aachen University
AppTek and RWTH Aachen University team together to participate in the offline and simultaneous speech translation tracks of IWSLT 2020.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Neural Simultaneous Speech Translation Using Alignment-Based Chunking
In simultaneous machine translation, the objective is to determine when to produce a partial translation given a continuous stream of source words, with a trade-off between latency and quality.
Novel Applications of Factored Neural Machine Translation
In this work, we explore the usefulness of target factors in neural machine translation (NMT) beyond their original purpose of predicting word lemmas and their inflections, as proposed by Garc\`ia-Mart\`inez et al., 2016.
Customizing Neural Machine Translation for Subtitling
In this work, we customized a neural machine translation system for translation of subtitles in the domain of entertainment.
Generating E-Commerce Product Titles and Predicting their Quality
The setting requires the generation process to be fast and the generated title to be both human-readable and concise.
Learning from Chunk-based Feedback in Neural Machine Translation
We empirically investigate learning from partial feedback in neural machine translation (NMT), when partial feedback is collected by asking users to highlight a correct chunk of a translation.
Can Neural Machine Translation be Improved with User Feedback?
We present the first real-world application of methods for improving neural machine translation (NMT) with human reinforcement, based on explicit and implicit user feedback collected on the eBay e-commerce platform.
Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search
In this paper, we introduce a hybrid search for attention-based neural machine translation (NMT).
Neural and Statistical Methods for Leveraging Meta-information in Machine Translation
In this paper, we discuss different methods which use meta information and richer context that may accompany source language input to improve machine translation quality.
Using Images to Improve Machine-Translating E-Commerce Product Listings.
We evaluate our models quantitatively using BLEU and TER and find that (i) additional synthetic data has a general positive impact on text-only and multi-modal NMT models, and that (ii) using a multi-modal NMT model for re-ranking n-best lists improves TER significantly across different n-best list sizes.
Human Evaluation of Multi-modal Neural Machine Translation: A Case-Study on E-Commerce Listing Titles
Nonetheless, human evaluators ranked translations from a multi-modal NMT model as better than those of a text-only NMT over 88{\%} of the time, which suggests that images do help NMT in this use-case.
Guided Alignment Training for Topic-Aware Neural Machine Translation
In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models.
