| Nome |
# |
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Adapting Transformer to End-to-End Spoken Language Translation, file ddb241a5-63a2-ba8a-e053-3a05fe0afd55
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1.838
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MuST-C: a Multilingual Speech Translation Corpus, file ddb241a5-65e8-ba8a-e053-3a05fe0afd55
|
1.227
|
|
Findings of the 2021 Conference on Machine Translation (WMT21), file ddb241a5-84b0-ba8a-e053-3a05fe0afd55
|
525
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|
Findings of the IWSLT 2022 Evaluation Campaign., file 08e4345f-53ff-4862-b29c-b520965bae75
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499
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Neural Text Simplification in Low-Resource Conditions Using Weak Supervision, file ddb241a5-65e6-ba8a-e053-3a05fe0afd55
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361
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|
Findings of the 2016 Conference on Machine Translation., file ddb241a5-3bd2-ba8a-e053-3a05fe0afd55
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358
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Enhancing Transformer for End-to-end Speech-to-Text Translation, file ddb241a5-7176-ba8a-e053-3a05fe0afd55
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279
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Contextual Handling in Neural Machine Translation: Look Behind, Ahead and on Both Sides, file ddb241a5-5ff6-ba8a-e053-3a05fe0afd55
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277
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Online Neural Automatic Post-editing for Neural Machine Translation, file ddb241a5-53bc-ba8a-e053-3a05fe0afd55
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264
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Post-editing in Automatic Subtitling: A Subtitlers’ perspective, file ec2c87bd-10af-47a4-850f-92eb85c255cc
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264
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Improving Zero-Shot Translation of Low-Resource Languages, file ddb241a5-4676-ba8a-e053-3a05fe0afd55
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251
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The IWSLT 2019 Evaluation Campaign, file ddb241a5-6f0b-ba8a-e053-3a05fe0afd55
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241
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Divide and Conquer: Crowdsourcing the Creation of Cross-Lingual Textual Entailment Corpora., file ddb241a5-2e83-ba8a-e053-3a05fe0afd55
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181
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Translation Quality and Productivity: A Study on Rich Morphology Languages., file ddb241a5-4ddd-ba8a-e053-3a05fe0afd55
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167
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Are Subtitling Corpora really Subtitle-like?, file ddb241a5-6f02-ba8a-e053-3a05fe0afd55
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146
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Data Augmentation for End-to-End Speech Translation: FBK@IWSLT '19, file ddb241a5-6ae4-ba8a-e053-3a05fe0afd55
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144
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Neural vs. Phrase-Based Machine Translation in Multi-Domain Scenario, file ddb241a5-4678-ba8a-e053-3a05fe0afd55
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123
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Multilingual Neural Machine Translation for Low-Resource Languages, file ddb241a5-67d5-ba8a-e053-3a05fe0afd55
|
115
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MT Quality Estimation for Computer-assisted Translation: Does it Really Help?, file ddb241a5-2da9-ba8a-e053-3a05fe0afd55
