pwc-archive/papers-with-abstracts huggingface dataset:
VUS at IWSLT 2021: A Finetuned Pipeline for Offline Speech Translation
Authors: Yong Rae Jo, Youngki Moon, Minji Jung, Jungyoon Choi, Jihyung Moon, Won Ik Cho
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Boundary Detection, Machine Translation, Sentence, Translation

In this technical report, we describe the fine-tuned ASR-MT pipeline used for the IWSLT shared task. We remove less useful speech samples by checking WER with an ASR model, and further train a wav2vec and Transformers-based ASR module based on the filtered data. In addition, we cleanse the errata that can interfere with the machine translation process and use it for Transformer-based MT module training. Finally, in the actual inference phase, we use a sentence boundary detection model trained with constrained data to properly merge fragment ASR outputs into full sentences. The merged sentences are post-processed using part of speech. The final result is yielded by the trained MT module. The performance using the dev set displays BLEU 20.37, and this model records the performance of BLEU 20.9 with the test set.
KIT’s IWSLT 2021 Offline Speech Translation System
Authors: Tuan Nam Nguyen, Thai Son Nguyen, Christian Huber, Ngoc-Quan Pham, Thanh-Le Ha, Felix Schneider, Sebastian Stüker
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Machine Translation, speech-recognition, Speech Recognition, Text Segmentation, Translation

This paper describes KIT’submission to the IWSLT 2021 Offline Speech Translation Task. We describe a system in both cascaded condition and end-to-end condition. In the cascaded condition, we investigated different end-to-end architectures for the speech recognition module. For the text segmentation module, we trained a small transformer-based model on high-quality monolingual data. For the translation module, our last year’s neural machine translation model was reused. In the end-to-end condition, we improved our Speech Relative Transformer architecture to reach or even surpass the result of the cascade system.
Maastricht University’s Multilingual Speech Translation System for IWSLT 2021
Authors: Danni Liu, Jan Niehues
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Data Augmentation, Translation

This paper describes Maastricht University’s participation in the IWSLT 2021 multilingual speech translation track. The task in this track is to build multilingual speech translation systems in supervised and zero-shot directions. Our primary system is an end-to-end model that performs both speech transcription and translation. We observe that the joint training for the two tasks is complementary especially when the speech translation data is scarce. On the source and target side, we use data augmentation and pseudo-labels respectively to improve the performance of our systems. We also introduce an ensembling technique that consistently improves the quality of transcriptions and translations. The experiments show that the end-to-end system is competitive with its cascaded counterpart especially in zero-shot conditions.
ZJU’s IWSLT 2021 Speech Translation System
Authors: Linlin Zhang
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, NMT, speech-recognition, Speech Recognition, Translation

In this paper, we describe Zhejiang University’s submission to the IWSLT2021 Multilingual Speech Translation Task. This task focuses on speech translation (ST) research across many non-English source languages. Participants can decide whether to work on constrained systems or unconstrained systems which can using external data. We create both cascaded and end-to-end speech translation constrained systems, using the provided data only. In the cascaded approach, we combine Conformer-based automatic speech recognition (ASR) with the Transformer-based neural machine translation (NMT). Our end-to-end direct speech translation systems use ASR pretrained encoder and multi-task decoders. The submitted systems are ensembled by different cascaded models.
Multilingual Speech Translation with Unified Transformer: Huawei Noah’s Ark Lab at IWSLT 2021
Authors: Xingshan Zeng, Liangyou Li, Qun Liu
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Data Augmentation, Decoder, Machine Translation, Multi-Task Learning, speech-recognition, Speech Recognition, Translation

This paper describes the system submitted to the IWSLT 2021 Multilingual Speech Translation (MultiST) task from Huawei Noah’s Ark Lab. We use a unified transformer architecture for our MultiST model, so that the data from different modalities (i.e., speech and text) and different tasks (i.e., Speech Recognition, Machine Translation, and Speech Translation) can be exploited to enhance the model’s ability. Specifically, speech and text inputs are firstly fed to different feature extractors to extract acoustic and textual features, respectively. Then, these features are processed by a shared encoder–decoder architecture. We apply several training techniques to improve the performance, including multi-task learning, task-level curriculum learning, data augmentation, etc. Our final system achieves significantly better results than bilingual baselines on supervised language pairs and yields reasonable results on zero-shot language pairs.
Multilingual Speech Translation KIT @ IWSLT2021
Authors: Ngoc-Quan Pham, Tuan Nam Nguyen, Thanh-Le Ha, Sebastian Stüker, Alexander Waibel, Dan He
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Translation

