This paper reports on the shared tasks organized by the 20th IWSLT Conference. The shared tasks address 9 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, multilingual, dialect and low-resource speech translation, and formality control. The shared tasks attracted a total of 38 submissions by 31 teams. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.

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. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved.

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. A total of 22 teams participated in at least one of the tasks. This paper describes each shared task, data and evaluation metrics, and reports results of the received submissions.

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. A total of teams participated in at least one of the tracks. This paper introduces each track’s goal, data and evaluation metrics, and reports the results of the received submissions.

Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines. This difficulty is also due to the fact that the training data on which models are built typically reflect the asymmetries of natural languages, gender bias included. Exclusively fed with textual data, machine translation is intrinsically constrained by the fact that the input sentence does not always contain clues about the gender identity of the referred human entities. But what happens with speech translation, where the input is an audio signal? Can audio provide additional information to reduce gender bias? We present the first thorough investigation of gender bias in speech translation, contributing with: i) the release of a benchmark useful for future studies, and ii) the comparison of different technologies (cascade and end-to-end) on two language directions (English-Italian/French).

Current research on spoken language translation (SLT) has to confront with the scarcity of sizeable and publicly available training corpora. This problem hinders the adoption of neural end-to-end approaches, which represent the state of the art in the two parent tasks of SLT: automatic speech recognition and machine translation. To fill this gap, we created MuST-C, a multilingual speech translation corpus whose size and quality will facilitate the training of end-to-end systems for SLT from English into 8 languages. For each target language, MuST-C comprises at least 385 hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations. Together with a description of the corpus creation methodology (scalable to add new data and cover new languages), we provide an empirical verification of its quality and SLT results computed with a state-of-the-art approach on each language direction.

This paper describes FBK’s submission to the end-to-end English-German speech translation task at IWSLT 2018. Our system relies on a state-of-the-art model based on LSTMs and CNNs, where the CNNs are used to reduce the temporal dimension of the audio input, which is in general much higher than machine translation input. Our model was trained only on the audio-to-text parallel data released for the task, and fine-tuned on cleaned subsets of the original training corpus. The addition of weight normalization and label smoothing improved the baseline system by 1.0 BLEU point on our validation set. The final submission also featured checkpoint averaging within a training run and ensemble decoding of models trained during multiple runs. On test data, our best single model obtained a BLEU score of 9.7, while the ensemble obtained a BLEU score of 10.24.

This paper describes the KnowledgeStore, a large-scale infrastructure for the combined storage and interlinking of multimedia resources and ontological knowledge. Information in the KnowledgeStore is organized around entities, such as persons, organizations and locations. The system allows (i) to import background knowledge about entities, in form of annotated RDF triples; (ii) to associate resources to entities by automatically recognizing, coreferring and linking mentions of named entities; and (iii) to derive new entities based on knowledge extracted from mentions. The KnowledgeStore builds on state of art technologies for language processing, including document tagging, named entity extraction and cross-document coreference. Its design provides for a tight integration of linguistic and semantic features, and eases the further processing of information by explicitly representing the contexts where knowledge and mentions are valid or relevant. We describe the system and report about the creation of a large-scale KnowledgeStore instance for storing and integrating multimedia contents and background knowledge relevant to the Italian Trentino region.

This paper reports on the participation of FBK at the IWSLT 2008 Evaluation. Main effort has been spent on the Chinese-Spanish Pivot task. We implemented four methods to perform pivot translation. The results on the IWSLT 2008 test data show that our original method for generating training data through random sampling outperforms the best methods based on coupling translation systems. FBK also participated in the Chinese-English Challenge task and the Chinese-English and Chinese-Spanish BTEC tasks, employing the standard state-of-the-art MT system Moses Toolkit.

Translation with pivot languages has recently gained attention as a means to circumvent the data bottleneck of statistical machine translation (SMT). This paper tries to give a mathematically sound formulation of the various approaches presented in the literature and introduces new methods for training alignment models through pivot languages. We present experimental results on Chinese-Spanish translation via English, on a popular traveling domain task. In contrast to previous literature, we report experimental results by using parallel corpora that are either disjoint or overlapped on the pivot language side. Finally, our original method for generating training data through random sampling shows to perform as well as the best methods based on the coupling of translation systems.

This paper reports on the participation of FBK (formerly ITC-irst) at the IWSLT 2007 Evaluation. FBK participated in three tasks, namely Chinese-to-English, Japanese-to-English, and Italian-to-English. With respect to last year, translation systems were developed with the Moses Toolkit and the IRSTLM library, both available as open source software. Moreover, several novel ideas were investigated: the use of confusion networks in input to manage ambiguity in punctuation, the estimation of an additional language model by means of the Google’s Web 1T 5-gram collection, the combination of true case and lower case language models, and finally the use of multiple phrase-tables. By working on top of a state-of-the art baseline, experiments showed that the above methods accounted for significant BLEU score improvements.