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103
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Continuous Learning from Human Post-Edits for Neural Machine Translation, file ddb241a5-4727-ba8a-e053-3a05fe0afd55
|
103
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eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing, file ddb241a5-5d59-ba8a-e053-3a05fe0afd55
|
89
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Findings of the 2015 Workshop on Statistical Machine Translation, file ddb241a5-2cdb-ba8a-e053-3a05fe0afd55
|
82
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The Multilingual TEDx Corpus for Speech Recognition and Translation, file ddb241a5-8773-ba8a-e053-3a05fe0afd55
|
80
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Automatic quality estimation for ASR system combination, file ddb241a5-472d-ba8a-e053-3a05fe0afd55
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78
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One-to-Many Multilingual End-to-End Speech Translation, file ddb241a5-723c-ba8a-e053-3a05fe0afd55
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76
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The FBK Participation in the WMT15 Automatic Post-editing Shared Task, file ddb241a5-2da6-ba8a-e053-3a05fe0afd55
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68
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Multitask Learning for Adaptive Quality Estimation of Automatically Transcribed Utterances, file ddb241a5-3311-ba8a-e053-3a05fe0afd55
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61
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Driving ROVER with Segment-based ASR Quality Estimation, file ddb241a5-330e-ba8a-e053-3a05fe0afd55
|
58
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The FBK Participation in the WMT 2016 Automatic Post-editing Shared Task, file ddb241a5-4331-ba8a-e053-3a05fe0afd55
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57
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Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation, file ddb241a5-5ff3-ba8a-e053-3a05fe0afd55
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55
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Lexical Modeling of ASR Errors for Robust Speech Translation, file ddb241a5-8795-ba8a-e053-3a05fe0afd55
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55
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On the Dynamics of Gender Learning in Speech Translation, file 4a69d532-2ffe-4d09-91db-3cc419f6a404
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54
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Towards a methodology for evaluating automatic subtitling, file 9b74fb92-ee18-4ac0-bd00-ba184a7ea961
|
53
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Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges, file eb3e0d6c-8d2c-4a16-8464-0da5427a0d59
|
53
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Online Automatic Post-Editing across Domains, file ddb241a5-4681-ba8a-e053-3a05fe0afd55
|
51
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Multi-source Transformer for Automatic Post-Editing, file ddb241a5-53be-ba8a-e053-3a05fe0afd55
|
48
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Using Bilingual Parallel Corpora for Cross-Lingual Textual Entailment, file ddb241a5-2e23-ba8a-e053-3a05fe0afd55
|
46
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FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings, file ddb241a5-37c0-ba8a-e053-3a05fe0afd55
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44
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Online Automatic Post-editing for MT in a Multi-Domain Translation Environment, file ddb241a5-472c-ba8a-e053-3a05fe0afd55
|
43
|
|
