Xutai Ma


2021

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FINDINGS OF THE IWSLT 2021 EVALUATION CAMPAIGN
Antonios Anastasopoulos | Ondřej Bojar | Jacob Bremerman | Roldano Cattoni | Maha Elbayad | Marcello Federico | Xutai Ma | Satoshi Nakamura | Matteo Negri | Jan Niehues | Juan Pino | Elizabeth Salesky | Sebastian Stüker | Katsuhito Sudoh | Marco Turchi | Alexander Waibel | Changhan Wang | Matthew Wiesner
Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)

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.

2020

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SIMULEVAL: An Evaluation Toolkit for Simultaneous Translation
Xutai Ma | Mohammad Javad Dousti | Changhan Wang | Jiatao Gu | Juan Pino
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Simultaneous translation on both text and speech focuses on a real-time and low-latency scenario where the model starts translating before reading the complete source input. Evaluating simultaneous translation models is more complex than offline models because the latency is another factor to consider in addition to translation quality. The research community, despite its growing focus on novel modeling approaches to simultaneous translation, currently lacks a universal evaluation procedure. Therefore, we present SimulEval, an easy-to-use and general evaluation toolkit for both simultaneous text and speech translation. A server-client scheme is introduced to create a simultaneous translation scenario, where the server sends source input and receives predictions for evaluation and the client executes customized policies. Given a policy, it automatically performs simultaneous decoding and collectively reports several popular latency metrics. We also adapt latency metrics from text simultaneous translation to the speech task. Additionally, SimulEval is equipped with a visualization interface to provide better understanding of the simultaneous decoding process of a system. SimulEval has already been extensively used for the IWSLT 2020 shared task on simultaneous speech translation. Code will be released upon publication.

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SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation
Xutai Ma | Juan Pino | Philipp Koehn
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

We investigate how to adapt simultaneous text translation methods such as wait-k and monotonic multihead attention to end-to-end simultaneous speech translation by introducing a pre-decision module. A detailed analysis is provided on the latency-quality trade-offs of combining fixed and flexible pre-decision with fixed and flexible policies. We also design a novel computation-aware latency metric, adapted from Average Lagging.

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Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq
Changhan Wang | Yun Tang | Xutai Ma | Anne Wu | Dmytro Okhonko | Juan Pino
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations

We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation. It follows fairseq’s careful design for scalability and extensibility. We provide end-to-end workflows from data pre-processing, model training to offline (online) inference. We implement state-of-the-art RNN-based as well as Transformer-based models and open-source detailed training recipes. Fairseq’s machine translation models and language models can be seamlessly integrated into S2T workflows for multi-task learning or transfer learning. Fairseq S2T is available at https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text.

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FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN
Ebrahim Ansari | Amittai Axelrod | Nguyen Bach | Ondřej Bojar | Roldano Cattoni | Fahim Dalvi | Nadir Durrani | Marcello Federico | Christian Federmann | Jiatao Gu | Fei Huang | Kevin Knight | Xutai Ma | Ajay Nagesh | Matteo Negri | Jan Niehues | Juan Pino | Elizabeth Salesky | Xing Shi | Sebastian Stüker | Marco Turchi | Alexander Waibel | Changhan Wang
Proceedings of the 17th International Conference on Spoken Language Translation

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.

2019

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Robust Document Representations for Cross-Lingual Information Retrieval in Low-Resource Settings
Mahsa Yarmohammadi | Xutai Ma | Sorami Hisamoto | Muhammad Rahman | Yiming Wang | Hainan Xu | Daniel Povey | Philipp Koehn | Kevin Duh
Proceedings of Machine Translation Summit XVII: Research Track

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AMR Parsing as Sequence-to-Graph Transduction
Sheng Zhang | Xutai Ma | Kevin Duh | Benjamin Van Durme
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3% on LDC2017T10) and AMR 1.0 (70.2% on LDC2014T12).

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Broad-Coverage Semantic Parsing as Transduction
Sheng Zhang | Xutai Ma | Kevin Duh | Benjamin Van Durme
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We unify different broad-coverage semantic parsing tasks into a transduction parsing paradigm, and propose an attention-based neural transducer that incrementally builds meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the neural transducer can be effectively trained without relying on a pre-trained aligner. Experiments separately conducted on three broad-coverage semantic parsing tasks – AMR, SDP and UCCA – demonstrate that our attention-based neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP.

2018

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Cross-lingual Decompositional Semantic Parsing
Sheng Zhang | Xutai Ma | Rachel Rudinger | Kevin Duh | Benjamin Van Durme
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We introduce the task of cross-lingual decompositional semantic parsing: mapping content provided in a source language into a decompositional semantic analysis based on a target language. We present: (1) a form of decompositional semantic analysis designed to allow systems to target varying levels of structural complexity (shallow to deep analysis), (2) an evaluation metric to measure the similarity between system output and reference semantic analysis, (3) an end-to-end model with a novel annotating mechanism that supports intra-sentential coreference, and (4) an evaluation dataset on which our model outperforms strong baselines by at least 1.75 F1 score.