ANNOUNCEMENTS

Description

The advent of large language models (LLMs) offers unprecedented opportunities to address traditional natural language processing (NLP) tasks in real-world scenarios and under diverse data conditions. Spoken Language Translation (SLT), which involves automatically translating spoken audio into text in a different language, is no exception thanks to the possibility to fine-tune powerful LLMs for specific tasks, domains, and languages, or to employ them in zero-shot settings when suitable adaptation data is unavailable.

The goal of the Offline Speech Translation Task at IWSLT, the one with the longest-standing tradition at the conference, is to provide a stable evaluation framework for tracking technological advancements in SLT, with a focus on unconstrained speech translation—free from the temporal and structural constraints imposed by tasks such as simultaneous translation or subtitling. To this end, while maintaining the overall task formulation is essential, over the years the emphasis has shifted towards incrementally raising the task’s difficulty to better reflect real-world needs, including the translation of new and diverse languages, domains, and speaking styles.

In this spirit, this year’s edition aims to:

  • include a new and challenging language, Arabic;
  • offer a varied scenario in terms of domains (news, physical training sessions, and TV series), speaking styles, and recording conditions (e.g., single speakers, multiple overlapping speakers, background noise, accent data);
  • promote the development and use of flexible systems capable of operating in this multi-domain scenario, without resorting to ad-hoc, domain-specialized models.

Similar to last year, the task will allow users to submit custom extensions (i.e. test suites) to standard offline test sets. These sets are designed to focus on specific aspects of the SLT output that traditional evaluation methods typically overlook.

Similarly to last year, three language directions are proposed in the offline task. Each language direction will be tested in different evaluation scenarios:

  • English -> German: TV series, scientific presentations, business news, and accent challenge data.
  • English -> Arabic: business news.
  • English -> Chinese: scientific presentations.

The test sets are totally or partially shared with other tasks (e.g. the subtitling task).

The system’s performance will be evaluated with respect to its capability to produce translations similar to the target-language references. Such similarity will be measured in terms of multiple automatic metrics: COMET, BLEURT, BLEU, TER, and characTER. The submitted runs will be ranked based on the COMET calculated on the test set by using automatic resegmentation of the hypothesis based on the reference translation by mwerSegmenter. The detailed evaluation script can be found in the SLT.KIT. Moreover, to meet the requests of last year’s participants, a human evaluation will be performed on each participant’s best-performing submission.

Evaluation Conditions

Both cascade and end-to-end models will be evaluated. We kindly ask each participant to specify at submission time if a cascade or an end-to-end model has been used.

In this task, we use the following definition of end-to-end model:

  • No intermediate discrete representations (e.g., source language transcripts like in cascade or target languages like in rover)
  • All parameters/parts that are used during decoding need to be trained on the end2end task (may also be trained on other tasks -> multitasking ok, LM rescoring is not ok)

All the systems will be evaluated using a combination of the different test tests (depending on the language directions) and each specific test suite, if any. It is important to note that all the test sets will be released together, but specific information to identify the different test sets will be associated with the data. Each audio file will have a clear identifier of the type of data: News_1.wav, ACL_1.wav, Press_1.wav. More detailed information will be released with the test sets.

Test Data

All test data can be downloaded from the SPEECHM Evaluation Server, see Submission STEP 0 below.

Test Suite

Test suites are custom extensions to standard offline test sets constructed so that they can focus on particular aspects of the SLT output. The goal of the test suite is to investigate specific aspects that are generally omitted by the classic evaluation strategies. Test suites also evaluate these aspects in their custom way. The particular test suite composition and its evaluation are fully on the test suite provider.

If you are interested in submitting a test suite, please send us a link to the data including the audio and a textual file describing the goal of the test suite. The format of the audio files is similar to the format of the test audio in the previous editions: a folder with the WAV files and a textual file containing the order in which the audio files will be processed. To share the test suite link, please use the following email: iwslt_offline_task_submission@fbk.eu

All the test suites will then be merged and made available to the participants in the test set section. Once the translations are received, they will be split according to the test suites and forwarded to the owners of the test suites. An evaluation is expected to be performed on time to be included in the findings paper.