Towards a Combination of Online and Multitask Learning for MT Quality Estimation: a Preliminary Study., file ddb241a5-3776-ba8a-e053-3a05fe0afd55
|
42
|
|
An Unsupervised Method for Automatic Translation Memory Cleaning., file ddb241a5-3f63-ba8a-e053-3a05fe0afd55
|
41
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Adapting Multilingual Neural Machine Translation to Unseen Languages, file ddb241a5-723e-ba8a-e053-3a05fe0afd55
|
35
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On the development of an information system for monitoring user opinion and its role for the public, file 2fed11cc-7005-4a1e-a372-143ae2e3987d
|
32
|
|
Exploring the Planet of the APEs: a Comparative Study of State-of-the-art Methods for MT Automatic Post-Editing, file ddb241a5-3313-ba8a-e053-3a05fe0afd55
|
32
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Multi-source transformer with combined losses for automatic post editing, file ddb241a5-5458-ba8a-e053-3a05fe0afd55
|
30
|
|
Improving Translations by Combining Fuzzy-Match Repair with Automatic Post-Editing, file ddb241a5-7178-ba8a-e053-3a05fe0afd55
|
30
|
|
Quality Estimation for Automatic Speech Recognition, file ddb241a5-1b56-ba8a-e053-3a05fe0afd55
|
28
|
|
FBK’s Multilingual Neural Machine Translation System for IWSLT 2017, file ddb241a5-4770-ba8a-e053-3a05fe0afd55
|
28
|
|
Zero-Shot Neural Machine Translation with Self-Learning Cycle, file ddb241a5-837c-ba8a-e053-3a05fe0afd55
|
28
|
|
Does Simultaneous Speech Translation need Simultaneous Models?, file 362aff82-c301-4d9a-a798-2b6a9accb101
|
27
|
|
Fine-tuning on Clean Data for End-to-End Speech Translation: FBK @ IWSLT 2018, file ddb241a5-5d67-ba8a-e053-3a05fe0afd55
|
25
|
|
Findings of the WMT 2019 Shared Task on Automatic Post-Editing., file ddb241a5-6649-ba8a-e053-3a05fe0afd55
|
24
|
|
Machine Translation for Machines: the Sentiment Classification Use Case, file ddb241a5-69ef-ba8a-e053-3a05fe0afd55
|
24
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|
1st Shared Task on Automatic Translation Memory Cleaning: Preparation and Lessons Learned, file ddb241a5-37ad-ba8a-e053-3a05fe0afd55
|
23
|
|
The MateCat Tool, file ddb241a5-1b54-ba8a-e053-3a05fe0afd55
|
21
|
|
Findings of the 2017 Conference on Machine Translation (WMT17), file ddb241a5-4de0-ba8a-e053-3a05fe0afd55
|
21
|
|
TranscRater: a Tool for Automatic Speech Recognition Quality Estimation, file ddb241a5-40dc-ba8a-e053-3a05fe0afd55
|
20
|
|
FBK Participation in the RTE-7 Main Task, file ddb241a5-46ee-ba8a-e053-3a05fe0afd55
|
20
|
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Instance Selection forOnline Automatic Post-Editing in a multi-domain scenario., file ddb241a5-432f-ba8a-e053-3a05fe0afd55
|
19
|
|
Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English., file ddb241a5-44aa-ba8a-e053-3a05fe0afd55
|
19
|
|
Effort-Aware Neural Automatic Post-Editing, file ddb241a5-6e8f-ba8a-e053-3a05fe0afd55
|
18
|
|
Multi-source Transformer for AutomaticPost-Editing of Machine Translation Output, file ddb241a5-6f10-ba8a-e053-3a05fe0afd55
|
18
|
|
MuST-Cinema: a Speech-to-Subtitles corpus, file ddb241a5-70cf-ba8a-e053-3a05fe0afd55
|
18
|
|
Extending the MuST-C Corpus for a Comparative Evaluation of Speech Translation Technology, file 6168974d-d09f-494b-8df2-95542dabfcd9
|
16
|
|
Match without a Referee: Evaluating MT Adequacy without Reference Translations, file ddb241a5-1beb-ba8a-e053-3a05fe0afd55
|
16
|
|
Findings of the WMT 2018 Shared Task on Automatic Post-Editing, file ddb241a5-5454-ba8a-e053-3a05fe0afd55
|
15
|
|
Forecasting the IBEX-35 stock index using deep learning and news emotions, file ddb241a5-871c-ba8a-e053-3a05fe0afd55
|
15
|
|
Multi-Domain Neural Machine Translation through Unsupervised Adaptation., file ddb241a5-4edd-ba8a-e053-3a05fe0afd55
|
14
|
|
Detecting Semantic Equivalence and Information Disparity in Cross-Lingual Documents, file ddb241a5-1bea-ba8a-e053-3a05fe0afd55