This paper contains the description for the submission of Karlsruhe Institute of Technology (KIT) for the multilingual TEDx translation task in the IWSLT 2021 evaluation campaign. Our main approach is to develop both cascade and end-to-end systems and eventually combine them together to achieve the best possible results for this extremely low-resource setting. The report also confirms certain consistent architectural improvement added to the Transformer architecture, for all tasks: translation, transcription and speech translation.
Edinburgh’s End-to-End Multilingual Speech Translation System for IWSLT 2021
Authors: Biao Zhang, Rico Sennrich
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Data Augmentation, feature selection, Machine Translation, Multi-Task Learning, Transfer Learning, Translation

This paper describes Edinburgh’s submissions to the IWSLT2021 multilingual speech translation (ST) task. We aim at improving multilingual translation and zero-shot performance in the constrained setting (without using any extra training data) through methods that encourage transfer learning and larger capacity modeling with advanced neural components. We build our end-to-end multilingual ST model based on Transformer, integrating techniques including adaptive speech feature selection, language-specific modeling, multi-task learning, deep and big Transformer, sparsified linear attention and root mean square layer normalization. We adopt data augmentation using machine translation models for ST which converts the zero-shot problem into a zero-resource one. Experimental results show that these methods deliver substantial improvements, surpassing the official baseline by > 15 average BLEU and outperforming our cascading system by > 2 average BLEU. Our final submission achieves competitive performance (runner up).
ON-TRAC’ systems for the IWSLT 2021 low-resource speech translation and multilingual speech translation shared tasks
Authors: Hang Le, Florentin Barbier, Ha Nguyen, Natalia Tomashenko, Salima Mdhaffar, Souhir Gabiche Gahbiche, Benjamin Lecouteux, Didier Schwab, Yannick Estève
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, NMT, speech-recognition, Speech Recognition, Translation

This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2021, low-resource speech translation and multilingual speech translation. The ON-TRAC Consortium is composed of researchers from three French academic laboratories and an industrial partner: LIA (Avignon Université), LIG (Université Grenoble Alpes), LIUM (Le Mans Université), and researchers from Airbus. A pipeline approach was explored for the low-resource speech translation task, using a hybrid HMM/TDNN automatic speech recognition system fed by wav2vec features, coupled to an NMT system. For the multilingual speech translation task, we investigated the us of a dual-decoder Transformer that jointly transcribes and translates an input speech. This model was trained in order to translate from multiple source languages to multiple target ones.
IMS’ Systems for the IWSLT 2021 Low-Resource Speech Translation Task
Authors: Pavel Denisov, Manuel Mager, Ngoc Thang Vu
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Machine Translation, speech-recognition, Speech Recognition, Transfer Learning, Translation

This paper describes the submission to the IWSLT 2021 Low-Resource Speech Translation Shared Task by IMS team. We utilize state-of-the-art models combined with several data augmentation, multi-task and transfer learning approaches for the automatic speech recognition (ASR) and machine translation (MT) steps of our cascaded system. Moreover, we also explore the feasibility of a full end-to-end speech translation (ST) model in the case of very constrained amount of ground truth labeled data. Our best system achieves the best performance among all submitted systems for Congolese Swahili to English and French with BLEU scores 7.7 and 13.7 respectively, and the second best result for Coastal Swahili to English with BLEU score 14.9.
The USYD-JD Speech Translation System for IWSLT2021
Authors: Liang Ding, DaCheng Tao
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Knowledge Distillation, NMT, Reranking, Translation

This paper describes the University of Sydney & JD’s joint submission of the IWSLT 2021 low resource speech translation task. We participated in the Swahili->English direction and got the best scareBLEU (25.3) score among all the participants. Our constrained system is based on a pipeline framework, i.e. ASR and NMT. We trained our models with the officially provided ASR and MT datasets. The ASR system is based on the open-sourced tool Kaldi and this work mainly explores how to make the most of the NMT models. To reduce the punctuation errors generated by the ASR model, we employ our previous work SlotRefine to train a punctuation correction model. To achieve better translation performance, we explored the most recent effective strategies, including back translation, knowledge distillation, multi-feature reranking, and transductive finetuning. For model structure, we tried auto-regressive and non-autoregressive models, respectively. In addition, we proposed two novel pre-train approaches, i.e. de-noising training and bidirectional training to fully exploit the data. Extensive experiments show that adding the above techniques consistently improves the BLEU scores, and the final submission system outperforms the baseline (Transformer ensemble model trained with the original parallel data) by approximately 10.8 BLEU score, achieving the SOTA performance.
mixSeq: A Simple Data Augmentation Methodfor Neural Machine Translation
Authors: Xueqing Wu, Yingce Xia, Jinhua Zhu, Lijun Wu, Shufang Xie, Yang Fan, Tao Qin
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Data Augmentation, Diversity, Machine Translation, Translation

