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Found 46 papers, 16 papers with code
Towards a methodology for evaluating automatic subtitling
In response to the increasing interest towards automatic subtitling, this EAMT-funded project aimed at collecting subtitle post-editing data in a real use case scenario where professional subtitlers edit automatically generated subtitles.
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).
CEF Data Marketplace: Powering a Long-term Supply of Language Data
We describe the CEF Data Marketplace project, which focuses on the development of a trading platform of translation data for language professionals: translators, machine translation (MT) developers, language service providers (LSPs), translation buyers and government bodies.
Extending the MuST-C Corpus for a Comparative Evaluation of Speech Translation Technology
This project aimed at extending the test sets of the MuST-C speech translation (ST) corpus with new reference translations.
Post-editing in Automatic Subtitling: A Subtitlers’ perspective
Subtitling tools are recently being adapted for post-editing by providing automatically generated subtitles, and featuring not only machine translation, but also automatic segmentation and synchronisation.
Is “moby dick” a Whale or a Bird? Named Entities and Terminology in Speech Translation
Automatic translation systems are known to struggle with rare words.
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.
Overview of the IWSLT 2017 Evaluation Campaign
The IWSLT 2017 evaluation campaign has organised three tasks.
On the Dynamics of Gender Learning in Speech Translation
In this work, we contribute to such a line of inquiry by exploring the emergence of gender bias in Speech Translation (ST).
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
SimulSeamless: FBK at IWSLT 2024 Simultaneous Speech Translation
This paper describes the FBK's participation in the Simultaneous Translation Evaluation Campaign at IWSLT 2024.
StreamAtt: Direct Streaming Speech-to-Text Translation with Attention-based Audio History Selection
To fill this gap, we introduce StreamAtt, the first StreamST policy, and propose StreamLAAL, the first StreamST latency metric designed to be comparable with existing metrics for SimulST.
SBAAM! Eliminating Transcript Dependency in Automatic Subtitling
Subtitling plays a crucial role in enhancing the accessibility of audiovisual content and encompasses three primary subtasks: translating spoken dialogue, segmenting translations into concise textual units, and estimating timestamps that govern their on-screen duration.
Enhancing Gender-Inclusive Machine Translation with Neomorphemes and Large Language Models
In this direction, we explore prompting techniques with large language models (LLMs) to translate from English into Italian using neomorphemes.
How do Hyenas deal with Human Speech? Speech Recognition and Translation with ConfHyena
The attention mechanism, a cornerstone of state-of-the-art neural models, faces computational hurdles in processing long sequences due to its quadratic complexity.
Speech Translation with Speech Foundation Models and Large Language Models: What is There and What is Missing?
The field of natural language processing (NLP) has recently witnessed a transformative shift with the emergence of foundation models, particularly Large Language Models (LLMs) that have revolutionized text-based NLP.
A Prompt Response to the Demand for Automatic Gender-Neutral Translation
Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a pivotal challenge for the creation of more inclusive translation technologies.
Test Suites Task: Evaluation of Gender Fairness in MT with MuST-SHE and INES
As part of the WMT-2023 "Test suites" shared task, in this paper we summarize the results of two test suites evaluations: MuST-SHE-WMT23 and INES.
Integrating Language Models into Direct Speech Translation: An Inference-Time Solution to Control Gender Inflection
When translating words referring to the speaker, speech translation (ST) systems should not resort to default masculine generics nor rely on potentially misleading vocal traits.
How To Build Competitive Multi-gender Speech Translation Models For Controlling Speaker Gender Translation
When translating from notional gender languages (e. g., English) into grammatical gender languages (e. g., Italian), the generated translation requires explicit gender assignments for various words, including those referring to the speaker.
No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulation
Automatic speech recognition (ASR) systems are known to be sensitive to the sociolinguistic variability of speech data, in which gender plays a crucial role.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus
Gender inequality is embedded in our communication practices and perpetuated in translation technologies.
Good, but not always Fair: An Evaluation of Gender Bias for three commercial Machine Translation Systems
Machine Translation (MT) continues to make significant strides in quality and is increasingly adopted on a larger scale.
Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges
Gender inclusivity in language technologies has become a prominent research topic.
Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation
Gender bias is largely recognized as a problematic phenomenon affecting language technologies, with recent studies underscoring that it might surface differently across languages.
Is "moby dick" a Whale or a Bird? Named Entities and Terminology in Speech Translation
Automatic translation systems are known to struggle with rare words.
Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?
Five years after the first published proofs of concept, direct approaches to speech translation (ST) are now competing with traditional cascade solutions.
How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation
In light of this finding, we propose a combined approach that preserves BPE overall translation quality, while leveraging the higher ability of character-based segmentation to properly translate gender.
Gender Bias in Machine Translation
Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, elaborating and communicating information.
Breeding Gender-aware Direct Speech Translation Systems
In particular, by translating speech audio data without intermediate transcription, direct ST models are able to leverage and preserve essential information present in the input (e. g. speaker's vocal characteristics) that is otherwise lost in the cascade framework.
Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus
Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines.
Machine Translation for Machines: the Sentiment Classification Use Case
Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback.
MuST-C: a Multilingual Speech Translation Corpus
Current research on spoken language translation (SLT) has to confront with the scarcity of sizeable and publicly available training corpora.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
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 SICK cure for the evaluation of compositional distributional semantic models
Shared and internationally recognized benchmarks are fundamental for the development of any computational system.
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.
Chinese Whispers: Cooperative Paraphrase Acquisition
We present a framework for the acquisition of sentential paraphrases based on crowdsourcing.
