Search Results for author:
Found 75 papers, 18 papers with code
Findings of the WMT Shared Task on Machine Translation Using Terminologies
Language domains that require very careful use of terminology are abundant and reflect a significant part of the translation industry.
FINDINGS OF THE IWSLT 2021 EVALUATION CAMPAIGN
The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2021) featured this year four shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Multilingual speech translation, (iv) Low-resource speech translation.
Machine Translation Human Evaluation: an investigation of evaluation based on Post-Editing and its relation with Direct Assessment
In this paper we present an analysis of the two most prominent methodologies used for the human evaluation of MT quality, namely evaluation based on Post-Editing (PE) and evaluation based on Direct Assessment (DA).
Findings of the IWSLT 2022 Evaluation Campaign
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.
Monolingual Embeddings for Low Resourced Neural Machine Translation
When only little data exist for a language pair, the model cannot produce good representations for words, particularly for rare words.
FBK’s Multilingual Neural Machine Translation System for IWSLT 2017
Particularly, we focus on the four zero-shot directions and show how a multilingual model trained with small data can provide reasonable results.
Overview of the IWSLT 2017 Evaluation Campaign
The IWSLT 2017 evaluation campaign has organised three tasks.
A Statistical Extension of Byte-Pair Encoding
Sub-word segmentation is currently a standard tool for training neural machine translation (MT) systems and other NLP tasks.
Adapting Multilingual NMT to Extremely Low Resource Languages FBK’s Participation in the Basque-English Low-Resource MT Task, IWSLT 2018
In the experimental setting, an extremely low-resourced Basque-English language pair (i. e., ≈ 5. 6K in-domain training data) is our target translation task, where we considered a closely related French/Spanish-English parallel data to build the multilingual model.
The IWSLT 2018 Evaluation Campaign
The International Workshop of Spoken Language Translation (IWSLT) 2018 Evaluation Campaign featured two tasks: low-resource machine translation and speech translation.
The IWSLT 2016 Evaluation Campaign
The IWSLT 2016 Evaluation Campaign featured two tasks: the translation of talks and the translation of video conference conversations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
The IWSLT 2019 Evaluation Campaign
The IWSLT 2019 evaluation campaign featured three tasks: speech translation of (i) TED talks and (ii) How2 instructional videos from English into German and Portuguese, and (iii) text translation of TED talks from English into Czech.
PEAVS: Perceptual Evaluation of Audio-Visual Synchrony Grounded in Viewers' Opinion Scores
Recent advancements in audio-visual generative modeling have been propelled by progress in deep learning and the availability of data-rich benchmarks.
A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism
We show that content on the web is often translated into many languages, and the low quality of these multi-way translations indicates they were likely created using Machine Translation (MT).
End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation
Conventional speech-to-text translation (ST) systems are trained on single-speaker utterances, and they may not generalize to real-life scenarios where the audio contains conversations by multiple speakers.
Speaker Diarization of Scripted Audiovisual Content
The media localization industry usually requires a verbatim script of the final film or TV production in order to create subtitles or dubbing scripts in a foreign language.
Improving Isochronous Machine Translation with Target Factors and Auxiliary Counters
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.
Jointly Optimizing Translations and Speech Timing to Improve Isochrony in Automatic Dubbing
Automatic dubbing (AD) is the task of translating the original speech in a video into target language speech.
A baseline revisited: Pushing the limits of multi-segment models for context-aware translation
This paper addresses the task of contextual translation using multi-segment models.
Improving Robustness of Retrieval Augmented Translation via Shuffling of Suggestions
Several recent studies have reported dramatic performance improvements in neural machine translation (NMT) by augmenting translation at inference time with fuzzy-matches retrieved from a translation memory (TM).
Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions
We explore zero-shot adaptation, where a general-domain model has access to customer or domain specific parallel data at inference time, but not during training.
Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric Into a Document-Level Metric
Our experimental results support our initial hypothesis and show that a simple extension of the metrics permits them to take advantage of context to resolve ambiguities in the reference.
Sockeye 3: Fast Neural Machine Translation with PyTorch
When running comparable models, Sockeye 3 is up to 126% faster than other PyTorch implementations on GPUs and up to 292% faster on CPUs.
CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality
However, in many cases, multiple different translations are valid and the appropriate translation may depend on the intended target audience, characteristics of the speaker, or even the relationship between speakers.
Prosodic Alignment for off-screen automatic dubbing
The goal of automatic dubbing is to perform speech-to-speech translation while achieving audiovisual coherence.
Isochrony-Aware Neural Machine Translation for Automatic Dubbing
We introduce the task of isochrony-aware machine translation which aims at generating translations suitable for dubbing.
Isometric MT: Neural Machine Translation for Automatic Dubbing
Automatic dubbing (AD) is among the machine translation (MT) use cases where translations should match a given length to allow for synchronicity between source and target speech.