Data augmentation, which refers to manipulating the inputs (e.g., adding random noise,masking specific parts) to enlarge the dataset,has been widely adopted in machine learning. Most data augmentation techniques operate on a single input, which limits the diversity of the training corpus. In this paper, we propose a simple yet effective data augmentation technique for neural machine translation, mixSeq, which operates on multiple inputs and their corresponding targets. Specifically, we randomly select two input sequences,concatenate them together as a longer input aswell as their corresponding target sequencesas an enlarged target, and train models on theaugmented dataset. Experiments on nine machine translation tasks demonstrate that such asimple method boosts the baselines by a non-trivial margin. Our method can be further combined with single input based data augmentation methods to obtain further improvements.
On Knowledge Distillation for Translating Erroneous Speech Transcriptions
Authors: Ryo Fukuda, Katsuhito Sudoh, Satoshi Nakamura
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Knowledge Distillation, Machine Translation, speech-recognition, Speech Recognition, Translation

Recent studies argue that knowledge distillation is promising for speech translation (ST) using end-to-end models. In this work, we investigate the effect of knowledge distillation with a cascade ST using automatic speech recognition (ASR) and machine translation (MT) models. We distill knowledge from a teacher model based on human transcripts to a student model based on erroneous transcriptions. Our experimental results demonstrated that knowledge distillation is beneficial for a cascade ST. Further investigation that combined knowledge distillation and fine-tuning revealed that the combination consistently improved two language pairs: English-Italian and Spanish-English.
Large-Scale English-Japanese Simultaneous Interpretation Corpus: Construction and Analyses with Sentence-Aligned Data
Authors: Kosuke Doi, Katsuhito Sudoh, Satoshi Nakamura
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Sentence

This paper describes the construction of a new large-scale English-Japanese Simultaneous Interpretation (SI) corpus and presents the results of its analysis. A portion of the corpus contains SI data from three interpreters with different amounts of experience. Some of the SI data were manually aligned with the source speeches at the sentence level. Their latency, quality, and word order aspects were compared among the SI data themselves as well as against offline translations. The results showed that (1) interpreters with more experience controlled the latency and quality better, and (2) large latency hurt the SI quality.
Inverted Projection for Robust Speech Translation
Authors: Dirk Padfield, Colin Cherry
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Translation

Traditional translation systems trained on written documents perform well for text-based translation but not as well for speech-based applications. We aim to adapt translation models to speech by introducing actual lexical errors from ASR and segmentation errors from automatic punctuation into our translation training data. We introduce an inverted projection approach that projects automatically detected system segments onto human transcripts and then re-segments the gold translations to align with the projected human transcripts. We demonstrate that this overcomes the train-test mismatch present in other training approaches. The new projection approach achieves gains of over 1 BLEU point over a baseline that is exposed to the human transcripts and segmentations, and these gains hold for both IWSLT data and YouTube data.
Tag Assisted Neural Machine Translation of Film Subtitles
Authors: Aren Siekmeier, WonKee Lee, HongSeok Kwon, Jong-Hyeok Lee
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Decoder, Machine Translation, Sentence, TAG, Translation

We implemented a neural machine translation system that uses automatic sequence tagging to improve the quality of translation. Instead of operating on unannotated sentence pairs, our system uses pre-trained tagging systems to add linguistic features to source and target sentences. Our proposed neural architecture learns a combined embedding of tokens and tags in the encoder, and simultaneous token and tag prediction in the decoder. Compared to a baseline with unannotated training, this architecture increased the BLEU score of German to English film subtitle translation outputs by 1.61 points using named entity tags; however, the BLEU score decreased by 0.38 points using part-of-speech tags. This demonstrates that certain token-level tag outputs from off-the-shelf tagging systems can improve the output of neural translation systems using our combined embedding and simultaneous decoding extensions.
A Statistical Extension of Byte-Pair Encoding
Authors: David Vilar, Marcello Federico
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Data Compression, Machine Translation, Translation