Important date:

  • The test suite should be submitted by the 1st of March.

For more information about the test suite: iwslt-evaluation-campaign@googlegroups.com

Past Editions Development Data

The development data is not segmented using the reference transcript. The archives contain segmentation into sentence-like segmentation using automatic tools. However, the participants might also use a different segmentation. The data is provided as an archive with the following files:

  • $set.en-de.en.xml: Reference transcript (will not be provided for evaluation data)
  • $set.en-de.en.xml: Reference translation (will not be provided for evaluation data)
  • CTM_LIST: Ordered file list containing the ASR Output CTM Files (will not be provided for evaluation data) (Generated by ASR systems that use more data)
  • FILE_ORDER: Ordered file list containing the wav files
  • $set.yaml: This file contains the time steps for sentence-like segments. It is generated by the LIUM Speaker Diarization tool.
  • $set.h5: This file contains the 40-dimensional Filterbank features for each sentence-like segment of the test data created by XNMT.
  • The last two files are created by the following command: python -m xnmt.xnmt_run_experiments /opt/SLT.KIT/scripts/xnmt/config.las-pyramidal-preproc.yaml

Training Data and Data Conditions

A “constrained” setup is proposed as the official training data condition, in which the allowed training data is limited to a medium-sized framework in order to keep the training time and resource requirements manageable. In order to allow participants to leverage large language models and medium-sized resources, we propose a “constrained with large language models” condition, where participants can use the training data allowed in the constrained condition plus any additional LLMS as long as it is released under a permissive license. In order to allow the participation of teams equipped with high computational power and effective in-house solutions built on additional resources, an “unconstrained” setup without data restrictions is also proposed.

  • Constrained training: Under this condition, the allowed training resources are the following ones (note that the list does not include any pre-trained language model):
Data type src lang tgt lang Training corpus (URL) Version Comment
speech en – LibriSpeech ASR corpus v12 includes translations into pt, not to be used
speech en – How2 na  
speech en – Mozilla Common Voice v11.0  
speech en – Vox Populi na only translation, no transcription
speech-to-text-parallel en de, ar, zh CoVoST v2  
speech-to-text-parallel en de Europarl-ST v1.1  
text-parallel en ar UNPC v1.0  
text-parallel en de Europarl v10  
text-parallel en ar Tanzil v1  
text-parallel en zh, de, ar NewsCommentary v18  
text-parallel en ar GlobalVoices v2018q4  
text-parallel en ar, zh, de OpenSubtitles v2018  
text-parallel en de OpenSubtitles v2018 apptek partially re-aligned, filtered, with document meta-information on genre
text-parallel en ar, zh, de Tatoeba v2023-04-12  
text-parallel en ar ELRC_2922 v1  
text-parallel en de ELRC-CORDIS_News v1  
text-monolingual – de OpenSubtitles with subtitle breaks v2018-apptek superset of parallel data, with subtitle breaks and document meta-info on genre, automatically predicted line breaks

Note: this list is identical to the one available in the subtitle task. Some training data are specific for the subtitling task including subtitle boundaries (<eob> and <eol>).

  • Constrained with Large Language Models training: Under this condition, all the constrained resources plus freely accessible large language models released under a permissive license are allowed.

  • Unconstrained training: any resource, pre-trained language models included, can be used with the exception of evaluation sets

Submission Guidelines

This year, the evaluation will be performed using the MeeTween SPEECHM centralized evaluation server: SPEECHM Evaluation Server.

General Guidelines

  • Multiple run submissions are allowed, but participants must explicitly indicate one PRIMARY run for each track. All other run submissions are treated as CONTRASTIVE runs. In the case that none of the runs is marked as PRIMARY, the latest submission (according to the file time-stamp) for the respective track will be used as the PRIMARY run.

  • Scoring will be case-sensitive and will include punctuation. Submissions have to be in plain UTF-8 text format, with one sentence per line. Tags such as applause, laughing, etc are not considered during the evaluation.