Machine Translation Verbosity Control for Automatic Dubbing
Automatic dubbing aims at seamlessly replacing the speech in a video document with synthetic speech in a different language.
Towards Modeling the Style of Translators in Neural Machine Translation
We show that our style-augmented translation models are able to capture the style variations of translators and to generate translations with different styles on new data.
TICO-19: the Translation Initiative for Covid-19
Further, the team is converting the test and development data into translation memories (TMXs) that can be used by localizers from and to any of the languages.
FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN
The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2020) featured this year six challenge tracks: (i) Simultaneous speech translation, (ii) Video speech translation, (iii) Offline speech translation, (iv) Conversational speech translation, (v) Open domain translation, and (vi) Non-native speech translation.
Joint translation and unit conversion for end-to-end localization
A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions.
From Speech-to-Speech Translation to Automatic Dubbing
We present enhancements to a speech-to-speech translation pipeline in order to perform automatic dubbing.
Adapting Multilingual Neural Machine Translation to Unseen Languages
In order to improve NMT for LRL, we employ perplexity to select HRL data that are most similar to the LRL on the basis of language distance.
Controlling the Output Length of Neural Machine Translation
The recent advances introduced by neural machine translation (NMT) are rapidly expanding the application fields of machine translation, as well as reshaping the quality level to be targeted.
Robust Neural Machine Translation for Clean and Noisy Speech Transcripts
Our experimental results on a public speech translation data set show that adapting a model on a significant amount of parallel data including ASR transcripts is beneficial with test data of the same type, but produces a small degradation when translating clean text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
On the Importance of Word Boundaries in Character-level Neural Machine Translation
Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and overall translation quality.
Multilingual Neural Machine Translation for Zero-Resource Languages
In recent years, Neural Machine Translation (NMT) has been shown to be more effective than phrase-based statistical methods, thus quickly becoming the state of the art in machine translation (MT).
Training Neural Machine Translation To Apply Terminology Constraints
This paper proposes a novel method to inject custom terminology into neural machine translation at run time.
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors
Machine translation systems are conventionally trained on textual resources that do not model phenomena that occur in spoken language.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Phonetically-Oriented Word Error Alignment for Speech Recognition Error Analysis in Speech Translation
We propose a variation to the commonly used Word Error Rate (WER) metric for speech recognition evaluation which incorporates the alignment of phonemes, in the absence of time boundary information.
Improving Zero-Shot Translation of Low-Resource Languages
Recent work on multilingual neural machine translation reported competitive performance with respect to bilingual models and surprisingly good performance even on (zeroshot) translation directions not observed at training time.
Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary
Our approach allows to extend an initial model for a given language pair to cover new languages by adapting its vocabulary as long as new data become available (i. e., introducing new vocabulary items if they are not included in the initial model).
Neural Machine Translation into Language Varieties
Both research and commercial machine translation have so far neglected the importance of properly handling the spelling, lexical and grammar divergences occurring among language varieties.
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation
Motivated by this, our work (i) provides a quantitative and comparative analysis of the translations produced by bilingual, multilingual and zero-shot systems; (ii) investigates the translation quality of two of the currently dominant neural architectures in MT, which are the Recurrent and the Transformer ones; and (iii) quantitatively explores how the closeness between languages influences the zero-shot translation.
Deep Neural Machine Translation with Weakly-Recurrent Units
Recurrent neural networks (RNNs) have represented for years the state of the art in neural machine translation.
Compositional Representation of Morphologically-Rich Input for Neural Machine Translation
By training NMT to compose word representations from character n-grams, our approach consistently outperforms (from 1. 71 to 2. 48 BLEU points) NMT learning embeddings of statistically generated sub-word units.
Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English
In this paper, we propose a new vocabulary reduction method for NMT, which can reduce the vocabulary of a given input corpus at any rate while also considering the morphological properties of the language.
Neural vs. Phrase-Based Machine Translation in a Multi-Domain Scenario
State-of-the-art neural machine translation (NMT) systems are generally trained on specific domains by carefully selecting the training sets and applying proper domain adaptation techniques.
Neural versus Phrase-Based Machine Translation Quality: a Case Study
Within the field of Statistical Machine Translation (SMT), the neural approach (NMT) has recently emerged as the first technology able to challenge the long-standing dominance of phrase-based approaches (PBMT).
WAGS: A Beautiful English-Italian Benchmark Supporting Word Alignment Evaluation on Rare Words
This paper presents WAGS (Word Alignment Gold Standard), a novel benchmark which allows extensive evaluation of WA tools on out-of-vocabulary (OOV) and rare words.
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency.
Dynamically Shaping the Reordering Search Space of Phrase-Based Statistical Machine Translation
Defining the reordering search space is a crucial issue in phrase-based SMT between distant languages.
The IWSLT 2011 Evaluation Campaign on Automatic Talk Translation
We report here on the eighth evaluation campaign organized in 2011 by the IWSLT workshop series.