Sub-word segmentation is currently a standard tool for training neural machine translation (MT) systems and other NLP tasks. The goal is to split words (both in the source and target languages) into smaller units which then constitute the input and output vocabularies of the MT system. The aim of reducing the size of the input and output vocabularies is to increase the generalization capabilities of the translation model, enabling the system to translate and generate infrequent and new (unseen) words at inference time by combining previously seen sub-word units. Ideally, we would expect the created units to have some linguistic meaning, so that words are created in a compositional way. However, the most popular word-splitting method, Byte-Pair Encoding (BPE), which originates from the data compression literature, does not include explicit criteria to favor linguistic splittings nor to find the optimal sub-word granularity for the given training data. In this paper, we propose a statistically motivated extension of the BPE algorithm and an effective convergence criterion that avoids the costly experimentation cycle needed to select the best sub-word vocabulary size. Experimental results with morphologically rich languages show that our model achieves nearly-optimal BLEU scores and produces morphologically better word segmentations, which allows to outperform BPE’s generalization in the translation of sentences containing new words, as shown via human evaluation.
Abusive Language Recognition in Russian
Authors: Kamil Saitov, Leon Derczynski
Date: 2021-04-01 00:00:00
Proceeding: EACL (BSNLP) 2021 4
Tasks: Abusive Language, Diversity

Abusive phenomena are commonplace in language on the web. The scope of recognizing abusive language is broad, covering many behaviors and forms of expression. This work addresses automatic detection of abusive language in Russian. The lexical, grammatical and morphological diversity of Russian language present potential difficulties for this task, which is addressed using a variety of machine learning approaches. Finally, competitive performance is reached over multiple domains for this investigation into automatic detection of abusive language in Russian.
Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company’s Reputation
Authors: Nikolay Babakov, Varvara Logacheva, Olga Kozlova, Nikita Semenov, Alexander Panchenko
Date: 2021-04-01 00:00:00
Proceeding: EACL (BSNLP) 2021 4

Not all topics are equally “flammable” in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.
BERTić - The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian
Authors: Nikola Ljubešić, Davor Lauc
Date: 2021-04-01 00:00:00
Proceeding: EACL (BSNLP) 2021 4
Tasks: Commonsense Causal Reasoning, Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, Translation

In this paper we describe a transformer model pre-trained on 8 billion tokens of crawled text from the Croatian, Bosnian, Serbian and Montenegrin web domains. We evaluate the transformer model on the tasks of part-of-speech tagging, named-entity-recognition, geo-location prediction and commonsense causal reasoning, showing improvements on all tasks over state-of-the-art models. For commonsense reasoning evaluation we introduce COPA-HR - a translation of the Choice of Plausible Alternatives (COPA) dataset into Croatian. The BERTić model is made available for free usage and further task-specific fine-tuning through HuggingFace.
Exploratory Analysis of News Sentiment Using Subgroup Discovery
Authors: Anita Valmarska, Luis Adrián Cabrera-Diego, Elvys Linhares Pontes, Senja Pollak
Date: 2021-04-01 00:00:00
Proceeding: EACL (BSNLP) 2021 4
Tasks: Descriptive, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Subgroup Discovery

In this study, we present an exploratory analysis of a Slovenian news corpus, in which we investigate the association between named entities and sentiment in the news. We propose a methodology that combines Named Entity Recognition and Subgroup Discovery - a descriptive rule learning technique for identifying groups of examples that share the same class label (sentiment) and pattern (features - Named Entities). The approach is used to induce the positive and negative sentiment class rules that reveal interesting patterns related to different Slovenian and international politicians, organizations, and locations.
Creating an Aligned Russian Text Simplification Dataset from Language Learner Data
Authors: Anna Dmitrieva, Jörg Tiedemann
Date: 2021-04-01 00:00:00
Proceeding: EACL (BSNLP) 2021 4
Tasks: Text Simplification

Parallel language corpora where regular texts are aligned with their simplified versions can be used in both natural language processing and theoretical linguistic studies. They are essential for the task of automatic text simplification, but can also provide valuable insights into the characteristics that make texts more accessible and reveal strategies that human experts use to simplify texts. Today, there exist a few parallel datasets for English and Simple English, but many other languages lack such data. In this paper we describe our work on creating an aligned Russian-Simple Russian dataset composed of Russian literature texts adapted for learners of Russian as a foreign language. This will be the first parallel dataset in this domain, and one of the first Simple Russian datasets in general.