|
13
|
|
Online Multitask Learning for Machine Translation Quality Estimation, file ddb241a5-2c9d-ba8a-e053-3a05fe0afd55
|
13
|
|
Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?, file ddb241a5-7e43-ba8a-e053-3a05fe0afd55
|
13
|
|
CoSyne: A Framework for Multilingual Content Synchronization of Wikis, file ddb241a5-4e18-ba8a-e053-3a05fe0afd55
|
11
|
|
Guiding Neural Machine Translation Decoding with External Knowledge, file ddb241a5-4eda-ba8a-e053-3a05fe0afd55
|
11
|
|
Findings of the IWSLT 2021 Evaluation Campaign, file ddb241a5-87ac-ba8a-e053-3a05fe0afd55
|
11
|
|
Semeval-2012 Task 8: Cross-lingual Textual Entailment for Content Synchronization., file ddb241a5-1be7-ba8a-e053-3a05fe0afd55
|
10
|
|
FBK: Combining Machine Translation Evaluation and Word Similarity metrics for Semantic Textual Similarity, file ddb241a5-1be8-ba8a-e053-3a05fe0afd55
|
10
|
|
Combining Quality Estimation and Automatic Post-editing to Enhance Machine Translation Output, file ddb241a5-5a16-ba8a-e053-3a05fe0afd55
|
10
|
|
Breeding Gender-aware Direct Speech Translation Systems, file ddb241a5-7c5d-ba8a-e053-3a05fe0afd55
|
10
|
|
TMop: a Tool for Unsupervised Translation Memory Cleaning, file ddb241a5-40da-ba8a-e053-3a05fe0afd55
|
9
|
|
On Knowledge Distillation for Direct Speech Translation, file ddb241a5-7502-ba8a-e053-3a05fe0afd55
|
9
|
|
Chinese Whispers: Cooperative Paraphrase Acquisition, file ddb241a5-1bec-ba8a-e053-3a05fe0afd55
|
8
|
|
CTC-based Compression for Direct Speech Translation, file ddb241a5-7e46-ba8a-e053-3a05fe0afd55
|
8
|
|
How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation, file ddb241a5-81c9-ba8a-e053-3a05fe0afd55
|
8
|
|
Dodging the Data Bottleneck: Automatic Subtitling with Automatically Segmented ST Corpora, file 07bdb5d2-4a5a-4d51-8ee6-35e58e4a567b
|
7
|
|
Direct Speech-to-Text Translation Models as Students of Text-to-Text Models, file 5c9eb45d-7b77-4df5-8b26-d413f3217c4e
|
7
|
|
Over-Generation Cannot Be Rewarded: Length-Adaptive Average Lagging for Simultaneous Speech Translation, file 6ddcd09a-8ef9-40cf-bd0b-36999bfb9aa9
|
7
|
|
Multi-source Neural Automatic Post-Editing: FBK’s participation in the WMT 2017 APE shared task, file ddb241a5-4de6-ba8a-e053-3a05fe0afd55
|
7
|
|
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020, file ddb241a5-7ce3-ba8a-e053-3a05fe0afd55
|
7
|
|
Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation, file bab88071-db3d-4eeb-8ea3-e1eafc806567
|
6
|
|
Machine Translation Quality Estimation Across Domains, file ddb241a5-1b55-ba8a-e053-3a05fe0afd55
|
6
|
|
Who Are We Talking About? Handling Person Names in Speech Translation, file 37d59b37-ea8b-4602-87fd-5fbd5a0b7d1a
|
5
|
|
Efficient yet Competitive Speech Translation: FBK@IWSLT2022, file 68c7f65a-119b-4e68-bb1c-3b3f162f14e5
|
5
|
|
How To Build Competitive Multi-gender Speech Translation Models For Controlling Speaker Gender Translation, file 126864ac-31da-4d44-8caf-468ff68c52ca
|
4
|
|
Towards Cross-Lingual Textual Entailment, file ddb241a5-1731-ba8a-e053-3a05fe0afd55
|
4
|
|
Is "moby dick" a Whale or a Bird? Named Entities and Terminology in Speech Translation, file ddb241a5-82a9-ba8a-e053-3a05fe0afd55
|
4
|
|
Integrating Language Models into Direct Speech Translation: An Inference-Time Solution to Control Gender Inflection, file e6fc2c5d-f1b2-4694-bbad-271d21b506ec
|
4
|
|
Direct Speech Translation for Automatic Subtitling, file 0e39a3a7-f2df-4e31-8f55-c2806700baa2
|
3
|
|
An Open-Source Package for Recognizing Textual Entailment, file ddb241a5-1733-ba8a-e053-3a05fe0afd55
|
3
|
|
Test Suites Task: Evaluation of Gender Fairness in MT with MuST-SHE and INES, file 133c326b-7ed2-463e-818b-4234ddc5afc1
|
2
|
|
FBK’s Neural Machine Translation Systems for IWSLT 2016, file cd84a27b-f93d-4244-b417-8e4b72a09bd8
|
2
|
| Totale |
9.553 |