  • Once logged in to the SPEECHM Evaluation Server, the submission process requires participants to create one or more Models for each language pair they intend to participate in (English-German, English-Arabic, English-Chinese).

  • For each chosen language pair, multiple Models can be created based on the training condition (CONSTRAINED / UNCONSTRAINED) and the submission type (PRIMARY / CONTRASTIVE).

  • The created Model(s) must be used to submit runs for each of the test sets released for the chosen language pair (i.e., 1 test set for English-Arabic and English-Chinese, and 4 test sets for English-German).

  • If any issues are identified, the submitted runs can be deleted or replaced with newer runs.

Submission Steps

Once logged in to SPEECHM, proceed through the following two steps.

STEP 0: Download and process the test data

0.1 Click on “Test sets” (at the top of the page).
0.2 Click on the “offline” button associated with any of the visible test sets in the list.
0.3 Download ALL the test sets for the language pair(s) chosen for participation  (4 test sets for en-de, 1 test set for en-ar, 1 test set for en-zh).
0.4 Process the test data to obtain your candidate submission file (to be stored in plain UTF-8 text format, one sentence per line)

STEP 1: Create a New Model

1.1 Click on “My submissions” (at the top of the page).
1.2 Click on “New model” (button at the top right).
1.3 Create a new model:
   Insert the Model Name using the standardized format:
   
     ${TEAM}_IWSLT25_Offline_${LANGUAGE_PAIR}_${CONDITION}_${SUBMISSION_TYPE}
     
      Where:
       - ${TEAM} → Short name of your team (e.g., KIT)
       - ${LANGUAGE_PAIR} → Choose from [en-de, en-ar, en-zh]
       - ${CONDITION} → Choose from [constrained, unconstrained]
       - ${SUBMISSION_TYPE} → Choose from [primary, contrastive]

       Example Model Names:
         KIT_IWSLT25_Offline_en-de_constrained_primary  
         KIT_IWSLT25_Offline_en-de_constrained_contrastive 
 1.4 Insert Description
   Provide a brief but accurate description of your model, including:
      - General approach (e.g., cascade / end-to-end)
      - Training data used
      - Model architecture
      - Any relevant features characterizing your approach
 1.5 Consent Option (optional)
   Consider enabling “Consents” to freely release your submitted system output data.
 1.6 Select Task Compatibility
   Choose the Offline Task Id in the compatibility map.
 1.7 Click “Create Model” (a “Model created” message will appear on the top right).

STEP 2: Submit Your Processed Test Set

2.1 Go to “My Submissions”.
2.2 Click on the specific model created in STEP 1 
   (e.g., KIT_IWSLT25_Offline_en-de_constrained_primary).
2.3 Click the “OFFLINE Hypotheses” button.
2.4 Once you have generated the outputs with your model for each test set, click “Upload hypothesis” for the intended submission:
   ${TESTSET} / ${LANGUAGE_PAIR} (e.g., IWSLT25INSTRUCT / en-de)
2.5 Upload your submission file (plain UTF-8 text format, one sentence per line).

Manage Your Submission

Download or Delete a Submission

1 Click on “My Submissions”.
2 Click on the model associated with the submitted run 
   (e.g., KIT_IWSLT25_Offline_en-de_constrained_primary).
3 Click on the “OFFLINE Hypotheses” button.
4 Use the three-dot menu on the right to:
    - Download the submitted run (hypothesis).
    - Delete the submitted run and confirm.

Replace a Submission

1 Delete your existing run.
2 Submit a new run file (repeat STEP 2 of “Submission steps”).

Contacts

Chairs: Matteo Negri (FBK, Italy), Marco Turchi (Zoom, Germany)

Discussion: iwslt-evaluation-campaign@googlegroups.com

Organizers

Sebastian Stüker (Zoom, Germany)
Jan Niehues (KIT, Germany)
Tsz Kin Lam (The University of Edinburgh, the United Kingdom)
Barry Haddow (The University of Edinburgh, the United Kingdom)\